Acoustic Emission - Beyond the Millennium

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Author: T. Kishi | M. Ohtsu | S. Yuyama

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Acoustic Emission

Beyond the Millennium

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9

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Acoustic Emission

Beyond the Millennium Edited by T. Kishi

National Institute for Advanced Interdisciplinary Research, Tsukuba, Ibaraki, Japan

M. Ohtsu

Graduate School of Science and Technology, Kumamoto University, Kumamoto, Japan

S. Yuyama

Nippon Physical Acoustic Ltd., Tokyo, Japan

11 - 14 September 2000 Tokyo, Japan

2OOO

ELSEVIER

A m s t e r d a m - Lausanne - New York- O x f o r d - Shannon - Singapore- Tokyo

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First edition 2000

L i b r a r y o f C o n g r e s s C a t a l o g i n g in P u b l i c a t i o n D a t a A c a t a l o g r e c o r d f r o m the L i b r a r y o f C o n g r e s s h a s b e e n a p p l i e d for. B r i t i s h L i b r a r y C a t a l o g u i n g in P u b l i c a t i o n D a t a A c a t a l o g u e r e c o r d f r o m the B r i t i s h L i b r a r y h a s b e e n a p p l i e d for.

I S B N : 0 08 0 4 3 8 5 1 2 Q T h e p a p e r u s e d in this p u b l i c a t i o n m e e t s the r e q u i r e m e n t s o f A N S U N I S O P r i n t e d in T h e N e t h e r l a n d s .

Z39.48-1992 (Permanence of Paper).

PREFACE Research on Acoustic Emission (AE) started in the middle of the 20th century in the areas of seismology, mining, physics and metallurgy. Up to the present, a variety of research on AE has been performed in laboratory. Field applications have been also carried out in various types of existing structures. The first International Acoustic Emission Symposium (IAES1) was held in 1972 in Tokyo, sponsored by the High Pressure Institute of Japan. Since then, a series of IAES has been held biennially. The symposia have been organized by the Japanese Society for Nondestructive Inspection (JSNDI) since 1978 and the 14th symposium (IAES14) was held in Hawaii in 1998, co-organized by JSNDI and AEWG (Acoustic Emission Working Group) of USA. Earlier in 1999, it was decided that the 15th International Acoustic Emission Symposium (IAES15) would be held on September 11-14, 2000 at the International House of Japan in Tokyo. The theme of the symposium was set as "Step out of frontier and go practical for life-extension and maintenance of plants and structures". Special emphasis was placed on review of AE research and applications in the 20th century and future aspects for the 21st century. In relation to the preparation of IAES15, it was realized that Prof. Teruo Kishi, who has made invaluable contributions to the advances of AE science and technology, was going to retire from the University of Tokyo in March 2000. In honor of his achievement, "Kishi Workshop 2000" was planned in parallel with the IAES15. The workshop was programmed on the third day of the IAES15 schedule. In this workshop, a limited number of invited papers are presented for technical discussions to review the achievement on the AE research and applications in the 20th century. The proceedings of the workshop is named "Acoustic Emission - Beyond the Millennium" to celebrate the coming new millennium, stepping forward to the new era. The authors and topics of the review papers are selected by the editorial board. It would be a great pleasure for all the board members to know that the workshop is really successful and could provide all the participants with a good opportunity to review the AE research and applications performed in the 20th century. The editorial board would like to express the greatest appreciation to the authors of the papers in this volume for their enthusiastic efforts. Thanks are also due to Elsevier for the continuous support to publish this separate proceedings of the IAES15.

Masayasu Ohtsu

and

Shigenori Yuyama

A c o u s t i c E m i s s i o n - N e x t Generation

The technique to monitor defects and abnormal vibrations due to machine failures is vitally important for the safety of structures in the modem society.

Acoustic emission (AE), as a

passive method other than active NDT methods, has drawn great attention because of its applicability to on-stream surveillance of structures. One important point is the capability to acquire data very simply but with high sensitivity, so that the development of non-contact sensing technique is particularly important. A quantitative method to evaluate structural integrity and remaining life from the detected AE signals is strongly requested. The quantitative analysis based on the inverse procedure, which has provided certain solutions, has not been utilized widely enough in structures due to its complexity. The applicability is limited partly because the accuracy of solutions depends on the noise levels and partly because the phenomenon is usually irreproducible. AE is expected to be a technique in the next generation not only to monitor conditions but also to repair damaged structures, combined with an active-adaptive technique using a device called "solid state actuator". "Smart Materials and Structure" are known in this respect. AE is considered to be a very promising technique, together with such sensing techniques as optical fiber, shape memory alloy and electro-rheological fluid. Thus, AE can play a very important roll to monitor, evaluate and repair structures. In the last two decades, great progress has been made in the areas of sensor calibration, quantitative waveform analysis based on the application of the Green's function to the forward and inverse problems and noise filtering. I believe that the continuing improvement with the new concepts such as smart structures will make it possible to improve further the reliability and applicability of this promising technique in practical use.

Teruo Kishi

vii

CONTENTS

Preface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ........... v Acoustic Emission Source Characterization in Materials Manabu Enoki and Teruo Kishi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 Moment Tensor Analysis of AE and SIGMA Code Masayasu Ohtsu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 Wavelet Transform - Applications to AE Signal Analysis Mikio Takemoto, Hideo Nishino and Kanji Ono . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 New Goals for Acoustic Emission in Materials Research Kanji Ono . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57 Thirty Years of Advances and Some Remaining Challenges in the Application of Acoustic Emission to Composite Materials Marvin A. Hamstad . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77 Acoustic Emission for the Detection of Fatigue Damage Oh-Yang Kwon and K. Lee . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93 Acoustic Emission/Microseismic Technique: Review of Research in the 20th Century and Future Aspects Hiroaki Niitsuma . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109 Acoustic Emission in Rock Mechanics Studies Gerd Manthei, Jtirgen Eisenbl~itter and Thomas Spies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127 Post-Failure Micromechanisms in Shear Banding of Rock Joseph F. Labuz and Fernanda C. S. Carvalho . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 145 Advanced Acoustic Emission for On-Stream Inspection Mark F. Carlos, Sotirios J. Vahaviolos and W. David Wang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 159 Listen to your Storage Tanks to Improve Safety and Reduce Cost Phillip T. Cole and Peter J. Van De Loo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 169 Acoustic Emission in Composite Materials and Structures Pierre Fleischmann and Jean Claude Lenain . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 179 Acoustic Emission Evaluation in Concrete Shigenori Yuyama and Masayasu Ohtsu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 187

viii

Diagnosis of Machinery Using Acoustic Emission Techniques Takeo Yoshioka . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 215 Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 231 Subject Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 233

ACOUSTIC EMISSION SOURCE CHARACTERIZATION IN MATERIALS MANABU ENOKI and TERUO KISHI* Department of Materials Science, The University of Tokyo Bunkyo-ku, Tokyo 113-8656, Japan *National Institute for Advanced Interdisciplinary Research Tsukuba, Ibaraki 305-8562, Japan

ABSTRACT Acoustic emission (AE) source characterization has been developed to understand the dynamic process of microfracture in metals, ceramics and composites. The development of AE source characterization is summarized and some examples of analysis are described. Firstly, AE waveforms from glass matrix composites during fracture toughness test were recorded by using the advanced AE measuring system with multi-channels. The source model of microcracking and the deconvolution method also could evaluate fracture mode and microfracture size. Secondly, the algorithm of a three dimensional AE source location for anisotropic materials was developed to investigate the crack propagation of glass fiber reinforced plastic thick plate which has variable wave velocities along different directions. The results of source location clearly demonstrated the forming of fracture process zone in the material, that is, the three dimensional shape and size of process zone could be estimated. Finally, the laser interferometer was used to measure AE during the thermal cycle and to evaluate fracture behavior in plasma-sprayed ceramics coatings onto steel substrate. The results showed that it is possible to use laser facility as acoustic emission measurement method. Moreover, by analyzing detected waves due to the AE source analysis, the fracture behavior of coatings during thermal cycle tests was quantitatively evaluated. KEYWORDS Acoustic emission, Source characterization, Location, Deconvolution, Moment tensor, Microcrack, Laser interferometer INTRODUCTION In the fields of micromechanics and seismology, the deformations such as microcracks have been formulated analytically. Those deformations in materials can be generally represented as non-elastic 'eigenstrain' in micromechanics [1]. The relationship between microcracking and eigenstrain (or deformation moment tensor) was established, and the method to obtain the

moment tensor has been developed. An acoustic emission (AE) technique has been used as an almost unique method to detect dynamic deformation and fracture of materials with high sensitivity. Some studies have attempted to characterize AE sources quantitatively on the analogy of seismology [2]. In order to determine the deformation moment tensor and characterize the AE sources, the multiple deconvolution must be carried out in multiple convolution equation by using the recorded AE waveform with more than six channels, as mentioned below. However, if the mode of microcracking is the tensile type, this equation can be reduced to a simple linear convolution equation, and then only the size of microcracking is the unknown parameter and measuring with one channel can determine the size and generation velocity of microcracking. Wadley et al. [3] determined the volume and generation velocity of the cleavage and intergranular microcracks in a mild steel and electrolytic iron by using a capacitance transducer, which can measure the displacement of the surface, and the Yobell specimen, to which the theoretical Green's function can be applied, and then by carrying out the single deconvolution method in the time domain. Kishi et al. [4], on the other hand, also independently applied the simple deconvolution method and characterized intergranular microcracks in Ni-Cr-Mo steel by using the response function, which includes both the transfer function of the measuring system and the Green's function of specimen and can be experimentally calibrated by a breaking pencil lead. Their method could characterize the AE sources by general transducers and specimens. The deformation moment tensor has to be determined to obtain the mode and orientation of microcracking. However, various simplified methods have been proposed in this problem to avoid solving the convolution equation directly. Ohira et al. [5] determined the moment tensor in A533B steel from the ratio of amplitudes between longitudinal and transverse waves, by comparing with the ratio which is calculated from the Green's function of an infinite plate. They obtained the time function of the moment tensor by using the single deconvolution method under the assumption that the components of the moment tensor have the same time function. Scruby et al. [6] carried out the analysis by using the strength of the first arrival pulse which is defined as multiplication of the first peak amplitude and time. As the Green's function of media, they used the far-field term of the longitudinal wave of the theoretical Green's function in an infinite medium under the consideration of the reflection at the surface, that is, they used the far-field term of the Green' function in a semifinite medium. They determined the moment tensor in 7010 A1 alloy by comparing the pulse strength and this far-field term. Ohtsu [7] used only the first peak amplitude and simplified the Scruby's method in the result. He determined the ratio of the moment tensor, however the absolute value of the moment tensor and time function is not determined by his method. Kim et al. [8] determined the strength and time function of dipoles due to a thermal crack in glass by comparing the epicentral normal response and the Green's function in an infinite plate and by using the single deconvolution method. They determined the radiation pattern of the moment tensor from the peak amplitude of the first longitudinal wave under the assumption that longitudinal wave amplitude is proportional to the surface displacement. The authors [9] determined the moment tensor in A470 steel by solving the convolution equation directly. The source location of each acoustic emission was determined from the signals recorded by six multi-transducers. Each dynamic Green function of the compact tension specimen concerning each source location was calculated by a finite difference method [ 10]. The transfer function of the measuring system was calibrated by a pencil breaking lead [11, 12], and then both deconvolution algorithms in time domain and in frequency domain were developed and all the moment tensor components were determined [ 13-15].

Waveform based AE measuring system became popular instead of conventional AE parameters analysis equipment because of the developing of personal computer based measuring system. It became easy to measure a plenty of AE waveforms and analyze these data, and the calculation time for deconvolution and Green's function could be significantly reduced [ 16]. Waveform analysis was applied to evaluate cracking parameters in fiber reinforced composites such as precise location and fracture mode [17, 18]. On the other hand, the low noise type transducer was developed to measure small AE signal comes from microcrack in ceramics [ 19]. Another methods to characterize AE sourced were applied such as neural network and wavelet transform [20, 21]. AE analysis was applied to many composite materials due to the developing these materials such as SiC particle reinforced glass composites [22]. The measurement in harsh environments is required because materials and structures are used these atmospheres. Laser interferometer was developed to measure AE at elevated temperature [23]. In this article the development of AE source characterization is reviewed. The theory of AE, the experimental and analysis system are mentioned, and some resent examples of analysis are summarized.

M O M E N T T E N S O R ANALYSIS Many ceramic and glass matrix composites have been investigated for the high temperature use. In particle reinforced ceramics remarkable increase of toughness has been reported [23-26]. The SiC particle reinforced glass composite was used as a model material where connection between matrix and fiber provides stress transfer. In this paper we try to apply the Acoustic Emission waveform inverse analysis method [13-15] to this material. Fracture process of this material is investigated in the terms of the location of microfracture by the arrival time differences, and the identification of fracture mode and the sizing of microfracture by the deconvolution method.

Theory of AE Source It is well known that the faulting source in an elastic medium can be modeled. Let S denote a fault surface contains two adjacent opposite internal surface, labeled S§ and S. Using the reciprocal theorem, the displacement field at position x' and time t, u(x', t), for point source can be represented as Ui(X', t) = Gij(x', x, t) * Tj(x, t) + Gij,k(X', x, t) * Djk(X; t), Tj(x, t)= ~s [tj(x, t)] dS,

Djk(X, t) = IS Cpqjk [Up(X, t)] Vq dS,

(1) (2) (3)

where * means a convolution integral with respect to time, ~s dS indicates a surface integral and Gij(x', x, t) is the displacement field in the direction xi at position x' at time t due to an impulsive force in the direction xj at position x at time 0, which is called as a Green's function. The displacement discontinuity is denoted by [u(x, t)] for x on S, and the traction discontinuity is denoted by [fix, t)]. The normal to S is v and Cpqjk is an elastic constant. Suppose the microcracking on surface S. From equation (3), we can represent Djk(X, t) for an isotropic medium as Djk =

JS {~, [Up] Vp 5jk + g([Uj] Vk +[Uk] Vj)} dS,

(4)

where ~, and ltt are Lam6's constants, and 5jk is Kronecker's delta. In the case of microcracking, [t(x, t)] = 0 on S. Finally the displacement field due to debonding can be represented as, from equations, Ui(X', t)

- Gij,k(X', x ,

(5)

t) * Djk(X, t).

Consequently, AE source of microcracking is equivalent to a dipole force. the concept of AE source characterization.

Figure 1 shows

Accurate source location is required in order to understand fracture processes. The location of each source event is determined by measuring the differences in the wave arrival time between two transducers [27]. Suppose that Atij is the difference in the wave arrival time between i-th and j-th transducers. Let ri denote the transducer positions (1 < i < P) and r denote the location of the source, where P is the total number of channels. We can represent the general equation for source location as c~ Atij = Ir- r i l - I r - rjl,

(6)

where a is the longitudinal velocity of material. A nonlinear least-square method can be used to solve the equation (6) for the three-dimensional source location r if P < 4, and the two dimensional source location if P < 3.

Experimental The PbO-SiO2-B203-A1203 glass was chosen as matrix glass and SiC was chosen as dispersed ceramics particle. Because thermal expansion constants of PbO-SiO2-B203-A1203 glass and SiC are almost the same and the difference of elastic modulus is large. The average size of SiC particle is about 8gm and 50gm, and the volume fraction of SiC particle was from 5% to 30%. The glass powder and SiC particle were mixed by ball milling in methanol and were dried in air. Hot pressing under the following conditions sintered the powder. The hot pressing temperature was 630~ that was 300C higher than softening point and the pressure was

Figure 1 Schematic of acoustic emission source characterization.

25.5MPa, the sintering time was 30 minutes in argon gas atmosphere. The sintered samples were performed X-ray diffraction (XRD) analysis and density was measured by Archimedes method and elastic modulus by ultrasonic method. Fracture toughness test was carried out by an Instron type testing machine at constant cross head speed of 0.5 mm/min, at room temperature in air. The four point bending tests were carded out in both air and vacuum by using the specimens of 3 by 4 by 40 mm. AE sensors were attached at the both ends of specimen and AE waveforms of two channels were recorded. (20, 6.35, 30.5 ) (20, O, 23 )

31.7mm 12.7mm

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Time, t / s Figure 3 AE behavior of 5vo1% SiC reinforced glass matrix composites during fracture toughness test with AE measurement.

One-dimensional locations of AE signals were analyzed. Also fracture toughness test using compact tension specimens were carried. Figure 2 shows the dimension of specimen and the position of AE sensors. AE signals during fracture toughness test were recorded with data of load and crack opening displacement (COD) by measuring system mentioned below. AE measuring system with multi-channels has been used in experiments. AE waveforms were recorded by the wave memory with sampling rate of 50 ns and 2 kwords each channel. Also conventional AE parameters such as event and amplitude with the load to specimen were analyzed. Microcomputer was used to record the AE parameters and waveforms via interface. Results

Figure 3 shows the AE behavior of 5vo1% SiC reinforced composites during fracture toughness test. Effect of volume fraction of SiC on fracture toughness and effect of grain size of SiC on fracture toughness were measured. Figures 4 and 5 show the location results by AE. The errors of source location are given by a sampling rate, positions of transducers and dimensions of transducers. A sampling rate of 20 MHz and a longitudinal velocity give the maximum error of about 0.6 mm in source location. Although the size of the piezoelectric element is about 1 mm, the error of positions in attachment of transducers is smaller than this. Then the experimental error on each coordinate is estimated to be approximately 1 mm. 30-

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i

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Location x / m m Figure 4 Location results during fracture toughness test of 5vo1% SiC glass composites.

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Location x / mm

Figure 5 Location results during fracture toughness test of 30vo1% SiC glass composites.

The deformation moment tensor has to be determined to obtain the mode and orientation of microcracking. We have presented the multiple deconvolution method to determine the moment tensor. Moment tensor Djk is determined by the frequency deconvolution method using some time points from longitudinal wave arrival. Applying the nonlinear least-square method, the displacement discontinuity [u] and the normal v are obtained from the determined moment tensor Djk. The inclination of the microcrack plane to the main crack surface and the inclination of the microcrack plane to the direction of the displacement discontinuity were calculated from the components of moment tensor. This result has demonstrated that a microcracking occurs in mixed mode of tensile and shear, but the shear component is stronger. Figures 6 shows the distribution of crack radius. Figures 7 shows the distribution of the mode angle. A cracking of particle and a debonding at interface were observed from the fracture surface. The estimated value of radius agrees well with the size of particle that is observed at the location of source event in front of the precrack tip. It can be concluded that the recorded AE events due to microcracking are identified as a cracking of particle and a debonding at interface in front of the precrack.

>-~ t.)

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Radius, r/~tm Figure 6 Distribution of microcrack size of 30vo1% SiC glass composites.

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80

90

Angle, 0 / degree Figure 7 Distribution of fracture angle for microcrack of 30vo1% SiC glass composites. LOCATION IN ANISOTROPIC MATERIALS In order to ensure the reliability of engineering materials, various techniques have been used, for example, measurement of Weibull modulus, prediction of life time, development of processing, characterization of microstructure, fractography and NDE (nondestructive evaluation), etc. Among these methods, AE technique is one of the promising NDE methods that can monitor the behavior of propagating cracks simultaneously.

In AE signal analysis, one of the most important techniques is calculating of the location of AE sources. Especially, three-dimensional AE source location is very useful in evaluation of crack growth. However, in anisotropic materials, for example, single crystals, long fiber reinforced composites, laminated composites etc., it is very difficult to determine the wave velocity in all directions. In the case of unidirectional fiber composites, equation (7) can be used approximately [28, 29], v

=

(Vx212+ vy2m2 + vz2n2) 1/2

(7)

where v is the wave velocity, 1, m, n are the direction cosines in the arbitrary direction and Vx, Vy, Vz are the velocities of the axis directions. However, this equation is no more useful in the woven fabric composites because it cannot represent the 45 degree symmetrical characteristic of the woven fabric composites. In the case of these composites such as plain weave fabric GFRP or carbon-carbon composite with satin structure, two dimensional source location has been reported by means of higher order plate theory described by Tang, et.al. [31, 32] or by using empirical equation. The algorithm of three-dimensional source location for woven fabric thick plate was developed. The wave velocities in the respective wave propagating directions were calculated from the stiffness matrix. This algorithm was applied to the plain weave fabric GFRP thick plate, where Christoffel equation for tetragonal single crystal was used for the wave velocities because of its similar structural symmetry.

Wave Velocity in Orthotropic Medium Christoffel has given a detailed account of the propagation of acoustic waves in a crystalline medium. The velocity of propagation of acoustic waves in an infinite crystalline medium is related to its elastic constants through the determinantal equation. The tetragonal single crystal has six elastic constants Cll, c33, c44, c66, C12 and C13. The coefficients of the Christoffel's equations for this case are given by ~Lll = ~al = ~31 = ~,22 = ~23 = ~,33 =

12Cli + m2c66 + n2c44 lm(Cl2 -1-C66) ln(c13+c44) 12c66q- m 2c 11 + n 2c44 mn(cl3 + C44) 12C44 + m2c66 + n2c33

(8)

where )~ij are the linear functions of the elastic constants determined by the direction cosines (1,m,n) of the direction of the propagation, and s is the density of the crystal. If six elastic constants (Cll, C33, C44, C66, C12, Cl3) were given, wave velocity in arbitrary direction can be calculated by using equation (8). Thus the most important thing for calculating a wave velocity is recovering the elastic constants of the materials from velocities in several directions. For the materials of which both longitudinal and transverse velocities in four directions ((100), (001), (110) and (111)) can be measured, the six elastic constants can be simply determined by C = SV2.

(9)

10 15

7 5

3500

0.4

2500

2000 ~

. . . . .

9 9

O. 45

....-

.

.

" ,.,"

."."

-.::

-'-: ~.

1500 1000 500

0

0

. . . . 500

O. 35

3000

9

o

4000

1000

1500

0 2000

0 0 <

0.3 O. 25

t:~

0.2

~< 0 ~.~

0.15 0.1 O. 05 0

C O D (gm) Figure 8 Load-COD curve and AE behavior during fracture test of GFRP.

In fiber reinforced composites, transverse wave velocity sometimes can not be successfully measured because the attenuation of transverse wave is too large. Therefore the six elastic constants need to be recovered only by longitudinal wave velocities. Every et. al. [32] explained the method to recover elastic constants in anisotropic material with an example of the case of triclinic material. They simply formulated the term of sv2 as a function of both the direction and the elastic constants by the perturbation technique like as sv2= f(direction, elastic constants),

(10)

and then recovered the elastic constants from several empirical values of direction and longitudinal velocity by Newton-Raphson numerical analysis method. In order to minimize the error that may be brought out due to their simplification, their formulation is not used but directly expanded the determinantal equation in itself to formulate the following equation. f(SV2, direction, elastic constants)= 0

(11)

Experimental The location evaluation method mentioned above was applied to the CT specimen (62.5 x 60 x 26, mm) of GFRP with plain weave fabric structure. The length of notch was 35 mm. The density is 1.97 Mg/m 3. The specimen was loaded with 1 mm/min cross head speed at room temperature in air. During tensile loading, AE signals were detected by using of AE analyzer ( DCM140, JTT, Japan) with 8 AE transducers (M304A, Fuji Ceramics Co., Japan). The diameter of the real sensing part of the transducer is 3 mm. The signals were amplified with the amplifier of 11 dB gain.

Results Figure 8 shows the plots of the applied load, the number of AE events and the amplitude with respect to COD. The whole process of test was divided into four stages. The point (A)

11

Figure 9 Results of source location of GFRP during fracture test.

corresponds to AE events increasing rapidly. And the point (B) is the starting point of nonlinear behavior in the COD - load curve. The point (C) corresponds the maximum load point. The point (D) means the final point of measurement. With increasing COD, the load is increased linearly up to point (B). After point (B), the load shows a nonlinear behavior. The catastrophic increase of the number of events started from point (A) does not almost affect on the linearity of the COD - load behavior. However, the number of the events with high amplitude increases from the point (A). Generally there are many fracture mechanisms in GFRP, for example, delamination, debonding, matrix crack, pull-out and fiber breakage etc. These all mechanisms have a possibility to generate AE. The AE events analyzed in present work may be due to these all mechanisms, too. To characterize each generation mechanism of AE sources is very important but it is very difficult. Therefore in this work we regard all fracture mechanisms as a microcracking mechanism and investigate the forming process of the fracture process zone and the crack propagation process by three-dimensional anisotropic source location technique. The source location result of each stage was shown in Figure 9. From the point (O) to (B), fracture process zone began to take place. The AE events are distributed in the range between -4 mm and +8 mm from the notch tip in the crack propagating direction. However, the AE

12 events are concentrated mainly in the range between 0 and 1.2 mm and show a curve clearly in x-z plane. This tendency is general in metal. This curve seems to correspond to the tip of the main crack. The main crack does not propagate until reaching the point (B). Then, the fracture process zone seems to be fully formed at the point (B). At (B), a nonlinear behavior of COD load curve initiates and the main crack begin to propagate. However both edges of the main crack located near the lateral surfaces of the specimen do not propagate yet. And after the maximum load point (C), the crack begins propagating through the width of the specimen. From above results, we found that our technique for AE source location can be used as a very powerful tool to determine the main crack front and to understand the fracture process in these materials.

NON-CONTACT MEASUREMENT The thermal barrier coating becomes very important for high temperature use. In this material, it is essential to evaluate the thermal shock or fatigue damage. It becomes very important to understand the microfracture process during thermal cycle. AE method is a well-known technique for in-situ monitoring of fracture behavior by attaching piezoelectric transducer. However, using piezoelectric transducer for detection of AE signals has several limitations. As the piezoelectric transducer must be in direct contact with the specimen, it cannot be used in harsh environment like high temperature environment. Then, to apply AE methodology with transducers to the thermal cycle test, extra systems such as a wave-guide are needed. The use of it makes the AE source analysis almost impossible. In recent years, numerous efforts have addressed the substitution of laser-based techniques for ultrasonic nondestructive evaluation in place of conventional piezoelectric transducers [33, 34]. Since a laser interferometer makes no physical contact with the surface being observed, it can be used in high temperature environment. Furthermore, it has the potential for overcoming most of the transducer problems such that the transducer loads the sample and potentially disturbs the process being monitored. The detected signal is averaged over a finite element. Further uncertainty is caused by variations in couplant thickness between the transducer and the specimen and the detected wave is affected by the properties of a sensor and coupling

Figure 10 Schematic of AE measurement setup using laser interferometer during thermal cycle test.

13 media. Especially, a laser interferometer can be used to measure Doppler-shifted scattered light from which the displacement or velocity of being monitored can be calculated. It is expected to perform accurate analysis for AE source wave. However, there are few reports referring to the detection of AE signals in the practical materials and testing because of the difficulty of experiments. The objective of this study is to investigate the possibility of using laser AE system in high temperature environment and to study the quantitative evaluation of fracture behavior in thermal barrier coatings.

Experimental The specimen was deposited by plasma splaying of A1203 top coat and Ni-Cr-A1-Y bond coat onto SUS304 with the thickness of ceramic coating layer of about 500ktm and bond layer of about 100~tm. The substrate size dimension was 15xl5x5mm. Detection of AE signals generated from microfracture was performed using thermal cycle test. Specimens were thermally cycled between room temperature and maximum temperature as 900, 1000, 1100, and 1200~ Heating of the sample was achieved with an infrared image furnace, and the temperature was controlled by a thermocouple placed on the surface of the specimen in heated zone. A thermal cycle consisted of a constantly heating of maximum temperature with a rate

Figure 11 Schematic of a heterodyne interferometer. 1400 1200

1400 I 1200

1000 o~ 800

1000 ~ ~ 800 ~!

~_

~

600

400

200

200 I I I

0

I I I I I I

1000

0

. : . ~ : : : =:= : ~. : : . .

2000 "time/s

3000

4000

0

1st cycle

A 2nd cycle r-! 3rdcycle

6oo

400 0

(b) 900%

f l 1 1 1 I Itd~"lr

0

: : : : : : : : : : : : : : : : : : : : : : : : : :

1000

2000 "[]me/s

3000

4000

Figure 12 Temperature history and detected AE events during thermal cycle test.

14 10~ a 10 seconds holding at maximum temperature, and a cooling to room temperature. The samples were thermally cycled until the large-scale delamination had been observed. Figure 10 shows the setup for AE measuring using a laser interferometer. The laser interferometer was a commercial heterodyne one (Graphtec, AT0022), which could directly measure the velocity of objects more than 2pm/s applying Doppler effect with He-Ne laser. The schematic diagram of the mechanism is shown as Figure 11. The experimentation was performed under the setting of LPF 300kHz and HPF 200Hz to decrease noise level during the test. The coated side of a specimen was heated by infrared furnace and the laser right was focused to the opposite center of the substrate surface that was polished to obtain the reflected light, and detected AE waveforms were recorded by the wave memory with sampling rate of 200ns and 2kwords.

Results

Figure 12 shows the typical temperature history and detected AE events for holding temperature of 1200~ and 900~ Different symbols are used to represent the detected AE signals at different cycles. Though the number of detected AE events was less than the reported similar tests with piezoelectric transducers and a wave-guide [35, 36], AE signals during thermal cycle test could be detected by the laser interferometer. AE signals were also generated during cooling period and detected from the temperature of about 400~ With the decrease of holding temperature, the number of cycles until large-scale delamination tended to increase, but the effect of holding temperature was not so remarkable, since cooling rate was rather low. From SEM observation, the delamination was recognized in the ceramic layer just above the interface between top and bond coatings. Since vertical cracks for the interface were not observed, the detected AE signals were due to delamination. AE waveforms during thermal cycle test can be classified into three groups. The typical waveforms are shown in Figure 13. One group denoted type A has a relatively high frequency component. These AE signals were generated early stage of cooling period and low amplitude. The group, denoted type B, has rather low frequency component. These AE signals were detected at low temperature or a few cycles later. The others were detected at the final failure and have very high amplitude. There are some factors that affect the AE waveform such as the location of AE events, the fracture mode, the generation time of AE source and so on. Under

.~ C,B

0.004

0.015

0.0035

0.01

E 0.0025 0

(b)

0.003 o

0.002

o(D 0.0015 >

ooo o

-0.005

0.001

0.0005

-0.01 ii

150

i i i i i

170

i i i i i

190

i i i i i

210

]]me( It s)

i i i i i

230

-0.015

ii

250

........................ 150

170

190

210

230

250

]]me(//s)

Figure 13 Examples of waveform of the detected AE signals, (a) type A and (b) type B.

15

600

O O

E 500

o~ 400 o

300

0 9

,,, 200

O0 9 0

o

o 100 0

500

1000

1500

Temperature Difference A T/~

Figure 14 Distribution of crack radius of type A (closed circle) and type B (open circle).

the assumption that the whole events are generated at the center of a specimen by the mode I fracture mode, the AE source analysis was carried out. Source function calculated from both Type A and B. From the calculated source intensity parameter, Do, the crack radius, a, was estimated applying following equation (12), which is derived by considering the volume change in the crack growth [37], a = ( (1 - 2v) Do/16 (1 -- V2) O'0 )1/3

(12)

where v is the Poisson's ratio and ~0 represents the fracture stress. Figure 14 shows the crack radius distribution for temperature difference, A T, between holding and AE detected temperature. The crack radius are distributed from 100gm to 600gm

CONCLUDING REMARKS AE waveform analysis was employed to evaluate the microfracture process in the SiC particle reinforced glass matrix composite. Locations of microfracture of this composite during the fracture toughness test were estimated. This source model of microcracking and the deconvolution method also enabled to evaluate microcracking size and fracture mode. These results gave an idea of microfracture process in glass matrix composites. An algorithm for three-dimensional AE source location on anisotropic materials was developed to find the more exact position of AE source. The algorithm was applied to GFRP with plain weave fabric structure to analyze of the fracture process zone. The source location results demonstrated that the fracture process zone was fully formed when the linearity of COD - load curve comes to an end and the crack through the thickness of the specimen started propagating after maximum load. Therefore the technique for AE source location in anisotropic materials can clearly evaluate the small microfracture and the propagation of main crack, and it will become the powerful tool to understand the fracture process in fiber composite materials.

16 AE signals during the thermal cycle test could be detected by using a laser interferometer as an AE sensor under non-contact and high temperature condition in plasma-sprayed A1203 coatings onto steel substrate. The detected AE signals were analyzed quantitatively applying AE source technique. The laser interferometer used can be considered to be insufficient in terms of the sensitivity, if the problem of noise and cost are solved. The laser facility will be a promising method for detecting and analyzing acoustic emission waves. REFERENCES 1. Mura, T. (1982). Micromechanics of Defects in Solids. Martinus Nijhoff Publishers, The Hague. 2. Aki, K. and Richards, P.G. (1980). Quantitative Seismology Vol.1. W. H. Freeman and Company, San Francisco. 3. Wadley, H.N.G., Scruby, C.B. and Shrimpton, G. (1981) Acta Metallurgica 29, 399. 4. Kishi, T., Ohno, K. and Kuribayashi, K. (1981) J. Japan. Soc. ND130, 911. (in Japanese) 5. Ohira, T. and Pao, Y.H. (1987). In: Solid Mechanics Research for Quantitative Non-Destructive Evaluation, pp. 411-423. Martinus Nijhoff Publishers, Dordrecht. 6. Scruby, C.B., Stacey, K.A. and Baldwin, G.R. (1986)J. Physics D 19, 1597. 7. Ohtsu, M. (1988). In: Progress in Acoustic Emission IV, pp. 67-74, The Japanese Society for Non-destructive Inspection. 8. Kim, K.Y. and Sachse, W. (1986) Int. J. Fracture 31, 211. 9. Enoki, M. and Kishi, T. (1988) Int. J. Fracture 38, 295. 10. Fukunaga, Y., Enoki, M., Kishi, T. and Kihara, J. (1990) J. Vib. Acoust. 112, 45 11. Hsu, N.N., Simmons, J.A. and Hardy, S.C (1977) Mater. Eval. 35, 100. 12. Ohisa, N. and Kishi, T. (1982). In: Proceedings of the 1982 Joint Conference on Experimental Mechanics, pp. 359-364, Society for Experimental Stress Analysis. 13. Enoki, M. and Kishi, T. (1991). In: Acoustic Emission: Current Practice and Future Directions, ASTM STP 1077, pp. 47-66, Sachse, W., Roget, J. and Yamaguchi, K. (Eds). American Society for Testing and Materials, Philadelphia. 14. Kishi, T. and Enoki, M. (1991). In: Fracture Mechanics: Current Japanese Materials Research Vol. 8, pp. 217-234. Elsevier, Oxford. 15. Enoki, M., Valentin, D., Tsuda, H. and Kishi, T. (1992) Nondestructive Test. Eval. 8-9, 857. 16. Mashino, S., Shiwa, M. and Kishi, T. (1992) Nondestructive Test. Eval. 7, 93. 17. Suzuki, H., Kinjo, T., Saito, N., Takemoto, M. and Ono, K. (2000) NDT and E International 33, 173. 18. Koo, J.H., Kim, B.N., Enoki, M. and Kishi, T. (1999)J. Japan. Soc. ND148, 283. 19. Ohara, Y., Shiwa, M., Yanagida, H. and Kishi, T. (1995) J. Ceram. Soc. Japan 103, 664. 20. Grabec, I., Sachse, W. and Grabec, D. (1993) Mater. EvaL 51, 1174. 21. Hayashi, Y., Ogawa, S., Cho, H. and Takemoto, M. (1999) NDT and E International 32, 21. 22. Enoki, M., Fujita, H. and Kishi, T. (1996). In: Progress in Acoustic Emission VIII, pp. 64-69, The Japanese Society for Non-destructive Inspection. 23. Watanabe, M., Chivavibul, P., Mori, H., Enoki, M. and Kishi, T. (1999) J. Japan. Soc. NDI 48, 369. 24. Kageyama, K., Enoki, M. and Kishi, T. (1995)J. Ceram. Soc. Japan 103, 205. 25. Enoki, M. and Kishi, T. (1996) Mater. Trans. JIM 37, 399. 26. Fujita, H., Enoki, M. and Kishi, T. (1996)Mater. Trans. JIM 37, 776. 27. Scruby, C.B. and Baldwin, G.R. (1984)J. Acoust. Emiss. 3, 182.

17 28. Rothman, R.L., Greenfield, R.J. and Hardy, H.R., Jr. (1974) Bull. Seism. Soc. Am. 64, 1993. 29. Kinjo, T., Suzuku, H. and Takemoto, M. (1997) Trans. JSME 63, 178. 30. Tang, B., Henneke II, E.G. and Stiffier, R.C. (1988). In: Acousto-Ultrasonics Theory and Application, pp. 45-65, Plenum Press. 31. Ziolar, S.M. and Gorman, M.R. (1992). In: Proc. of Fourth International Symposium on Acoustic Emission from Composite Materials, pp. 411-417. 32. Every, A.G. and Sachse, W. (1992) Ultrasonics 30, 43. 33. Palmer, C.H. (1986). In: Review of progress in Quantitative Nondestructive Evaluation 5A, pp.651-658. 34. Bruttomesso, D.A. (1993) J. Eng. Mech. 119, 2303. 35. Enoki, M., Kishi, T., and Mantyla, T. (1993). In: Proc. Ultrasonics International Conference, pp. 243-246. 36. Bemdt, C.C. (1989)J. Mater. Sci. 24, 3511. 37. Hutchinson, J. W. (1987) Acta Metallurgica 35, 1605.

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19

M O M E N T T E N S O R A N A L Y S I S OF AE AND SIGMA C O D E

MASAYASU OHTSU Professor, Graduate School of Science and Technology, Kumamoto University 2-39-1 Kurokami, Kumamoto 860-8555, JAPAN

ABSTRACT Acoustic emission (AE) is an inspection technique by means of detecting elastic waves due to dynamic motions at an AE source, such as cracking, delamination, cleavage, fretting and so forth. To treat AE waves theoretically, elastodynamics has been introduced. This is because elastodynamic theories are indispensable to model AE sources and to solve wave propagation. For quantitative analysis on kinematics of AE sources, a moment tensor has been derived from the theories. For the analysis of a large amount of AE wave data, a simplified procedure is developed and is implemented as a SIGMA (Simplified Green's functions for Moment tensor Analysis) code. After presenting theoretical treatment of AE waves for SIGMA analysis, case studies on error estimation and applications to damage evolution in cementitious materials are discussed. KEYWORDS Elastodynamics, moment tensor analysis, SIGMA code, crack kinematics, damage mechanics

INTRODUCTION An application of the moment tensor analysis to AE waves was formerly found on cracking mechanisms of glass due to indentation [ 1], where only diagonal components & t h e tensor were analyzed. Later, a generalized treatment was clarified [2]. To deal with an inclined crack to the coordinates system, all moment tensor components were taken into consideration [3, 4]. For the analysis, the spatial derivatives of Green's functions are inevitably required [5]. Although Green's functions themselves of a finite body can be empirically determined from a pencil-lead break experiment [6], the spatial derivatives are analytically obtained only in an infinite or a semi-infinite body. Accordingly, numerical solutions by F D M (tinite difference method) were applied to AE waves in CT (compact tension) specimens of metal [7]. This procedure, however, needed a vector processor for computation and was not readily available for processing a large amount of AE waves, which might be obtained even in a single fracture experiment. Based on the far-filed term of P wave, therein, a simplified procedure was developed [8], which was suitable for a PC-based processor and robust for computation. The procedure is now implemented as a SIGMA (Simplified Green's functions for Moment tensor Analysis) code, and has been successfully applied to fracture tests of reinforced concrete specimens [9, 10]. It is also applied to mode-identification study in the mixed mode loading

20 of concrete [11]. This tensor components, but reported [12], but was version of the procedure

is because the procedure provide information not only on moment also on crack type and crack orientation. A similar procedure was limited to the two-dimensional (2-D) problem. Two-dimensional (SiGMA-2D) is already developed for in-plane wave motions [ 13].

Recently, the decomposition of the moment tensor components different from that of SIGMA is proposed in relation with seismic sources [14]. In addition, the relative moment tensor inversion is proposed [15]. Taking into account these latest attempts, the basics of the moment tensor analysis is reviewed. Then, case studies on error estimation and applications to damage mechanics are presented.

M O M E N T TENSOR Elastic waves due to microcracking are discussed herein only in a homogeneous medium. Although many materials are not homogeneous but heterogeneous, the material property of constituents is physically dependent on the relation between the wavelengths and the characteristic dimensions of heterogeneous materials. In the case that the wavelengths are even larger than the sizes of heterogeneous inclusions, the effect of heterogeneity is inconsequent. For AE waves in concrete, for example, the velocity of elastic: waves is over 1000 m/s, and then the waves of frequency range up to some 100 kHz correspond to the wavelengths longer than several centimeters. It results in the fact that the concrete consisting of normal aggregate (of around 10 mm diameter) is reasonably referred to as homogeneous. It is noted that the wavelengths to be detected should be physically deployed in the propagating medium. This is occasionally not the case of AE waves in steel plates, because the thickness of the plate is shorter than the wavelengths. In the case that wavelengths of propagating waves are longer than the thickness, diffracted and dispersive waves are dominantly generated. Because these waves are not associated with generating mechanisms, it may result in the fact that source characterization of AE in the steel plate is not a easy task. Elastodynamic solutions of wave motions u(x,t) are mathematically represented as, Uk(X,t) = f s[Gki(X,y,t) * ti(y,t) - Tki(X,y,t) * ui(y,t)]dS,

(1)

where u(x,t) and u(y,t) are displacements, and t(y,t)is a traction. The asterisk symbol * represents the convolution integral. G~k(x,y,t) is Green's function and T~k(x,y,t) are the associated tractions with Green's functions, Tik(X,y,t) = Gip,q(X,y,t) Cpqik ni.

(2)

Here Cpqii are the elastic constants, Gip,q(X,y,t) is the spatial derivative of Green's function as given by 8 Gip(X,y,t)/8 Xq and n is the normal vector to the boundary surface S. Crack modeling as an AE source starts with considering internal surface F as a crack surface. To introduce the discontinuity of physical quantities on the surface, virtual two surfaces F + and F- are introduced as shown in Fig. 1. Before a crack is nucleated, these lwo surfaces

21 deform concurrently. Due to cracking, the discontinuities of traction t(y,t) and displacement u(y,t) are generated between the two surfaces. These are denoted by using superscripts + and - on surface F + and F-, respectively, fi(y,t) = ti+(y,t) + t/(y,t)

(3)

bi(y,t) = ui+(y,t) - ui-(y,t).

(4)

These correspond to the mathematical representation of dislocation motions. For example, vector b(y,t) is identical to Burgers vector. Replacing the surface S to the surfaces F + and F-, and applying eqs. 3 and 4 to eq. 1,

X3 f

11

n,~F -F+

Fig. 1

•

Crack (dislocation) surfaces F + and F-.

Uk(X,t) = f F+[Gki(X,y,t) * ti+(y,t) - Tki+(x,y,t) * ui+(y,t)]dF, + f v-[Gki(X,y,t) * ti-(y,t) - Tki-(x,y,t) ~kui-(y,t)]dF Here Tik+ and Tik- contain the normal vector n + and n-, respectively. and F = F-,

(5) Assuming n = n- = -n +,

Uk(X,t) = - f v[Gki(X,y,t) $ [-t~+(y,t)] + Tki(X,y,t) $ [-ui+(y,t)]]dF, + f F[aki(X,y,t) * tq-(y,t) - Tki(X,y,t) $ ui-(y,t)]dF = f ~[Gki(x,y,t) * [t+(y,t) + ti-(y,t)] + Tki(x,y,t) * [Ui+(y,t) - ui-(y,t)]]dF = f F[Gki(X,y,t) • fi(y,t)

+ Tki(X,y,t) * bi(y,t) ]dF.

(6)

Although the dislocation on the traction is considered in eq. 3. M o s t l y the sum of tractions on crack surface F results in the equilibrium condition, fi(y,t) = 0, inside the medium. It implies that eq. 3 is only applicable to such a case where a force is driven as pencil-lead break. Neglecting the effect of the displacement discontinuity b(y,t), elastic waves due to the pencillead break are represented, Uk(X,t) = Gki(X,y,t) * fi(y,t)AF,

(7)

22 where f(y,t)AF is the released force at the area AF.

Because it is reasonably assumed that

fly,0AF is equivalent to Heaviside's step function [6],

(s)

Uk(X,t) = f Gki(X,y,t) fi dt,

where f is the direction vector of the pencil-lead break. Thus, Green's functions G~(x,y,t) of a finite body can be empirically obtained by the pencil-lead break as du(x,t)/dt. Due to cracking, the dislocation motion is introduced as b(y,t) in eq. 4.

From eq. 6,

Uk(X,t) = f VTki(X,y,t) * bi(y,t) dF -- f F

Gkp,q(X,Y,t)

Cpqij ni

(9)

* bi(y,t)dF.

This is the integral representation of AE wave due to cracking and was applied to simulation analysis of AE waves [ 16, 17]. It is noted that elastic waves due to cracking are related with not Green's function itself, Gki, but the spatial derivatives Green's function, Gkp.:|. As a result, AE waves due to pencil-lead break have nothing to do with the source characterization of cracks. This is because that it is practically not possible to compute the spatial derivatives of Green' functions from eq. 8. b

2>

\

(a) Crack Motion Fig. 2

(b) Moment tensor

Modeling crack motion by the moment tensor.

Crack motion is illustrated in Fig. 2 (a). A pure tensile crack is represented as the case that vector b is parallel to vector n, while a pure shear crack corresponds to the case that vector b is perpendicular to vector n. In general cases, cracks are classified, depending on the magnitude of normal component and tangential component of crack vector b. The treatment is quite comparable to mode I, mode II and mode III of fracture mechanics, although crack motions are specified not at the crack tip but on the crack surface. It is noted that a crack which has an opening component does not necessarily means a tensile crack. This is because an opening crack could contain dominant tangential motion. Referring to crack vector b(y,t) as b(y)lS(t) where b(y) is the magnitude of crack motion, I is the unit crack-motion vector and S(t) is the source-time function, the integration Cpqiinjbi(y,t) of eq. 9 over crack surface F is performed under the point-source assumption as, f F Cpqijnjbi(y,t)dS = f F Cpqkl b(y)lk nl S(t) dF - Cpqkllknl [ J" vb(y)dF]S(t)

23

(lo)

= Cpqkllknl AV S(t) = Mpq S(t).

Here AV is the crack volume. The moment tensor, Mpq,is defined by the product of the elastic constants [N/m 2] and the crack volume [m3], which leads to the moment of physical unit [Nm]. In the case of an isotropic material, \ /~lknk + 2btllnl, ~(11n2 + 12nl), ~(lln3 + 13nl) \ Mpq = ~ lt.t(12nl + 11n2), ~lknk + 2~tl2ne, ~t(12n3+ 13ne) ] AV, \~(13nl + 11n3), lt.t(13n2+ 12n3), )~lknk+ 2~(13n3)] /

where )~ and ~t are Lame constants.

(11)

Originally, the seismic moment was defined by the

product ~tbAF of shear modulus ~t, shear displacement b, and area of fault AF [ 18]. Setting 1 = (1, 0, 0), n = (0, 1, 0) and AV = bAF in eq. 11, Mpq = 2~tbAF. Thus, the tensor is named the moment tensor. The moment tensor is obtained from the product of elastic constants, crack vector, crack normal and the crack volume. The product of crack vector and crack normal is proposed as a crack strain tensor in the damage mechanics [19] and also an eigenstrain in micromechanics [20]. This suggests that the moment tensor is comparable to a stress due to crack nucleation, as a symmetric second-order tensor. Crack kinematics is sometimes depicted by such equivalent forces that dipole forces and couple forces [21]. The dipole forces are known as two forces of the same magnitude and the opposite directions on the coincident straight line. The couple forces are two parallel and opposite-direction forces with infinitesimal distance. Mathematically, these forces (rigorously tractions) correspond to particular components of the stress. As can be seen in Fig. 2 (b), normal components of the moment tensor are identical to dipole forces, while couple forces correspond to tangential (shear) components. The concept of the equivalent forces is sometime so misleading that nucleation of a tensile crack is represented by only a pair of dipole forces. In the case of a pure tensile crack, a scalar product lknk =1. Since all diagonal components contain the scalar product as given in eq. 11, the tensile crack should be modeled by three normal components (three pairs of dipole forces). In contrast, couple forces correspond to off-diagonal components in eq. 11. Since the moment tensor is symmetric, double-couple forces are rational. Applying the moment tensor to eq. 9, ui(x,t) = Gip,q(X,y,t)Mpq * S(t).

(12)

In seismology, Taylor series expansion is sometimes depicted in eq. 12 [15]. This results from the case that Green's functions in the corresponding boundary conditions are not completely but approximately obtained. In addition, the point-source assumption does not mean infinitesimal AE sources, but crack vector b(y,t) and dimensions of crack surface F are small enough compared with the wavelengths detected. It implies that most AE sources in materials could be properly formulated by the point-source approximation.

24 SIGMA CODE

Waveform Parameters" By taking into account only P wave motion of the far field (1/R term) of Green's fimction in an infinite space, the displacement Ui(x,t) of P wave motion is represented by eq. 12, Ui(x,t) = - 1/(4~pVp 3) rirprq/R dS(t)/dt Mpq.

(13)

Here P is the density of the material and Vp is P-wave velocity. R is the distance between the source y and the observation point x, of which direction cosine is r = (rl, r2, r3). Considering the effect of reflection at the surface and neglecting the source-time function, amplitude A(x) of the first motion is represented as,

A(x) = Cs Ref(t,r)/R

(rl, r2, r3)

/m,,, m,3) (r,)

~ m12, m22, m23 m13, m23, m33

r2 r3 ,

(14)

where Cs is the calibration coefficient including material constants in eq. 13. t is the direction of the sensor sensitivity. Ref(t,r) is the reflection coefficient at the observation location x. In the relative moment tensor analysis [15], this coefficient is not taken into consideration, because the effect of the sensor locations are compensated. Since the moment tensor is symmetric, the number of independent unknowns mpq to be solved is six. Thus, multi-channel observation of the first motions at more than six channels is required to determine the moment tensor components. Although another procedure for the two-dimensional (2-D) problem was proposed [12], eq. 14 is readily applicable to the 2-D problem, where AE waves are detected as in-plane waves [13]. 13= n3 = 0 in the in-plane problem and, m33 = V (mix + m22),

(1 5)

where v is Poisson's ratio. Thus, more than 3-channel system is available for determining moment tensor components ml 1, m12, and m22. To solve eq. 14, the amplitude A(x) of the first motion has to be read from AE waveforms. Concerning the first motion, the arrival time should be also read to perform the source location analysis. An example of AE waveform recorded is given in Fig. 3, from which two parameters of the arrival time (P 1) and the amplitude of the first motion (P2) are determined. In the source location procedure, source location y is determined from the arrival time differences. Then, distance R and its direction vector r are determined. 5-ch~mnel system is desirable for locating AE sources in the three-dimensional (3-D) coordinates. For the twodimensional (2-D) problem, 4-channel system is necessary. To determine the moment tensor components, 6-channel system is needed for the 3-D and 3-channel system for the 2-D. Thus, 6-channel system is necessary for the 3-D and 4-channel for the 2-D.

25

~.pencil-lead ~

Atrt v

P1\

o

/

P2 J

tJ .,4

0

I

128

Fig. 3

/ 9

I I ! !

/

t

I

256 T i m e (Usec)

384

Sx2

AE waveform recorded.

I

O

break

9

block

9

AE s e n s o r s

Fig. 4

Sensor calibration.

For determining the two parameters in Fig. 3, visual observation is not only suitable for a few number of well defined events, but also is too sensitive to the subjectivity ot' the inspector. For processing a large amount of data, a computer aided method is proposed [22], by employing an adaptive moving average and a Laplacian filter. It is reported that only 15% of all detected events had to be discarded due to either ambiguous arrival times or failure of the source location routine to converge.

Sensor Calibration Prior to solving eq. 14, calibration coefficient Cs should be determined to compensate the sensitivity of AE sensors. For example, all sensors to be employed in the test are attached on a sample block as shown in Fig. 4. Here, mechanical properties of the block are inconsequential but the characteristic dimension has to be large enough compared with the maximum size of inclusions and defects. At a certain location of the sample block, a pencil lead is broken. AE wave at each sensor is recorded and the amplitude of the first motion is read. Referring to eq. 14, the amplitudes are calibrated by distance R and reflection coefficient Ref(t,r). Thus, relative calibration coefficients, Cs, of all sensors are determined. Since the SIGMA code requires only relative values of the moment tensor components, the relative calibration is sufficient enough. In the case that the sensors are calibrated absolutely, it is possible to determine correct values of the moment tensor. One method based on Davis bar technique was applied to calibrate AE sensors [23].

Eigenvalue Analysis Classification of a crack is theoretically derived from the eigenvalues (principal values) of the moment tensor. From eq. 11, three eigenvalues of an isotropic elastic material are obtained, the maximum eigenvalue = ~t (lknk / (1 - 2V) + 1)AV, the intermediate eigenvalue = 2~tv lknk/(1 - 2v)AV, the minimum eigenvalue = 9 ( lknk / (1 - 2V) - 1) AV,

(16)

26 where the relation k = 2vbt / (1-2v) is employed.

In the case lknk = 1, i. e. tensile crack,

intermediate eigenvalue is identical to the minimum. (1-2v) and others are 2btvAV / (1-2v).

the

The maximum eigenvalues is 2hi(l-v) AV/

These eigenvalues are uniquely decomposed into

deviatoric components: 4btAV/3, -2btAV/3, and -2btAV/3, and three isotropic components: 2bt(1 + v)AV/ (3 - 6v), respectively. The deviatoric components are also known as the compensated linear vector dipole (CLVD) [21 ], because the sum of the all components is equal to zero. In the case lknk = 0, i. e. shear crack, the intermediate eigenvalue is equal to 0, and two others are of the same absolute value and of opposite sign as btAV and -~tAV. The eigenvalues for the shear crack are also deviatoric. It implies that volume expansion, which is nucleated by opening mode, is represented by the isotropic components of the tensile crack. It suggests that not only crack formation but also dynamic sources such as detonation and laser impulse are possibly represented by the moment tensor. Consequently, introduction of the point explosion term [14] is inconsequent. Setting the ratio of the maximum shear contribution as X, three eigenvalues for the shear crack become X, 0,-X. Likewise, the ratio of the maximum deviatoric tensile component is set as Y and the isotropic tensile as Z. It is reasonably assumed that the principal a~es of the shear crack is identical to those of the tensile crack. Then, the eigenvalues of the moment tensor for a general case are represented by the combination of the shear crack and the tensile crack. Because relative values are determined in the SIGMA, eq. 16 is normalized and decomposed,

the intermediate eigenvalue/the maximum eigenvalue the minimum eigenvalue/the maximum eigenvalue

1.0 = X + Y + Z , = 0 - Y/2 + Z, = -X - Y / 2 + Z ,

(17)

tensile components

shear components

I"

0.5Y eigenvalues of shear

deviatoric of t e n s i l e Fig. 5

Z

h y d r o s t a t i c mean of t e n s i l e

Unified decomposition of eigenvalues.

27 where X, Y, and Z denote the shear ratio, the deviatoric tensile ratio, and the isotropic tensile ratio, respectively. This decomposition is schematically shown in Fig. 5. It is pointed out that the ratio X becomes greater than 1.0 in the case that both Y and Z are negative [11]. From eq. 16, the case occurs only when the scalar product lkn k is negative. Recorrecting this term, the three ratios become well-posed. In the present SIGMA code, AE sources of which the shear ratios are less than 40% are classified into tensile cracks. The sources of X > 60% are classified into shear cracks. In between 40% and 60%, cracks are referred to as mixed mode. In the eigenvalue analysis, three eigenvectors are also determined. Solving a characteristic equation of eq. ll, these are mathematically determined and can be represented by using vectors | and n, the eigenvector for the maximum eigenvalue the eigenvector for the intermediate eigenvalue and the eigenvector for the minimum eigenvalue

: : :

l+n, lxn, | - n,

(18)

where x means the vector product. Unit vectors el, e2 and e3 of directions l + n, I x n and l n are determined, respectively. Thus, the vectors | and n, which are interchangeable, are recovered from the following relations, | = (2 + 21knk)el/2 + (2 - 21knk)e3/2, n = (2 + 21knk)el/2 - (2 - 21knk)e3/2.

(19)

Here lknk can be directly computed from eq. 16.

CASE STUDIES Error Estimation

SIGMA procedure was applied to AE sources due to hydrofracturing [8]. In Fig 6, elevation view of AE sources analyzed are shown along with crack types and crack orientations. An arrow symbol indicates a tensile crack of which direction is identical to that of the eigenvector for the maximum eigenvalue. Shear cracks are indicated by a cross symbol, directing two vectors 1 and n. An array of AE cluster and directions of crack orientations are in good agreement with the joint plane which is suggested from the dip angle by boehole-camera observation. AE source mapping suggests the propagation of cracks from the bottm to the top along weak seams of the preexisting joints. AE sources classified into tensile cracks are observed in the outer boundary and crack-opening motions are perpendicular to the joint plane. In contrast, shear cracks are intensively distributed near the borehole and directions of motions are parallel to the joint plane. Thus, reasonable agreement between results of SIGMA analysis and field observation is obtained. Because the variance of the data was not clear, a relative measure of errors was necessary to estimate the accuracy of the results. Thus, the residual in the solutions were determined, because these solutions were obtained from the least square approximation in eq 14. Results

28 0

0.5

1.0

i

1.5

Ill

2.0

I

,I

I

-- y (m)

I I treatment borehole

II II

o._

I[

~No.147

ii II I iI

m ~._

ii

I I

fluid injected

at 5.68 m o.-

No.2

"J

t

)(No.14

~" No.163

No.18,~ I I I ~ No.19J~ i I

iI

i~!/n"g le

IIII

d'p

I I

-

II

"E v

Fig. 6

Crack locations and orientations by SIGMA analysis.

are indicated in column 2 of Table 1. The largest residual was found in event 147, but the value itself is so small that the residuals seem inconsequent to be taken into account. Thus, the covm~mce of the inverse matrix corresponding to the product of Green's functions was computed. These are given in columns 3-8 in the table. The largest elements are found in GG33 components (gravitational direction). This is because all sensors were set in horizontal Stage I shear

or

eo

mixed

-./

tensile

Reinforcement

Stage III

tensile

shear or mixed . ~ ~ ~ '~ ............................

Reinforcement

Stage IV ~

shear or mixed

tensn~___~. . . . . r . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Reinforcement

Fig. 7

Results of the post-analysis of SiGMA procedure.

29 directions. Finally, the ratio of the largest to the smallest eigenvalues of the product of Green's functions was computed as an estimate of the condition number, which are indicated in column 9. It is found that the tensor components obtained in event 2 are more reliable than those of event 117. Table 1

Error estimation of the moment tensor components C o v a r i a n c e Matrix

Event

Variance of Residuals

GGil

2

2.648 • 10 -22

2.70

14

6.390 • 10-23

sym. 4.80

18

1.787 • 10-22

sym. 2.06

19

2.779 • 10 -22

sym. 0.86

20

2.911 • 10 -22

sym. 0.22

117

1.141 • 10 -22

sym. 0.70

147

3.656 x 10 -21

sym. 45.4

163

1.721 • 10 -22

sym. 2.36

sym.

GGI2, GG22

GGI3 - GG33

GGI4 - GG44

GGI5 - GG55

0.00 1.47

0.30 0.23 5.03

- 1.75 0.33 0.14 6.46

- 1.35 0.36 -0.74 4.08 6.07

-0.88 2.84

- 1.98 2.87 6.28

- 1.32 0.50 0.82 2.46

1.67 - 1.36 -3.06 -0.45 2.87

-0.24 0.94

1.62 0.45 2.63

-0.74 -0.01 -0.62 2.03

0.43 -0.09 -0.81 0.15 3.96

-0.14 2.74

0.51 0.22 1.23

-0.01 -0.19 -0.05 0.63

0.15 - 1.18 -0.15 0.06 3.40

0.11 1.13

0.36 -0.01 3.85

-0.02 0.00 0.34 1.55

0.01 0.16 0.06 -0.08 2.64

-0.54 0.97

1.02 -0.04 4.48

-0.31 0.18 -0.01 2.31

0.17 0.09 0.11 0.16 3.59

-0.35 34.9

- 1.46 37.5 47.1

-0.04 1.40 1.46 3.26

9.67 13.9 16.9 2.23 12.8

0.57 1.07

0.36 0.66 2.09

-1.02 -0.34 -0.44 3.54

0.64 0.19 1.18 -0.56 2.23

GGI6 - GG66 - 1.01 -0.61 - 1.76 - 3.60 0.85 21.0 -0.16 - 1.25 - 1.44 -0.89 0.46 2.89 - 1.98 -0.31 -1.53 0.08 -0.16 7.36 -1.11 -0.04 -0.94 -0.17 -0.21 5.50 -0.29 0.00 -2.36 - 1.08 0.15 8.14 -0.83 0.19 -2.88 -1.09 -0.65 10.5 - 13.7 33.3 41.4 0.66 11.3 41.9 -1.32 1.01 -0.05 -3.80 0.89 16.6

Condition Number 3.34

8.46

19.4

4.27

16.9

35.1

34.0

5.05

In addition to this error analysis, error estimation was also performed assuming location errors. Then, it was realized that the accuracy was highly dependent on the geometrical configuration between the sensor array and AE sources. It implies that the accuracy of the solutions is dependent on the cases and may not be able to be estimated from the. simple criterion. Consequently, another procedure to select reliable solutions was investigated. Theoretical AE waveforms at actual AE sensor locations in the test can be computed, by applying eq. 12. As a post-analysis, therefore, the data of AE source location and moment

30 tensor components determined by the SIGMA analysis are applied. For simplicity, the spatial derivatives of Green's functions in an infinite space are employed, taking into account the reflection coefficients and the calibration coefficients. Although one procedure was proposed to compare theoretical waveforms with AE waveforms detected [ 12], the comparison of waveforrns at all channels is time-consuming and is not quantitative. From theoretical first motions, the two data ofP 1 and P2 for the SIGMA analysis are read and analyzed again. Thus, the data of reasonable agreement on the source locations (the difference < 5 ram) and the moment tensor components (the difference of the shear ratio X < 5%) are selected as reliable solutions. One result of a reinforced concrete (RC) beam under bending [24] is shown in Fig. 7, where two directions ! and n are plotted for both tensile and shear cracks, after crack-type classification.

Damage Evolution and Crack Volume In the damaged mechanics, a crack strain is defined as [19], (20)

ec~j = (b~ni + bini)AF/2V*,

where V* is the representative volume element (RVE), crack-motion vector b and normal vector n is defined at crack surface AF. Then the crack-density parameter e is derived, (21)

e = ni eCti ni = A v / v , lknk. From eqs. l0 and 11, the trace of the moment tensor components is obtained as,

(22)

Mkk = (3)~+2g) lknkAV. Thus, the damage evolution process can be estimated from the moment tensor,

(turn)

120

"~..' ........... i...: ........................

......... :. l-~----I:

Li t ,0 Fig. 8

Configuration of an off-center notched mortar beam.

31 Z e(i) = 1 /V* Z [AV lknk] (i) = 1 / [(3X+21a)V*] Z [M~] (i).

(23)

In a three-point bending test of a mortar beam shown in Fig. 8, SIGMA analysis was performed [25]. Results are given in Fig. 9. Both types of tensile cracks and shear cracks are observed

-u 4-

~,, ~

4/

tensile crack

+ mixed-mode %

& s h e a r crack

,..

~

,,

Elevation v i e w Fig. 9

Results of SIGMA analysis.

along the final crack surface. Then, relative values ZMkk is computed. Damage evolution by eq. 23 is given in Fig. 10. It is observed that a sudden increase of the damage start with the occurrence of tensile cracks of small damage, and then shear cracks and mixed-mode cracks follows, introducing high damage accumulation. !

::3 --.1" ;>

_

>.

1

'"

9 tensile

I

I

I

' I'

"!

0

crack

a mixed-mode o shear crack

j _ A

!

1

i

Zmkk/lknk ,~cr

i

ca:a:Iz~"

.= C~

" o

~

z.mkk

? 1

5O0

0

lO00

AlE hit n u m b e r Fig. 10

Damage evolution process in the notched beam.

From eq. 22, the crack volume is obtained from, AV = Mkk/[ (3)~+2[ut)lknk].

(24)

This implies that the crack volume is also obtained from the trace component of the moment tensor. To determine the crack volume, however, the moment tensor should be determined

32 as calibrated values. This implies that AE sensors must be calibrated prior to the experiment. It is noted that sensor calibration is not necessary for the standard SIGMA procedure, because only relative values are applied to the eigenvalue analysis. In the uniaxial compression tests of mortar plates with a slit, AE waves were recorded b y applying calibrated sensors [23]. (b) Slit angle = 30 ~

(a) Slit angle = 0 ~

I

I•

x

t

l

I

.~,

e

/

l

N"~

I

1

Results of SIGMA analysis in plate specimens with a slit of mortar.

Fig. 11

0.001

,

i

'

I

'

0.0008

*

~ 0.0006-

,

'

_ _

_

o

,_~

"

'

o

-e 0 . 0 0 0 4 00002.-

,

(a) Slit angle = 0 ~

oo

o

-

-

o

.~~

oo

~'a

~

~

|

_

o

( Shear ratio (%) 0

90 0 0 4

'

I

'

I

'

I

'

I

'

(b) Slit angle = 45 ~

0.0003 _~ 0.0002 O

o

~0.0001(~

~ "0

( ~ ~o

O

= 0

oo

~ oo2~ ~ ~

--20

40

o

o

O

o

o

O0

~o~ ~o

60

o

~

80

o

100

Shear ratio (%) Fig. 12

Crack volumes estimated in mortar specimens.

33 Results of SIGMA analysis are shown in Fig. 11. In the experiments, slit angles were varied as 0~ 30 ~ and 45 ~ Results of the first two cases are given. In both cases, high intensity zone of AE cluster is observed around the tip of the slit. The zone is well known as the fracture process zone in cementitious materials. The crack volumes determined are plotted against the shear ratio in Fig. 12. Two cases of the inclined angles of the slit are shown. It is observed that AE events of the larger crack volumes are observed in the narrow region of smaller shear ratios than 50% in the case of the 0~ slit model. The distribution becomes broad in the case of the slit angle 45 ~. Comparing the two slit models, it is found that the crack volumes decrease in mortar with the increase of the slit angle. It results in the fact that crack extension may be arrested due to the mixed-mode extension, which could be observed in Fig. 11.

CONCLUSIONS To clarify theoretical backgrounds of the SIGMA code, theories of the moment tensor are summarized. AE sources can be modeled by the dislocation model and represented by the integral representation. One of important conclusion is that AE waves due to pencil-lead break are formulated differently from those of cracking. The moment tensor analysis of AE is developed as the SIGMA code. The eigenvalue analysis is introduced for the classification of cracks and the determination of crack orientations. Based on these theories, fundamentals on the SIGMA procedure are summarized. Since the moment tensor analysis is very promising for practical applications, case studies on error estimation and the application to damage evolution are discussed.

REFERENCES 1. Kim, K. Y. and Sachse, W. (1984),"Characterization of AE Signals from Indentation Cracks in Glass," Progress in Acoustic Emission II, JSNDI, 163-172. 2. Ohtsu, M. (1987),"Mathematical Theory of Acoustic Emission and Moment Tensor Solution," J. Soc. Materials' Sci. Japan, Vol. 356, No. 408, 1025-1031 (in Japanese). 3. Ohtsu, M. (1987),"Determination of Crack Orientation by Acoustic Emission," MateriaLs' Evaluation, Vol. 45, No. 9, ASTN, 1070-1075. 4. Ohtsu, M. and Ono, K. (1988),"AE Source Location and Orientation Determination of Tensile Cracks from Surface Observation," NDT International, Vol. 21, No. 3. 143-150. 5. Ohtsu, M. and Ono, K. (1984),"A Generalized Theory of Acoustic Emission and Green's Functions in a Half Space," Journal ofAE, Vol. 3, No. 1,124-133. 6. Hsu, N. N. and Hardy, S. C. (1978),"Experiments in AE Waveform Analysis for Characterization of AE Sources, Sensors and Structures, Elastic Waves and Nondestructive Testing of Materials, AMD-Vol. 29, 85-106. 7. Enoki, M., Kishi, T. and Kohara, S. (1986),"Determination of Micro-cracking Moment Tensor of Quasi-cleavage Facet by AE Source Characterization," Progress in Acoustic Emission III, JSNDI, 763-770. 8. Ohtsu, M. (1991),"Simplified Moment Tensor Analysis and Unified Decomposition of AE

34 Source," J. GeophysicalResearch, Vol. 96, No. B4, 6211-6221. 9. Yuyama, S., Okamoto, T., Shigeishi M. and Ohtsu, M. (1995),"Cracking Process Evaluation in Reinforced Concrete by Moment Tensor Analysis of AE," Journal ofAE, Vol 13, No. 1/2, s 14-s20. 10. Ohtsu, M., Okamoto, T. and Yuyama, S. (1998),"Moment Tensor Analysis of AE for Cracking Mechanisms in Concrete," ACI Structural Journal, Vol. 95, No. 2, 87-95. 11. Suaris, W., van Mier, J. G. M. (1995),"Acoustic Emission Source Characterization in Concrete under Biaxial Loading," Materials and Structures, No. 28, 444-449. 12. Ouyang, C., Landis, E. and Shah, S. P. (1991),"Damage Assessment in Concrete using Quantitative Acoustic Emission," Journal of Engineering Mechanics, 117( 11 ), ASCE, 2681-2698. 13. Shigeishi, M. and Ohtsu, M. (1994),"Observation of Mixed-Mode Fracture Mechanics by SiGMA-2D," Journal ofAE, 11(4), 57-63. 14. Shah, K. R. and Labuz, J. F. (1995),"Damage Mechanisms in Stresses Rock from AE, .7. GeophysicalResearch, Vol. 100, No. B8, 15,527-15,539. 15. Dahm, T. (1996), "Relative Moment Tensor Inversion based on Ray Theory: Theory and Synthetic Tests," Geophys. J. Int., No. 124, 245-257. 16. Ohtsu, M. (1982),"Source Mechanism and Waveform Analysis of Acoustic Emission in Concrete," Journal ofAE, Vol. 2, No. 1, 103-112. 17. Ohtsu, M., Yuyama, S. and Imanaka, T. (1987),"Theoretical Treatment of Acoustic Emission Sources in Microfracturing due to Disbonding," J. Acoust. Soc. Am., V ol. 82, No. 2, 506-512. 18. Aki, K. and Richards, P. G. (1980), Quantitative Seismology:Theory and Methods, Vol. 1~ W. H. Freeman, New York. 19. Kachanov, M. (1992),"Effective Elastic Properties of Cracked Solids: Critical Review of Some Basic Concepts," Applied Mechanics Review, Vol. 45, No. 8, 304-335. 20. Mura, T. (1982),Micromechanics of Defects in Solids, Martinus Nijhoff Publishers, The Hague. 21. Burridge, R. and Knopoff, L. (1964),"Body Force Equivalents for Seismic Dislocations," Bull. Seism. Soc. AM., Vol. 54, No. 6, 1875-1888. 22. Landis, E., Ouyang, C. and Shah, S. P. (1992),"Automated Determination of First P-wave Arrival on AE Source Location," Journal ofAE, Vol. 10, No. 1/2, s97-s 103. 23. Shigeishi, M. and Ohtsu, M. (1999),"Identification of AE Sources by using SiGMA-2D Moment Tensor Analysis," Acoustic Emission: Standard and Technology Update, ASTM, STP 1353, 175-188. 24. Ohtsu~ M. (1994),"Post-Analysis of SIGMA Code for AE Moment Tensor Analysis," Journal ofJSNDI, Vol. 43, No. 12, 776-782. 25. Ohtsu, M. and Ohtsuka, M. (1998),"Damage Evolution by AE in the Fracture Process Zone of Concrete," J. Materials, Conc. Struct. Pavement, JSCE, No. 599/V-40, 177-184.

35

WAVELET TRANSFORM -APPLICATIONS TO AE SIGNAL ANALYSISMIKIO TAKEMOTO, HIDEO NISHINO Faculty of Science and Engineering, Aoyama Gakuin University 6-16-1,Chitose-dai Setagaya, Tokyo 157-8572, JAPAN KANJI ONO Dept of Materials Science and Engineering University of California, Los Angeles, CA 090095-1595, U.S.A ABSTRACT Wavelet transform (WT) allows the determination of frequency spectrum as a function of time using short waveform segment or wavelets as the basis functions. Resultant mapping of wavelet coefficients in frequency-time coordinates plane provides more effective informative characterization of acoustic emission (AE) signals. We utilized the WT for AE signal analysis since 1996. It has been successfully utilized for the fracture mode classification and determination of group velocities of dispersive waves such as multi-modes Lamb waves and guided waves in hollow cylindrers. The latter enables us to determine the source location of AE signals in anisotropic media. In this paper, the authors first present a short tutorial on WT, in relation to windowed Fourier transform (WFT). Next we introduce our recent applications of WT to AE signal analysis. The application includes the fracture-type classification in FRPs, determination of group velocity dispersion, utilization of the Ao-Lamb group velocity at a selected frequency for the AE source locations in anisotropic FRPs, modal analysis of hollow-cylinder guided waves and source location of AE signals monitored by a single transducer. At the last, denoising of noisy signals by discrete WT and inverse WT is introduced. wavelet transform, windowed Fourier transform, signal classification, cross-correlation, discrete wavelet, inverse wavelet, denoising, modal analysis, guided wave INTRODUCTION Wavelet transforms (WT) are relatively new topic in signal processing. The theory continues to evolve and their applications are expanding to various fields. The WT is a time-frequency transform of signals. It is described as a time-scale transform in the WT literature. The WT is a direct alternative to the windowed Fourier transform (WFT) and has been applied to a number of areas, including data compression, image processing and time-frequency spectral estimation. Its theoretical foundation was developed by Grossmann and Morlet [1,2,3,]. Daubechies [4,5,6] and Mallat [8,9] brought the concept of wavelets into digital signal processing. For recent monographs, see Chui [10], Daubechies [6] and Kaiser [11]. Within its short history, the WT has become an indispensable tool in various fields. In the analysis of acoustic emission (AE) signals, the Fourier transform (FT) or fast Fourier transform (FFT) is utilized often. Beside providing the frequency spectral information of a signal, FT is the key element of many pattern recognition analysis methods as well as source characterization algorithms. However, FT cannot generate time-dependence of the frequency spectrum because the exponential basis function, exp(-i co t), extends over the entire signal duration. Here, i is the imaginary sign and co is the angular frequency, 2 7cf.

36 The high frequency limit of the FT in the presently practiced digitized form can be increased by increasing the rate of digitization according to the Nyquist theorem. In the FT, a signal of typically limited duration is extended in time to +c~ by periodically repeating the signal. This periodicity defines the resolution of the FT. Thus, the frequency resolution can be improved by extending the duration of the signal analyzed. This, in turn, reduces the time resolution, because the obtained spectrum is for the entire duration. This is due to the uncertainty principle in time t and frequency f; A t A f <= 1/4 7r. The WFT overcomes the drawback of FT to some extent. It consists of multiplying a signal s(t) with a short window function w ( t - z ), centered at time z', and computing the FT of the product s(t)w(t- z ). The window is short relative to the signal duration. Gabor [12,13] used the Gaussian function as the window function and this procedure is now known as Gabor transform. By the Gabor transform, one can estimate the frequency content of the signal s(t) in the neighborhood of z. The window width is constant in Gabor transform and is of the order of the period of the lowest frequency being analyzed, whether s(t) around z has a low or high frequency content. Thus, the uncertainty in time, A t, is still substantial at higher frequencies. WT is a further extension of WFT. Instead of the constant window shape of the basis function of the WFT, the basis function of the WT are scaled with respect to frequency. This will be discussed in the next section in detail. In this paper, the authors review recent applications of WT to AE signal analysis. These include; 1) the fracture-type or bulk-mode AE signal classification in unidirectional glass-fiber reinforced plastics (UD-GFRP) 2) determination of group velocity dispersion and orientation dependence of Lamb waves in carbon-fiber reinforced plastic (CFRP) plates, 3) AE source locations in anisotropic CFRPs by utilizing the Ao-Lamb group velocity at a selected frequency, 4) modal analysis of cylinder waves and source location of AE signals monitored by a single transducer, and 5) denoising or signal recovery of noisy signal by the discrete wavelet (DWT) and inverse discrete wavelet (IDWT) transform. WAVELET T R A N S F O R M

We are interested in the application of the WT to the time-frequency analysis of AE signals and a brief introduction of WT, in comparison to FT and WFT, is given below. The FT is used widely in signal analysis. The Fourier transform and its inverse are defined as follows: S(co) = s(t) exp(-i co t) dt (1)

f

s(t) = (1/2 re) f S ( c o ) exp(ico t) dt (2) where S(o9 ) is the "FT of a signal s(t). The S(co ) is a function independent of time and describes the signal in the frequency domain (in terms of co = 2 7r f). Here, exp(i co t) = cos co t + i sin (co t) is the basis functions that exist for _c~ < t <+ c~. Thus, the FT is ideal for the analysis of stationary signals. When s(t) is short as in most AE signals, we assume a periodicity in the application of the FT. For the analysis of non-stationary or transient signals, the WFT or short-time Fourier transform (STFT) has been used. Gabor [7] originated WFT as an extension to the classical FT. Here, s(t) is windowed by a window function w(t ) . As the window function is shifted in time by changing z" over the whole signal and consecutive overlapped transforms are performed, we can describe the frequency spectrum of the signal as a function of time. This is commonly called the~signal spectrogram. The WFT of s(t) is defined as [WFTs](CO ,t) = f s(t) w ( t - z ) exp(i co t) dt (3) When the window ftfnction is Gaussian, w ( t - z ) = e x p [ - ( t - z )2]/0 2 (4) with a constant a , the WFT is also called Gabor transform. It is possible to describe the WFT as the decomposition of the signal s(t) into the windowed basis functions w ( t - z ) exp(icot). Three examples from a set of the windowed basis functions are given in Fig. (c), which are formed by the product of cosine waves in (a) and the window functions in (b). The length of these basis functions is constant as the shape of the

37 Gaussian window function remains the same. The FTs of the windowed basis functions are shown in (d). These clearly indicate that the frequency resolution remains constant; this also implies that the relative resolution improves at higher frequency.

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38 At Aco ~ 1/2 (5) Both of these factors remain constant in a WFT analysis, resulting in the WFT covering the time-frequency plane with a uniform array of resolution squares. Despite such limitations, the WFT is useful in many problems where time-frequency characteristics are required. The voice-print or WFT analog-equivalent was used by Green et al. [14] in the analysis of AE signals from rocket motor case testing. Instead of the constant window shape of the basis functions of the WFT, the basis functions of the WT are scaled with respect to frequency. Figure 2(c) the fundamental basis function and two scaled basis functions.

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39 In analogy to Fig. 1, these are formed by multiplying cosine (Fig. 2(a)) and window (Fig. 2(b)) functions. Figure 2(d) shows the FT of the scaled wavelets. These indicate that the spectral width broadens at a higher frequency while a better frequency resolution is obtained at a lower frequency. In turn, the relative frequency resolution in WT remains constant; i.e., A f/f = constant. In WT, the amplitude is also scaled (the shorter in time, the higher in amplitude) as shown in Fig. 2(c). These basis functions are short waves with limited duration; thus, the name "wavelet" is used. There are many different wavelets that can be used as the basis functions, although it is adequate for AE signal analysis to use Gabor wavelet or a Gaussian window function applied on exp(i co t). The real and imaginary parts of a Gabor wavelet are shown in Fig. 3(a), whose FT is shown in Fig. 3(b) with the center frequency of 450 kHz in this example. The fundamental basis function is usually referred to as "mother wavelet", while the scaled versions are daughter wavelets.

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Due to the scaling shown in Fig. 2, wavelets at high frequencies are of short duration (good time-resolution) and wavelet at low frequencies are relatively longer in duration (good frequency resolution). This varying "window" structure of the WT is reflected in the resolution on the time- frequency plane by rectangles, as shown in Fig. 4(a). The corresponding partition of the time-frequency plane in the case of WFT is represented by identical squares (Fig. 4(b))

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40 These variable window-length characteristics of WT are obviously suited to the analysis of signals containing short high-frequency components and extended low-frequency components, which are often the case for AE signals. The WT utilizes its basis functions, known as wavelets, and performs a decomposition of the signal s(t) into a weighted set of scaled wavelet functions ~(t). In general, a wavelet ~(t) is a complex valued function. A general wavelet function is defined as Wa,b(t) =Ja1-1/2 [xIz(t- b)/a] (6) The function Xl,r(t) is the mother wavelet with the scale parameter a and the shift parameter b and provides a set of localized functions both in frequency and time. The scale parameter, a, gives the width of window and consequently frequency as the mother wavelet is expanded or compressed in time. This can be understood from the following FT of the mother wavelet ; [FY'l,Pa,b ](o3)= +a,b(OJ) -- la]l/Zxp (am)e -imb (7) This effect of the scale parameter in the frequency domain is illustrated in Fig. 5.

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The effect of the scale parameter in the frequency domain.

The shift parameter, b, determines the position of the window in time and thus defines which part of the signal s(t) is being analyzed. It is common in the WT analysis that the frequency variable CO is replaced by the scale variable a and the time-shift variable z is represented by b. The wavelet transform of s(t) is then defined by: [WTsl(a, b) = f*~,,b (t)s(t)dt

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where ~ * is the complex conjugate of the wavelet function defined by equation (6). In this work, we use the Gabor wavelet based on the Gaussian function, given as equation (4). The mother wavelet and its Fourier transform are given as" (t) = =-1/4

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41 Consider a typical digitized AlE signal of the sampling interval of A ts and the sampling length of N; the corresponding angular Nyquist frequency is 7c/A ts. In order to obtain the wavelet transform of such a signal, we select the center frequency, r p, of the mother wavelet as (2) a/2 :z/A ts. For the frequency range of 10 kHz to 2 MHz to be covered with adequate resolution, we need to use 20 to 40 scale parameters or an with n = 0 to 19 to 39. The scale parameters are selected as an = ol n, with ol =2 1/4. Other suitable values of o~ include 2 VSand 2 v6, which cover wider ranges of frequency with the same value of n. The shift parameters, bm, are chosen as a multiple of the sampling interval of A ts; i.e., bm - m A ts. Here, 0 <_-- m <_--N/2, in order to avoid the WT calculation extending beyond the sampling length. Frequency-defining parameter, n, in the above wavelet transform can be converted to frequency by f-~ = 2 (n+2)/4]/k ts, since the n-th daughter wavelet has the center frequency of o~ p/an. Results of WT of a signal are a collection of wavelet coefficients; each wavelet coefficient for a given set of the scale (or frequency) and shift (or time) parameters, a and b, which can also be represented by n and m. The results are often represented as a contour map on a log(a)-b plane with the gray levels (or false color levels) representing the wavelet coefficients. The contour profile is basically the same as the voiceprint, and shows how the signal intensity of a particular frequency changes with time. Using a logarithmic scale-parameter axis allows a large range of frequency to be simultaneously displayed. Most discussion will be done using the contour map in the following. Shown in Fig. 6 is an example of bird's eye view (b) and contour maps (c) of WT coefficients of the wide-band Lamb waves monitored by a laser surface acoustic wave (LSAW) system for a steel plate of i mm thickness. The contour map is presented in gray-scale diagram. Here, white represents the lowest level wavelet coefficient, and black part the highest with different shades of gray in between. The zero-th order symmetric (So) and anti-symmetric (Ao) mode Lamb waves are separately represented by the WT. It is noted the frequency scale, originally represented by 2"n/4 in our first WT program [15], is represented by a regular scaling in limited frequency range. Due to a higher time-scale resolution of WT, the group velocity dispersion of So-and Ao-mode Lamb waves can be easily determined by the time-of-flight method. Two curves in the contour map represent the group velocity dispersions of So and Ao modes. Velocity dispersion of So-mode in the frequency range below 0.5 MHz is small, as shown by a vertical line. This is an example showing the utility of the WT for modal analysis of dispersive multi-mode waves.

Fig.6

Lamb waveform monitored by a laser interferometer for l m m thick steel plate (a), bird's-eye view (b) and contour map of wavelet coefficients (c)

42 APPLICATION OF THE WAVELET TRANFORM FOR BULK WAVE AE SIGNAL CLASSIFICATION In order to examine the utility of WT for AE signal or fracture mode classification, we applied WT to bulk-mode AE signals from a 10 mm-thick UD-GFRP specimen [10]. The purpose is to classify the AE signals produced by fiber and matrix crack (Mode-I or crack-opening fracture), fiber disbonding (Mode-II or shear type) and debonding (Mode-I). In the signal classification, we examined the correlation or similarity factor gi of wavelet contour maps. Three correlation methods were utilized. These are the classical matched filtering (MF) of contour maps, matched filtering of Laplacian image (MF-LI) and Fourier phase correlation (FPC). In this section, the authors first introduce three classification methods and then compare the discrimination capability of three methods.

Signal Classification Methods 1) Matched filtering (MF) The MF evaluates the similarity between the reference and given images (contour maps of WT coefficients) using full phase and amplitude. The similarity coefficient gi of the measured images f(x,t) to the reference image hi(x,y) is formulated by integrating the product of corresponding values for i=l to M as. gi=f f f (x, y)hi(x, y)dxdy (10) Variables x and y represent the frequency and time for WT AE signals. The similarity coefficient gi can be rewritten by using a vector notation as gi=fthi (11) Here f and h are vectors in the N x N-dimensional space with fik and hik as elements. Both satisfy the following two expression, and a superscript t indicates a transposed vector. llfi II 2= Ilhi II 2=1

(12)

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The gi is then given by equation (14) using the angle 0 between the two vectors. gi=cos 0 i (14) The gi takes a value from zero to one, and becomes unity when 0 is zero, or when a given image completely matches the reference image.

2) Matched filtering of Laplacian Image (MF-LI) The MF-LI utilizes the Laplacian images, expressed by Eq.(15), of WT contour maps. Laplacian images emphasized the edge of the original image and also islands. This is known to be useful in classifying similar images (Marr and Hildreth [16]). V 2f(x,y)= 0 2f(x,y)/0 X2 + 0 2f(x,y)/0 y2 (15) Second order differentiation gives positive and negative peaks around edges.

3) Fourier Phase Correlation (FPC) For the FPC method, the phase information of WT contour maps is used. Here the similarity coefficient gi is given by Eq.(16) using the Fourier phase images f4~(x,y) and hi 4~(x,y).

g~=f f

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The Fourier phase images are obtained by inverse F T (F -I) of the phase terms, 4)F( ~, r/) and 4~Hi( ~f$ 7) ), by e~uations (17) and (18) i(x,y)=F- {exp[-i 45F( ~, r/)]} (17) h~i(x,y) = F-l{exp[-i
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43 (19) ~fr [[2 [[hi~ [1 2=1 The factor gi takes values from-1.0 to +1.0, and gi =1.0 implies the best matching. Classification of 100 AE Events From a UD-GFRP In this section, we introduce the classification result of AE signals from a GFRP with fiber orientation as shown in Fig.7. Six thousand E-glass fibers were spread uniformly on the X-Y plane at the mid-thickness and molded in epoxy. Eight sensors and a ten-channel AE monitoring system were used. Seven sensors (#1 to #7) with 3-mm aperture diameter (PAC: Type-PICO) have mainly been used for the source location and fracture mode analysis based on the radiation pattern of the P-wave. The #8 sensor is a conical-type displacement sensor fabricated in house. It was mounted on the opposite side of the #1 sensor. This sensor was utilized to obtain the fracture dynamics (fracture mode and kinetics) by the P-waveform simulation. Detail for waveform simulation can be found elsewhere [17,18]. For the signal classification, output of #1 resonant-type sensor was used.

, Fig. 7

The specimen configuration and experimental setup for AE signal acquisition for tensile UD-GFRP with side slit

Two separate digitizers (Digitizer A and B) were used because the sampling conditions for the wavelet transform and source simulation and for the source location/radiation pattem analyses were different. The outputs of #1 and #8 sensors were amplified by 40-dB preamplifiers (NF Circuit Block Co. Ltd., Type 9913) and then digitized at 200 ns sampling interval and 8 bit with 4096 sampling points by the digitizer A (Tektronix, RTD 720, H310). This set of data was used for the wavelet transform and source simulation. The outputs of #1 to #8 sensors were amplified by the same amplifiers and digitized at 50 ns interval and 10 bit with 1024 sampling points by the digitizer B (Autonics, APC-510). The outputs of #1 and #8 sensors were recorded on both digitizers. The data on 8 channels of digitizer B was used for the source location and P-wave radiation pattem analyses. This monitoring system enables us to compare the wavelet transform results with the fracture mode deduced by the waveform simulation and radiation pattern analyses. Digitizer A recorded over 2000 AE events. Source simulation was possible for 100 events recorded by digitizer B. Figure 8 (the left) shows the stress-strain curve, and the cumulative AE events vs. strain. We classified the 100 AE signals from the waveform simulation analysis (Fig. 9) into four types. Here, Type-1 is Mode-I fiber fracture, Type-2 and

44 -3 are Mode-I debonding of the fiber-matrix interface in the transverse and thickness directions, Type-4 the Mode-II disbonding.

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Classification with WT were achieved for 86 events since 14 events were not recorded in the second digitizer. The MF method classified the 53 events into 37 as Type-l, 7 as Type-2, 4 as Type-3 and 5 as Type-4. Thirty three events were mis-classified to other types. The MF-LI method classified 55 events, and the event counts of each fracture types are almost identical to those by the ME The FPC method classified 74 events into 53 as Type-l, 10 as Type-2, 6 as Type-3 and 5 as Type-4, with 12 mis-classified events. Percentage of the classified events by the FPC are similar to those by the source simulation. These results indicate that the FPC method is superior to the other two methods.

45

Fig.9 Typical waveforms due to the four fracture types detected by the displacement -sensitive sensor (first row), simulated out-of-plane displacement (second), waveform by #1 resonant sensor (third), their WT contour map (fourth) , Laplacian (fifth) and Fourier phase (bottom) images.

46

Classification of 600 AE Events by the FPC Method From 2000 events recorded by digitizer A, we selected 600 events by picking one event from 3 events in time sequence. Classification was attempted by the FPC method using almost the same reference images as those in the previous section. Results are shown in the right of Fig.8. Six hundred events were classified to 364 Type-l, 151 Type-2, 44 Type-3 and 41 Type-4 events. Comparing with the left figure, we find the general strain dependence of each fracture mode is similar to that revealed by the analysis of 100 events. Since the number of events analyzed by the FPC is larger, the present classification method is useful for AE signal classification. This method was also applied to the signal classification of 1843 events from actual UD-GFRP with 60 mass % fiber content [19]. From the 1843 events monitored using a wide-band sensor (PAC-WD), we selected 627 events by picking one from every 3 events in time sequence as before. Among these, the source location of 375 events could be identified and FPC classification was applied for 192 events whose fracture modes were classified by the source simulation. Figure 10(a) represents the cumulative AE events of the four fracture types for 627 events. Comparing this result with (b) for 192 events, Types-2, -3 and -4 are much more frequently observed. They were also generated from the beginning of loading, almost simultaneously with the generation of Type-1 signals. Debonding (Type-2 and -3) increased exponentially with increasing the load. When combined, Type-2 and -3 events were comparable in the cumulative count to that of Type-1. Mode-II disbonding, (Type-4) events are relatively fewer. Difference between the results of Fig. 8 and Fig. 10 can be attributed to the sensor sensitivities. The wide-band sensor is more sensitive because of its resonant characteristics. It can detect weaker signals, accounting for non-Type-1 signals detected at lower strains.

--!Count ---]

~-1:2811 YPe'2:96 I

2oo

ype-3 : 154] ype-4:96 I

2;150

ota, : 627J

.4~, / " ~'/"

co~ /

/ J

Fig. 10

C~

.7

-1:116 I yp~-3: 11 I

200

ype-2:13 I

,ao./

ype-4 : 52~ 150 >~ = r

==100

loo~

g

O

50

0

/

250 ~

/

/

loo

a

/I~ /I) [

.~C,~,, f

1.0

2.0 Strain, %

3.0

0

50

G

/

J

/

120

> >

30

T~-3

50

0.0

/

0.0

1.0

2. Strain,

%

9

o

b Change of cumulative AE events counts of four fracture types in 60mass % fiber content UD-GFRP. (a) for 627 events and (b) for 192 events.

CLASSIFACTION AND S O U R C E L O C A T I O N OF LAMB WAVE AE SIGNALS BY WAVELET T R A S N F O R M

Fracture Type Classification in Point Loaded Cross-ply C F R P We introduce how the WT is utilized for the classification of Lamb AE signals from various fracture types such as the fiber fracture, matrix cracking (splitting), transverse cracking and delamination in thin CFRP plate [20]. AE signals from a plate are monitored as the complex-mode dispersive Lamb waves. Classification of Lamb AE signals is much more difficult compared to that of bulk mode waves. For obtaining the reference AE images due to these fractures, we simulated four fractures using a point or line-focused pulse YAG laser. Method is shown in the left of Fig.ll. We monitored the Lamb AE signals by a resonant AE sensor (PICO) mounted at 50 mm from the source in the direction normal to surface 0 fiber of 2.4 mm thick (0 6/90 6)sym. CFRP plate

47

Fig.ll

Method for simulating the types of fracture by pulse YAG laser ablation (the left), monitored waveform and wavelet contour maps( the right).

Fiber fracture (Fig.ll(A)) and matrix crack (B) were simulated by an adiabatic thermal expansion of a line-focused laser pulse on the plate in the direction perpendicular and parallel to the frontal 0 fiber, respectively. Delamination was simulated by irradiating a line-focus laser at the interface of layers 2/3 interface (D) at the distal plane. Lamb AE waveform due to transverse crack was simulated by superposing seven point-laser induced AE signals at Z=+0.6 to 0.0 mm in 0.1 mm step, with each signal being delayed the preceding one by 2 /~ s . This superposition is performed for accounting a slow propagation of transverse cracking at 50 m/s. Waveforms and their wavelet contour maps by four fractures are shown in the right of Fig.10. Amplitude profile and duration in WT contour maps clearly show characteristic feature of each fracture type. Using these waveforms and contour maps as the reference patterns, we classified AE signals monitored during a point compression at the center of a clamped cross-ply CFRP plate. One hundred ninety one AE signals, monitored at load of 350 to 430N, were classified into four fracture types (Type-A to D). For this experiment, AE signals were monitored by four sensors on the corners of 100 mm square in a 230 mm square plate. Waveform and WT contour map of Type-A (Data Count, D.C.3) of Fig. 12 resemble those by the fiber fracture (FF) shown in Fig.11, though the frequency components by simulated fracture are higher than those of monitored one, due to fast adiabatic thermal expansion of laser pulse. Signal amplitude by the FF is 50 times larger than those by other fractures. Type-B signal (D.C. 89) has two modes (So and Ao) and a relatively strong Ao component, and resembles the waveform by the matrix crack (MC). It is noted the frequency component of the simulated AE signal coincides that of monitored one, indicating the assumed crack propagation (50 m/s) is reasonable. Type C is attributed to the transverse crack (TC). Type-D AE signal (D.C.54) agrees well with that by the delamination (Del) along the 2/3 interface layer.

48

Fig. 12

Typical waveforms and wavelet contour maps of AE signals monitored during a point compression on a cross-ply CFRP plate

Though we did not use the correlation method for these signals due to large difference in arrival time and frequency component between the reference and monitored signals, we successfully classified the internal fractures in a cross-ply CFRP plate. The sequence of these fractures will be discussed later together with their locations.

Source Location of Lamb AE signals in Locally Loaded CFRP Source location in isotropic thin plates is possible when the first arrival times of the So mode Lamb waves (traveling with the frequency independent sheet velocity) are monitored. However, for anisotropic plates, difficulty arises due to the dispersive nature of the Lamb wave. Strong orientation dependency of the Lamb wave velocities poses another problem. We [21] recent developed a simplified but accurate source location method of Lamb AE signals by utilizing the WT. It uses the orientation dependence of Ao-Lamb group velocities at a selected frequency. Method is briefly shown for UD and cross-ply CFRP. First we experimentally determined the orientation dependence of Ao-Lamb group velocities. The waves, produced by pencil lead break, were detected by two AE sensors (PICO) located at 40 and 90 mm along the angle 0 (measured from the frontal 0 fiber). Figure 13 shows the waveforms and their wavelet coefficients at 65 kHz, in the direction 0 =0", 30 ~ and 90 ~ for UD-CFRE This frequency is chosen to be the major component of the observed signal and to best differentiate the desired wave mode from others. WT is particularly useful in getting arrival time information with a high S/N ratio and sharp peak definition. Group velocities were measured by dividing the inter-transducer distance (50 mm) by the first arrival peak time difference of 65 kHz.

ch//

r

Detected wave

ch.2 ~ !I

100

wT

vs- I

coefficient,

(65kHz)

200

300

0=90 ~ (perpendicular to fiber)

I

400 100

Time, ps

....

200

I Time, ~

300

400 100

ch.1

ch.2

100

Fig.13

r" ,,,.,

......

0=30 ~

0=0 ~ (fiber direction)

49

200

300

400

Time, Us

ch.1

200

_

I o,., /, \

,~ 300

400 100

Time, I~S

200

/o,.'

300

400 100

,/

200

Time, IZS

~,

300

/'~

400

Time, Us

Example of Lamb waves and their 65kHz components in UD-CFRP plate as a function of the propagation direction 0 ~

Orientation dependence of 65 kHz Ao-Lamb group velocities for UD and cross-ply CFRP are shown in Fig.14. Measured data was approximated by a 6-th order polynomial equation (solid line) and submitted to the source location scheme developed. It is noted that the velocity anisotropy of 65 kHz Ao-mode is very small for cross-ply CFRP and can be approximated by a circle.

t 40L

,:00F:

f ' ~ 1 78~176 6 1 7 6 ..-'7~.. t " 'oo~ ..-"~ i ~OoOl ....~.~ \ 9 [

. . . . . .

.~,5,~0".;;,soo ,~coo =~o

Fig.14

[""

o

., ~"

........

Z*

/

/

'=!~ ,o;! ~

t

~

x

500 loco ~:.oo 2oco - i ~ 6 Veloc~i'/,~'s

..~

"~.

..... " 5

'~176

~ .... .

o

s~o

~oo ..-..-,-~

.-~oo

icoo is~o V,liccit'/. m.."s

Orientation dependence of 65 kHz Ao-Lamb group velocities in 2mm thick UD-CFRP (the left) and cross-ply CFRP (the right)

We first determined the source location of ball-drop using the experimental setup shown in Fig.15. AE were monitored by four AE sensors (ch.1 to ch.4) mounted at the comers of a 300

50 mm square on 500 mm square CFRP plates. Ball was dropped on the position #1 to #9. Here, the channel 5 sensor was used for fracture type classification under quasi-static point compression, and will be discussed later.

I

___.~__,_~___,~

......

transducer(P.AC,PICO)

Fig.15

Schematic illustration of source location experiment of ball drop at #1 to #9 on UD-CFRP plate

Shown in Fig. 16 are the examples of Lamb waveforms produced by ball drop at #4 position and their wavelet coefficient at 65 KHz (the bottom). These waves contain only the Ao-Lamb wave, having lower frequency and large amplitude. ch.1

0.1

-O.i

I 0 200 400 s Time,US %" xlO" ~--~" -r" 6.0,.~ arrival / ~ ^I1

0.1

ch.2

-01 600" 0 xlO"s

I 200 400 Time,US

. . ,3.5, arrival//7 i i ~

time

i~

Fig.16

. 0.1

600~

ch.4

0.1

4)1 I ~.0.11 I I 600"0 200 400 600 0 200 400 -s Time, US Time, US xlO xlO"6 / ~. 4.5,i

time

2~o .oo Time,US

ch.3

/

"I 600

' 60[ arrival

ardval time

200 4~o Time,Us

600~

200

400

Time,US

600~

200 400 Time,US

600

Examples of Lamb wave AE signals produced by ball drop on UD-CFRP

Source location method consists of the following steps; 1) Determine the peak arrival times of 65 kHz Ao-Lamb to four sensors. 2) Select a zone from the first arrival. Utilizing the sequence of the arrival times, the source location is confined to a zone; e.g., one of four quadrants for a square-transducer arrangement. 3) Determine the source location by sequentially minimizing the difference of the measured arrival time and arrival time differences computed by moving a virtual source position in the quadrant determined in the previous step. The position was moved sequentially by a preset amount in the X- and Y-direction. When a minimum is found, the preset amount is halved and a minimum is searched again in the immediate neighborhood. This is repeated several times until a certain level of accuracy is achieved.

51 Location results are given in Table 2. The source location for UD-CFRP was determined with the average error of 3.9 mm. In spite of a higher velocity anisotropy of UD-GFRP, the source location can be accurately estimated by the present method. For cross-ply CFRP, it was determined with the average error of 5.6 mm.

Table 2

Source location of ball drop on UD- and cross-ply CFRP plates using 65 KHz Ao -component of Lamb AE signals. UD-CFRP

No.

x(mm)l y(mm) 75 150 225 75 150 225 75 150 225

#1 #2 #3 #4 #5 #6 #7 #8

#9

225 225 225 150 150 150 75 75 75

Cross-ply

....

I m p a c t l o c a t i o n E s t i m a t e d location error

x(mm)

y(mm) (rami

No.

72.7 150.9 227.3 66.5 148.2 228.2 73.2 148.2 227.1

228.8 228.8 224.1 150.6 149.7 150.9 71.8 70.9 73.2

4.4 3.9 2.5

#1 #2

Average error

x(mm) y(mm) 75 150 225 75 150 225 75 150 225

#3

['~

1.8 3.3 3. 7

4.5 2.8

CFRP

I m p a c t location iE s t i m a t e d location

#4 #5 #6 #7 #8

#9

x(mm) 72.7 149.4 227.7 71.5 147.1 234.8 73.2 149.4 229.8

225 225 225 150 150 150 75 75 75

3.9mm

y(mm) 228.2 230.1 229.3 147.7 152.1 148.9 68.5 68.8 71.8

A v e r a g e error

error

(mm) ;3.9 5.1 5.1 4.2 3.6 6. 7

6.2 5.8

5.6ram

Progression of internal fracture in point loaded cross-ply CFRP plate Combining the signal classification and source location methods, progression of internal fracture types in a point-loaded cross-ply CFRP plate was studied for the data shown in Fig. 12. Loading was applied at the center of 100 mm square sensor layout on 185 mm square plate. Waveform was monitored by #5 sensor (Fig.14). As the fracture source was faster than the pencil lead break (1/zs), we used the orientation dependence of 260 kHz component of Lamb waves excited by a compression-wave transducer with a needle contact. Because the waveforms produced were strongly modified by internal fractures, source location was possible only for 46 events of 181 events in all. Progression of four fracture types is shown in Fig. 17.

60

,

SO ~y

1

9

55

/

S~ . . . . . . . . . /

.x

~'*--~

4sF

/

I(1)

40 1 35

, 40

............ #..,. 6o x~s

1 /

i InDelaminati~

I /

i I o Fiber fracture ' I / i I z, Transverse crack I "l

/

, 45

12D4 /

n z~ &~9o--,

I

i I x Matrix crock I XJ [ " , - '"' .... 50 55 60 65

5o . . . . . . . .

'--'~<--.- . . . . . ~*'~• ~, I

40 35

9

(2)

i

, 40

, 45

i i 50

, 55

X 6O

65

40 35

(3)

40

9

|

1t5

13~ nls7 r'l

//

B ......

I

45'

9

[

45

1 xi~'l/

!

,

,

50

55

60

Fig.17 Progression of internal fracture types in a point-loaded cross ply CFRP plate of 185 mm square (loading (compression) position is at X=Y=50 m m . ) Here, the source sequence is shown in three steps from (1) for event count 2 - 52, (2) 70 - 93 and (3) 100 - 165. We find the sequence of internal fracture as follows: 1) fiber fracture ( Q ) in the frontal layer, 2) transverse crack ( A ) in the mid-lamina, 3) matrix crack (X) and 4) delamination (El). Source locations of delamination at a later stage in (3) are found to be close to the outer periphery of double-tree-shaped damage (delamination) detected by the post-test ultrasonic C-scan method.

~l

65

52 M O D A L ANALYSIS AND SOURCE L O C A T I O N OF C Y L I N D E R WAVE Quantitative mode classification of hollow cylinder guided wave (cylinder wave) poses much difficulty due to complex circumferential modes, compared to that of Lamb wave. We [22] utilized the WT for modal analysis and arrival time determination of specific mode wave. The latter made it possible to perform the source location of cylinder wave AE signals detected by a single AE transducer mounted at the pipe end. First application of the WT for a cylinder is the wave mode identification of cylinder waves. We excited cylinder waves by laser ablation on the distal plane and monitored by a wide-band (20 MHz) heterodyne-type laser interferometer and a resonant AE sensor (Fig.18). Figure 19 is an example of cylinder wave monitored by the laser interferometer at z-300 mm for an aluminum pipe of 6 mm outer diameter and 1 mm wall thickness.

Fig. 18 Experimental setup for monitoring the laser excited cylinder wave

Fig.19

Waveform of cylinder wave AE detected by laser interferometer

Theoretical group velocity dispersion curves were overlapped on the WT contour map in Fig.20. In order to study the excitation efficiency of each mode, two contour maps are shown; the left in the amplitude range from 0 (the maximum amplitude) t o - 2 0 dB and the right from 0 t o - 3 0 dB. Here, the vertical axis is represented by logarithmic scale of group velocity to compare the theoretical dispersion curves to measured ones. The first portion of the low amplitude wave (Fig.19) at around 60 /1. s was interpreted as the L(0,1) mode, and the second at around 120 /z s the F(1,1) mode. The group velocity dispersions, represented by dark ridges (higher WT coefficient), agree well with the theoretically predicted velocity dispersions of L(0,1), F(1,1) and L(0,2) and so on. The excitation efficiency, in the short propagation distance, increases in the order from L(0,1), L(0,2), F(1,1), F(1,2), F(2,2) and F(2,2). We observe a large group velocity difference of L(0,1) and F(1,1) modes at a low frequency range, as shown in Fig.21, and can determine the source location of AE signal using the velocities VL of L(0,1) and VF of F(1,1) modes monitored by a single AE sensor. The distance Z of the source from the sensor is given by Eq.(20) Z=VLVF A t/(VL-VF)

(20)

A t is the arrival time difference between L(0,1) and F(1,1) modes at selected frequency (140 kHz for 6 mm diameter aluminum pipe).

53

Fig.20

Superposing of theoretical group velocity dispersions (solid lines) of cylinder waves in wavelet contour map of monitored wave

5000 ~ L

C 0 , 2 ) ~

,~ 4000 ~- i L~(0,1

~3ooo~- ~r 1

~2o0o~ looo ~ ~2,~)/ F(4,1)

o0t,i

F(3J)

I

500

thickness/diameter= 1/5

I

1000

ft (kHz mm)

I

1500

l

2000

Fig. 21 Group velocity dispersion with velocity VL of L(0,1) mode and VF of F(1,1) mode at frequency f

Figure 22 shows the time transient of 140 kHz component of the two modes, extracted by WT. First peak at 63.6 lz s of the interferometer and 63.9 # s of the AE sensor represent the L(0,1) mode, and second peak at around 120 /zs the F(1,1) mode. The WT made it possible to determine the arrival time difference of certain modes of the cylinder waves. The source location is estimated as 297 mm ( 1 % error) and 293 mm (2.2%). Here the theoretical group velocities VL and V~ at 140 kHz were 4860 and 2520 m/s, respectively. Location errors for another pipe diameter and source distance are summarized in Table 3. Average error of all results obtained using AE sensor was 4.9 %. We also estimated the source location in long pipes (1000 to 3000 mm) by using the arrival time difference of L(0,2) and F(1,2). The error was within 2 % for 3000 mm propagation distance.

54

(a) l

~ I-

o.o

0

time (ITS)

Propagation

0

_

~~'40I

(b)

o.o I a.o,

.o4k

/

. ~

-60 ~ 40

80

Fig. 22

~ 0.2

.

.

(a) l

120 160 P r o p a g a t i o n time (ps)

200

0

~~ ' 4 0 -60

40

Errors (%) of source location using the cylinder wave detected by the laser interferometer and resonant type AE sensor AE sensor

Laser interferometer Outer diameter mm 6 mm 5 mm 4 mm (Thickness=lmm)

200

140KHz wavelet coefficient of L(0,1) and F(1,1) mode cylinder waves detected by laser interferometer and AE sensor

Table 3

8

80 120 160 Propagation time ([as)

100

200

300

z (mm) 400 500

-(%)

-

3.3

3.8

7.0

-

10 5.0

4.3 2.5

2.0 1.0 2.3

3.3 0.30 3.8

3.6 0.20 4.0

propagation length

Outer diameter mm 6 mm 5 mm 4 mm 8

(Thickness=l mm)

100 -(%)

200

300

z (mm) 400

500

5.5

6.7

1.5

4.6 9.2

propagation length

-

-

2.0

9.5

-

5.0

1.3

1.8

7.4

6.0

2.2 2.3

7.5

6.0

D I S C R E T E WAVELET T R A N S F O R M Discrete wavelet transform (DWT) is another useful tool, when it is used in combination with inverse DWT (IDWT). It is utilized for denoising of AE signal (signal recovery from noisy signal) and filtering of certain components of AE signals. In the DWT, the mother wavelet sequence is expressed by (x)=2 -j/2 9 (2-ix-k) (21) The variables, j and k, are integers that scale and shift the mother wavelet sequence to generate wavelets. It is noted that the mother wavelet sequence is rescaled or dilated by power of two. We used the Daubechies wavelet sequence[17], which is a compactly-supported orthogonal wavelet and makes the IDWT possible. A family of Daubechies wavelet (db) are written as dbN, where the N is the order and dbl is the same as Harr sequence. We used db4 sequence in our signal processing. Kwon and Joo [23] successfully utilized the DWT/IDWT for the source location of Lamb AE from anisotropic plates. They decomposed the detected AE signals to level 5 and utilized reconstructed filtered waveforms, from which arrival times were determined. The result of source location with WT was superior to cross-correlation assisted threshold crossing method.

55 One drawback is the need to perform both DWT and IDWT to deduce the filtered waveforms. Next, we briefly introduce the denoising of noisy AE signals by the DWT/IDWT. Figure 23(a) is a cylinder AE signal monitored for a pipeline installed in building and contains strong high frequency noise component. Each signal is digitized at 50 ns with 4096 sampling points.

/

t/Epsilonfilter

=

9

,"11 (b) Denoisingby -Level 1 to 3 DWT/IDWT

(d)

Fig. 23

I

L

.

,

Denoisingby

f

Level 1 to 4 DWT/IDWT (256

I

I(e)

I

. I[

[L IL

,,r "

[

Jl

11

Denoisingbybutterworthl towpassf,ter , fL

'~ILDenoisingby

, ,,,,

l I [ Level 1 to 5 DWT/IDWT (128 points) ,_ I III

I~

.

'

,

I I

.

~l I

1

(a) Detected noisy AE signal (cylinder wave). (b) denoising by an epsilon filter; (c) Butterworth low-pass filter (2 MHz cut-off), (d) denoising by level 1 to 3 DWT/IDWT, (e) by level 1 to 4 DWT/IDWT, and (f) by level 1 to 5 DWT/IDWT.

We compared a noisy signal (Fig. 23(a)) with those recovered by advanced filtering methods in the time and frequency domains and also by the DWT/IDWT. Figure 23(b) is the signal filtered by an epsilon filter in the time domain, and (c) by the Butterworth low-pass filtering with 2 MHz cut-off. Signals shown in (d), (e), and (f) are recovered by DWT/IDWT denoising procedures. Here, (d) represents waveform with level 1 to 3 removed after DWT, then performing IDWT, (e) and (f) are waveforms denoized by level 1 to 4 DWT and by level 1 to 5 DWT, respectively. Effective filter is 2.5, 1.25 and 0.625 MHz for (d),(e) and (f). It is noted that the waveforms (d) and (e) resemble that after the Butterworth filtering, keeping small amplitude first arrive L(0,1) mode. This reflects the effective low-pass cut-off of the denoising is 2.5 and 1.25 MHz, respectively. Waveform (f) retains all the recognizable features of the low frequency waveform of the original one. This is obtained without phase delay, which often is found in low-frequency filters. The last waveform also demonstrated the usefulness of DWT for data compression, as the amount of data is reduced substantially to 1 part in 64. This feature is beneficial when a large data file needs to be stored. CONCLUSION Applications of continuous wavelet transform to AE signal analysis are reviewed. As the wavelet transform allows one to map signal power as a function of frequency and time, the resultant contour map in essence shows the change of FFT spectra from the beginning to the end of a signal providing better insight on the nature of the signal. In this paper, we reviewed the following two applications of WT to AE signal application. These are 1) Frequency-time distribution (contour map of wavelet coefficients) is beneficial in identifying the wave mode and signal classification. 2) Time transient of certain frequency, extracted by wavelet transform, enables the source location using Lamb waves (even in anisotropic plates) and cylinder waves. In addition, we utilized the discrete WT(DWT) for AE signal denoising; i.e., reducing noise by frequency-selective filtering via DWT and inverse DWT.

56 Wavelet transform, introduced in the AE field during the late 20-th century, has a great potential in its applications to AE signal analysis and data compression, and is expected to be a powerful tool for the AE researchers and practitioners in the 21-st century. ACKNOWLEDGMENT The authors are grateful to Dr. Yasuhisa Hayashi of Shizuoka University, who supplied us with the original computer program for the wavelet transform. The authors acknowledge the assistance of Mr. Hiroaki Suzuki (presently at Chiyoda Engineering. Corp.) and Dr. Hideo Cho (Tohoku University) and former graduate students in the materials science laboratory at Aoyama Gakuin University: Messrs. Tetsuo Kinjo (Ishikawajima-Harima Heavy Ind. Co.) , Naoya Saito (NTT Communication Ware Co.), Okiharu Tamura (DISCO Corp.) Shingo Ogawa (Nippon Dengen-kaihatsu Co.), Yasuyuki Morikawa (Fijitsu General Co.), Takashi Futatsugi (Ajinomoto Co.), Asako Ishida (Nikon Co.), current graduate students: Fukutoshi Uchida, Hirokazu Yamada and Yoshihiro Mizutani and undergraduate student; Sunao Takashina. REFERENCE CITED [1] Grossmann, A. and Morlet, J. (1984) SIAM J. Math. Anal., 15,723 [2] Grossmann, A., Morlet, J. and Paul T. (1985) J.Math. Physics, 26, 2473 [3] Grossmann, A., Morlet, J. and Paul T. (1986) Am. Inst. Hneri Poincare, 45,293 [4] Daubechies,I. (1988) IEEE Trans.Inform. Theory, 34, 605 [5] Daubechies,I. (1990) IEEE Trans. Inform. Theory, 36,961 [6] Daubechies,I. (1990) IEEE Trans Inform. Theory,, 36, 961 [7] Daubeches, I.(1992) "Ten Lectures on Wavelet, CBMS-NSF Series in Applied Math. 61, SIAM Publ. Philadelphia [8] Mallat, S.G. (1989) IEEE Trans. Pattern Anal. Machine Intel., 31,674 [9] Mallat, S.G. (1989) IEEE Trans. Acoustic. Speech Signal Proc., 37,2091 [10] Chui, C.K. (1992), "Introduction to Wavelet", Academic Press, New York [11] Kaiser, G. (1994) "A Friendly Guide to Wavelets", Birkh/iuser, Boston. [12] Gabor, D. (1982) Geophysics, 47,203 [13] Gabor.D. (1946) J.Inst.Elect. Eng., 93,429 [14] Green, A.T., Lockman, C.S. and Steele, R.K. (1962), Modem Plastics, 41, 137 [15] Kinjo, T., Suzuki, S., Saito, N.,Takemoto, M.,and Ono, K., (1998) J.Acoustic Emission, 15,19 [16] Marr, D., Hidereth, E.(1980) Proc.Roy.Soc. London, Ser.B, B207, 187 [17] Suzuki, S., Takemoto, M. and Ono, K. (1993) J. Acoustic Emission, 11, 117 [18] Suzuki,H., Takemoto, M. and Ono.K. (1996) J. Acoustic Emission, 14, 351 [19] Saito, N.,Takemoto, N., Suzuki, H. and Ono, K.(1998) J. of Acoustic Emission, 16,$289 [20] Mizutani, Y., Nagashima, K., Takemoto, M. and Ono, K.(2000) NDT and E, Int., 33, 101 [21] Yamada, H., Mizutani, Y.,Nishino, H., Takemoto, M.and Ono, K.(1999) 42-nd AEWG Meeting, USA, [22] Nihsino, H., Uchida, E, Takashina, S., Takemoto, M. and Ono, K.(2000) European WG of AE, to be presented [23] Kwon, K. and Joo, Y.(1999) Progress in AE IX, IV-9

57

NEW GOALS FOR ACOUSTIC EMISSION IN MATERIALS RESEARCH Kanji Ono Department of Materials Science and Engineering University of California, Los Angeles, CA 90095-1595 USA

ABSTRACT This article reviews recent progress in methods of signal analysis used in acoustic emission (AE). The achievement and inadequacy in understanding of AE from materials during the deformation, fracture and other processes are evaluated critically. New goals for the 21st century are discussed in conjunction with analytical tools now available. KEYWORDS: signal analysis, simulation analysis, source function, transfer function, deformation, fracture, phase transformation.

INTRODUCTION Acoustic emission (AE) is suited for the investigation of dynamic behavior of materials. During the second half of the 20th century, AE from materials was studied extensively, especially AE from deformation and fracture. Advances have been chronicled, among others, in Journal of Acoustic Emission since 1982 and in biennial International Acoustic Emission Symposium proceedings dating back to 1972. However, most studies were hardly complete for the lack of experimental and theoretical resources at the time these studies were made. This article critically reviews a limited number of subjects from wide ranging materials topics and attempts to suggest new approaches for resolving extant issues. The topics will include signal analysis tools, deformation, fracture, and phase transformation.

SIGNAL ANALYSIS TOOLS Systems theory teaches us that, in the frequency domain, an AE signal consists of the characteristic or transfer functions of the source, propagation medium and transducer (H s, H m and Ht). The AE signal in the frequency domain is expressed as the product of the three;

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The AE signal waveform is obtained by inverse Fourier transform of HAE. When the electronic system has band-limiting characteristics, of course, its transfer function must also be considered in (1). It follows that when we have H mand Ht, which are sufficiently broad, HAE can be deduced via inverse processing or deconvolution analysis. The advances in analytical methods are the major achievement in the last quarter of the 20th century and began with Breckenridge et al. [ 1]. They devised a glass-capillary break source and characterized with a capacitance sensor on a large steel block that represented a half space. Measured displacement response achieved a good match with theoretical H m (also known as Green's function in elastodynamics) for the epicenter and surface wave geometry. Attempts to determine the source function (Ha, displacement or force vs. time function at the source) have flourished in the early 1980's. For selected specimen shapes, restricted crack orientation and strong fracture events, notable success has been reported. For example, groups at Harwell, Cornell and Tokyo [2-4] generally used compact tension geometry for fracture studies, while the Harwell group also devised a special specimen shape combining a cone and cylinder. They have succeeded in estimating the volume and speed of crack expansion in steels and ceramics. NBS also introduced point-contact conical sensors, which extended the frequency limit to several MHz with improved backing [5]. However, the deconvolution approach has attracted limited usage from others in the field and little progress has occurred during the past decade. Three reasons may be cited. First, the inverse (or deconvolution) processing used in these studies is inherently unstable and highly sensitive to noise. Wu [6] showed that attempt to minimize noise effect results in unstable Green's function, as shown in Fig. 1. Difficulties also xlffl0

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59 dimensional finite element calculation [7]. Lastly, calculated Hm is for a point source and point detection, while a typical sensor covers a larger area and phase cancellation effect occurs. With the exception of the conical sensors used at Harwell [5b], this point was hardly given serious attention. It was also not feasible to compare H m with experiment to assess its validity. Ohtsu and Ono [8] developed a generalized theory of AE on the basis of the theory of elastodynamics and dislocation models. AE sources are represented as dislocation motion, including both discontinuities of displacement and traction. AE signals are observed at a stress-free surface, so Green's functions in a half space are obtained as Lamb's solutions. Ohtsu and Ono [9] extended the source representation in the generalized theory by correlating a crack (or glide) to moment-tensor (M) description; that is, the principal vectors and values of a moment tensor denote the crack orientation and motion (displacement and area). For a tensile crack, M consists of three diagonal components, [(~, + 2/a)b, Z.b, Z.b], where b is the crack opening. M for a shear crack (or glide) has two equal off-diagonal terms,/ab. M concept was first introduced in geophysics and Kim and Sachse [ 10] used it in analyzing indentation crack AE. Realizing the limits of inverse processing approach, simulation analysis approach was advocated by Ohtsu and Ono [ 11]. This is also known as a forward problem. Here, a source location is determined with a set of arrival times, then an AE waveform is calculated from specified characteristics of a source and compared with an observed waveform. This step is repeated until a best match between the calculated and observed waveforms is achieved. This study also demonstrated that the amplitude distribution of the initial P-wave arrivals yields the source characteristics and provides the components of a moment tensor. This finding led to the moment tensor analysis. This method has since blossomed as a practical AE tool owing to the tireless efforts of Ohtsu and co-workers [ 12,13]. In reviewing the literature for this article, it was recognized that Kishi et al. [ 14] first used a source simulation method on alumina fracture AE. However, they concentrated on the deconvolution approach in the subsequent studies. Takemoto and Hayashi [ 15] developed the simulation analysis (forward problem) approach using an integrated computer system that includes an optimization procedure to match the simulated and observed waveforms. They found water-hammer pressure by using this method. Takemoto group has continually expanded the simulation tool and incorporated source location procedures and radiation pattern analysis. These are needed to properly apply the simulation analysis. The evolving simulation analysis was utilized in different AE studies, such as obtaining micro-fracture parameters in homogeneous solids and in composites. Suzuki et al. [ 16,17] obtained the location and source characteristics in glass-fiber reinforced plastics, indicating that fiber fracture takes place in 0.2-0.5/as while shear-type disbonds occur over 0.51/as. In this work, the viscoelastic nature and attenuation of the matrix were accounted for in the wave propagation characteristics. Other work examined fracture of carbon-fiber composites and stress corrosion cracking. It is noted that crack orientations in these studies are limited to the coordinate axes and calculated Green's functions adequately represent the observed sources.

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62

Fig. 5 Cylinder source by driving PZT element with an electric pulse, producing displacement equivalent to a laser-excited source. Masaki et al. [20] with an electric pulse can be calibrated and correlated to the source motion. This is also valid in Lamb wave characterization, as shown in Fig. 6 [22]. Interferometric displacement waveforms with different pulse rise times are also shown. These new laser techniques and the simulation analysis procedure allow us to determine the source functions for crack-opening type sources, such as usual fracture and decohesion of nonmetallic inclusions often found in steels and aluminum alloys. The main problem at present is the apparent lower limit in the source rise time of 0.1-0.2/as. This comes from the upper frequency limit of sensors used (typically, NIST-type conical or PAC Pico sensors) and commonly available digitizers. By expanding the frequency range to at least 10-20 MHz, faster AE sources may be characterized. In reality, slower events may be more difficult to cope with, because the amplitude is lower (cf. Fig. 6), wideband sensors have poor response at low frequency and specimen resonance needs to be avoided. Problems of detecting and characterizing shear-type sources still remain. An improvement is needed in extracting information from continuous AE signals, as will be discussed later.

AE FROM DEFORMATION This topic has been central in the study of AE over the years, starting with historic studies of Kishinouye and Kaiser. In their comprehensive review, Carpenter and Heiple [23] summarized the status of understanding of deformation AE in terms of material parameters and how they affect AE. Since then, progress has been slow, as general attention shifted to composite AE. Although instrumentation has advanced through extensive use of digital electronics, the sensor technology remains essentially unchanged. Our efforts have been hampered because AE of plastic deformation results in continuous emission due to the overlap of numerous individual events and each elemental deformation process cannot be separated. For another, shear is the primary mode of plastic deformation and this is more difficult to analyze than the case for crack opening displacement, representing most fracture. Consequently, we may not reach the goal of finding the nature of each dislocation glide from an AE signal any time soon. Here, the direction and plane of glide, size of slip area, speed of dislocations, etc. are the information

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Fig. 8 Integrated rms voltage values of yield AE vs. test temperature for longitudinal (L) steel samples from-150~ to 150~ Also shown are burst event rates for short-transverse samples. Okajima and Ono [27] studies. At higher temperatures, the yield AE reached a peak, followed by a decrease in most materials examined [23,24]. The frequency spectra of AE signals have long been viewed with suspicion, because these most often reflected either sensor or specimen resonance. However, it is clear that Hs can be different for different source mechanisms. With the capability of characterizing H mand Ht independently, it is now possible to attempt to utilize the frequency spectra of AE signals in the study of deformation AE. (As noted earlier, I-Iracannot easily be obtained for a shear source and this may become the next obstacle.) In the past, there have been reports of changing frequency spectra of AE signals with identical test configuration. For example, Ono et al. [28] used a correlation analysis method to show that the deformation of a magnesium alloy in the yield and work-hardening regions produced differing AE spectra. Fleischmann and Rouby [29] reported changes in AE intensity in different frequency ranges as a function of plastic strain (see Fig. 9). However, assessing the physical meaning of such findings was difficult, as Hs could not be separated from HAE and most signals are continuous type. Simulation analysis has amply demonstrated that the early parts of AE signals carry the important information on the source. By identifying if frequency effects come from the rate of individual events or from the characteristics of individual events, we can perhaps begin the new course of investigation. In the new century, spectral examination of continuous AE may open a new avenue of investigation, provided that measuring system is well characterized. Statistical modeling of continuous emissions also needs to be conducted. It is not clear at present how Gaussian random distribution results from the sum of discrete events. In such investigation, essentially all the basic AE studies should be re-examined, including the relationship of yield AE to

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Fig. 9 Changes in AE intensity in different frequency ranges as a function of plastic strain. Fleischmann and Rouby [29] various materials parameters (crystal structure, grain size, solid solution, precipitation, test temperature and dislocation locking). We do know that strong AE arises when slip occurs suddenly or intermittently, whereas slip that continues without interference gives off weak AE. It is not clear, however, why pure AI or solution-treated A1 alloys have dislocation avalanches and give off strong continuous AE. What would limit dislocation sources from operating freely here? It is probably wise to tie this kind of study with on-going materials modeling that is based on the first principles and is exploring how the dislocation sources activate and glide loops expand. Another distinct type of AE observed during deformation is burst emissions. This type originates from micro-cracking, grain-boundary separation, decohesion of non-metallic inclusions, twinning, stress- or strain-induced transformation, etc. The last two are shear-type and a new method needs to be devised. Since the first three are similar to crack-opening displacement, H mcan be obtained by procedures discussed in the preceding section. In fact, early work used the so-called yobell geometry to use a half-space Green's function during tensile tests. Cleavage fracture and inclusion effects were studied [3,30]. Fukaura and Ono [21] examined three ultra-high strength steels under tension. These cold-work tool steels are used for die-forging and require good low-cycle fatigue strength. Strength limitation comes from the presence of primary carbides, which fracture or disbond prematurely. An AE signal from such a carbide crack is shown in Fig. 4c. The transfer function for this geometry and sensor combination is given as Fig. 4a, which resulted from laser excitation and has the main peak at -400 kHz, extending to 1.5 MHz. The convolution of a bell-shaped source function with 1/as

67 rise time (Fig. 4b) with the transfer function produces a simulated waveform (Fig. 4d). For the first two ocsillations, the waveforms (c and d) match closely as shown in Fig. 4e. (The overall frequency range appears to peak at 200 kHz in the AE signal, especially when the entire signal of ,-0.5 ms is used. This is due to the grips.) Thus, the crack opening mode of brittle fracture generated the AE, and cracking process is fast (~1/as). By modifying the composition, the content and average size of primary carbides were reduced and concomitant increase in fatigue strength was achieved. This is due to reduced carbide cracking. In the modified steel, the number of burst emissions was reduced by a factor of 5-30. In fractographs, one also finds indications of slow crack growth around the cracked carbides. AE waveforms may have difficulty discriminating between the carbide cracks and matrix cracks because both are the crack-opening types. However, possible changes in the crack velocity and size can change the rise time and amplitude of AE burst events. A high-sensitivity, high-frequency sensor and instrumentation can perhaps detect smaller crack events. Similar studies involving the grainboundary separation and decohesion of non-metallic inclusions are also needed in characterizing burst AE events and improving materials performance. Historically, Mason, McSkimin and Shockley started instrumented AE experiments on twinning, which has been known as "tin cry" for centuries. However, few modem AE studies on twin have been conducted. Although the twinning has no strong technological implication currently, it is a shear source and its study should reveal information useful in understanding AE from glide and phase transformation. The use of a shear piezoelectric element may provide a means to assess Hm, but characterizing shear-sensing sensors remains to be a major challenge. Recent improvements in AE instrumentation and analysis methods can help us expand the understanding of deformation processes. Besides those discussed above, note the following: 1. There is an opportunity in the use of different frequency bands. By extending to 10 to 20 MHz upper limit and minimizing resonance effects of specimen geometry, one can access the higher end of H s. This can reveal high-speed phenomena during deformation. 2. More uses of shear sensors may provide useful data, although these cannot be characterized as well as usual sensors responding to vertical displacement or velocity. 3. We need to identify various sources of burst emissions in order to improve materials performance and to get at the nature of continuous emission. Statistics will have to be addressed to deconstruct continuous emissions. 4. Material variables as well as experimental conditions need to be understood and controlled. When one or the other is poorly maintained, the merit of a study diminishes beyond salvage.

FRACTURE Most materials and structures fracture with large, audible sounds. AE is detected long before catastrophic fracture and has been utilized in preventing such failures of engineering structures. This application remains to be the main driving force of the technological AE development.

68 Perhaps because this is used widely, few comprehensive review of fracture AE exists. So a brief summary follows. Brittle solids, including ultra-high strength steels and ceramics, generate only small number of AE signals just before final fracture. In the materials with the fracture toughness values of less than ~5 MPa m 1/2, subcritical crack growth is minimal, limiting the AE activities. However, all the micro-fracture mechanisms that are operative in these materials, i.e., cleavage, quasi-cleavage and intergranular fracture, produce high amplitude emissions and can be detected easily. During the subcritical crack growth, AE event counts increase in proportion to some power of stress intensity factor, KI. Nc = A (KI)m

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where Nc is the cumulative number of AE events, A is a constant, m is 4 in case of alumina ceramic [31 ] and this agrees with an old theory of Tetelman [32]. Similar relations between K~ (or other fracture toughness parameters or even crack opening displacement (COD)) and cumulative energy are also found. The cumulative amplitude distribution of AE signals follows a power-law distribution of the form, Nc = B (Vp) -n

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where Vp is the peak amplitude, B and n are constants. For a steel sample exhibiting quasicleavage cracking, the data fits well with the exponent n = 0.5 [33]. When the fracture toughness values increase moderately to 15 to 50 MPa m 1/2, as in high strength A1, Ti alloys and in high strength steels, subcritical crack growth becomes significant and numerous AE signals are produced as a stable crack grows. All the mechanisms of microfracture may be involved. That is, in addition to those mentioned above, low energy tear, alternating shear and micro-void coalescence are observed. In these materials, relation like (2) often exhibits two regions of rising exponent with a so-called knee. An example of this type of plot for 4340 and A533B steels is shown in Fig. 10 [34]. This knee corresponds to microcleavage and is suggested as a critical value for thicker samples. However, the value of K~ at the knee is not a good predictor of K~c. [See also ref. 35 regarding non-correlation of J~at knee and J~c.] In contrast, cumulative energy vs. COD curves for 4340 steels of different temper shows a single linear plot, as shown in Fig. 11 [36]. This represents smaller number of stronger emission for weaker temper and vice versa, but with no obvious AE sources visible on electron fractographs. There are other contradictory observations. Generally, fracture AE data is diverse and conclusions need to be drawn only after careful review of all available information. Low strength, high toughness materials produce weak emissions. Specifically, tear and shear fracture mechanisms generate low level AE signals [33]. The rms voltage rose only slightly above the background level even at general yield of samples exhibiting ductile tear and shear fracture. Amplitude distribution of these AE signals fits the power-law distribution having an exponent of 1.8. Microvoid coalescence processes are normally quiet. The AE associated with dimple or fibrous fracture consists of low amplitude emissions as well. Even with larger

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a knee. Khan et al. [34] samples, AE provides poor indications of fracture criticality in laboratory [37]. However, adequate warning signals are produced in large scale pressure vessels and AE testing remains a valuable practical tool. In conventional fracture/AE tests, some reports noted changes in the relationship of AE event counts and KI due to differences in sensors used. For example, Ono and Ucisik [38] reported such changes in aluminum alloys. This aspect has not been pursued of late and is worth reexamination with the help of the transfer function analysis. Because of limited size of typical fracture specimens, resonance effects must be taken into consideration unless the initial wave arrivals are studied exclusively. The analysis of AE during fracture has assumed, generally with good reasons, that AE is burst type and each event can be treated independently. Accordingly, most work has relied on the relationship of AE event counts and K~ and amplitude distribution analysis or their equivalent using AE counts, AE energy (with practical definition) and others. This may be proven wrong if a recent experiment by Baran and others (to be published) is verified in other materials. This experiment used a medium-carbon (C---0.45%) steel with proeutectoid

70

Fig. 11 Cumulative energy vs. COD curves for 4340 steels of different temper. Carpenter and Pfleiderer [36] ferrite and pearlite microstructure. This was 30-year-old steel and contained numerous MnS inclusions, producing burst-type AE profusely during tensile testing starting at half the yield stress. However, during fracture tests with either three-point bending or with compact tension geometry, continuous AE was observed (see Fig. 12a, taken 70 ms before the main load drop). This is quite a puzzling result. Analyzing such signals with conventional AE testing system, of course, would produce erroneous results. We will have to repeat similar tests using other materials with well-known tensile characteristics. If continuous AE is the predominant mode of fracture AE signals, complete reconsideration becomes imperative. A time-expanded segment of the continuous AE signal is shown in Fig. 12b. When waveform phase discontinuities (marked by short vertical bars in Fig. 12b) are used to punctuate contributing wavelets, the signal is found to comprise of ~20 wavelets, each 6-22/as long. These could correspond to each elemental AE event. Further analysis should be of interest and AE examination of other fracture cases is imperative to clarify this new question. By far, the most detailed analysis of AE from fatigue cracking was conducted by Buttle and Scruby [39]. They used aluminum compact tension specimen, to which 6 point-contact sensors were attached. With the bandwidth extending to 7 MHz, precise location (+_0.1 mm) and radiation pattern analyses were possible. Results match with a micro-crack model in terms of source positions and radiation patterns of the initial P-wave arrivals (although the moment tensors were not specifically computed). See Fig. 13 for three types of waveforms and corresponding frequency spectra. The micro-crack AE (Fig. 13a and b) has stronger high frequency components (>0.5 MHz) in comparison to fretting AE and noise, although the peak amplitude is generally low. Fretting AE (Fig. 13c and d) has high amplitude (by as much as 60 dB) and is observed periodically. Low frequency noise (Fig. 13e and f) was sporadic. Additional studies using this level of sophistication should be continued.

71

Fig. 12 (a) Continuous AE signal during fracture of medium carbon steel (70 ms before fracture) [top]. (b) Time-expanded view of the same signal (between two lines in (a)), showing many wavelets.[bottom] (I. Baran, to be published) When fatigue AE is studied in sheets, Lamb-wave propagation effects need to be considered and tight fatigue cracks tend to produce more variety of fretting and crack-face interference signals [40]. Again, crack advance produced AE with dominant peaks at 300-400 kHz range (using PAC-WD sensors), while other signals were in 100-280 kHz range. In this case, the frequency values themselves reflect sensor resonance, but cracking does induce stronger high frequency content. These findings may correspond to the source rise time of less than 1/~s, as was observed in fiber fracture in composites. Transfer function analysis in fatigue samples should reveal such useful fracture parameters and may render more insight to observed shapes of frequency spectra. Since the frequency spectra can be displayed along with waveforms, this relationship will provide faster path for AE analysis. Detailed study of seemingly quiet processes can yield surprising results. Recent work by Takemoto and co-workers [41] on stress corrosion cracking (SCC) of stainless steel has shown that it undergoes fast cracking under conditions long thought to generate no AE. Since Okada [42] and Yuyama [43], anodic dissolution or active-path corrosion has been considered to be quiet. When sensitized 304 stainless steel samples are exposed to chloride or polythionic acid, AE signals were detected with fast rise times of 0.5 to 1.5/~s. Experiments were conducted using thin sheet samples and Lamb-wave behavior was incorporated in the transfer function analysis. Although the number of events was low, observed AE clearly demonstrates that SCC under the active-path corrosion occurs in fast discontinuous steps.

72

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Fig. 13 Waveforms and corresponding frequency spectra from fatigue cracking. Buttle and Scruby [39] The understanding of fracture processes can expand with improved AE instrumentation and analysis methods. Following steps are recommended: 1. Following the lead of the Harwell group, higher frequency limits, extending to 10 to 20 MHz, allow one to access the higher end of H s. This can reveal even higher speed phenomena in fracture than the minimum rise time of 0.1-43.2/~s obtained at present. 2. Establish a relation between source rise time and frequency spectra, thereby allowing faster estimation of source characteristics. Wave propagation effects need to be incorporated in any such procedures. 3. Re-examine various sources of continuous and burst emissions during fracture to better correlate observed emissions to physical events at the source. If continuous emissions occur widely in fracture, extreme caution is needed in using conventional AE instrumentation. 4. As in deformation studies, material variables and experimental conditions should be controlled carefully in fracture studies.

PHASE TRANSFORMATION By far the first best-known AE study is that of F6rster and Scheil on martensitic transformation in 1936. By demonstrating the presence of fast and slow transformation, it influenced the development of theory of transformation in the following two decades. Subsequent AE studies

73 by Speich [45] and Ono et al. [46] showed that AE can determine sensitively the start of transformation (e.g., Ms temperature) and detect pre-transformation activities. It was also possible to map various microstructural effects on Ms, such as prior plastic deformation, carbide size and distributions. Work by Takashima and Higo [47,48] examined the transformation in Fe-Ni and stainless steel making use of a conical waveguide and Green's function analysis. They estimated transformation velocity and source function. Their recent study [49] suggests the presence of pre-martensitic nuclei, when a sample is cooled to a few degrees above Ms. The nuclei density is high, and this in itself indicates that the observed nuclei are not from segregation or at favorable nucleation sites. Their finding, if confirmed further, would be of extreme importance in extending the theory of transformation. In this area of AE study, it is necessary to examine whether shear sources can be adequately characterized by normal displacement. The radiation pattern analysis is one way to establish a shear source, as was done by Buttle and Scruby [39]. Direct Green's function approach can be used, but no suitable means of experimental verification seems to be available. DISCUSSION Various material/AE studies have relied on conventional AE instrumentation. In the new century, these should be upgraded using source function analysis. As reviewed earlier, the new approach enables the determination of source type (via radiation pattern), source rise time and displacement volume (via simulation analysis). In combination with microscopic or other means, source area or opening distance can be assessed. Or utilize a stressed crack model. The added information should make AE even more valuable tool for materials research than it has been in the past. With casual use of waveform recording instrument, a simpler approach has been practiced in recent years. This emerged from the work of Gorman and Prosser [50], who recognized the critical importance of dominant modes of Lamb wave propagation in thin structures. While detailed Lamb-wave analysis is certainly useful in many cases, even a simple comparison of symmetric and asymmetric Lamb-wave amplitudes often leads to valuable insights to the nature of the active AE source [51]. Dunegan discussed practical applications of this concept [52]. It is also possible to combine the Lamb-wave propagation with source function analysis. In this approach, experimental Green's function can be determined by using line-focused laser beams for a surface crack and by a laser beam or PZT excitation for in-plane cracks. Hydrogeninduced and stress-corrosion cracking has been studied using this approach successfully, but many AE applications with thin shells can benefit as well. The need for improvement in sensor technology is acute, especially for severe environment necessary for some of materials investigation. High temperature and corrosive conditions are commonly encountered, but the advent of nano-technology requires AE sensors to shrink as

74 well in order to realize the unique contribution possible with AE. Perhaps, this will come from laser technology or micro-electro-mechanical systems.

CONCLUSION Issues involving signal analysis, deformation and fracture AE are reviewed with suggested future directions. Source simulation analysis with experimental Green's functions should prove valuable in many new applications. Focused attention is also needed to understand continuous AE and shear sources in deformation and phase transformation. These problems require fresh thought as previous attempts have proven inadequate.

ACKNOWLEDGEMENT The author is grateful for continual discussion with Profs. A. Mal, M. Ohtsu and M. Takemoto and for valuable assistance of Messers. I. Baran, R. Masaki, and M. Mizutani.

REFERENCES

For historic articles, see Drouillard, T.F., (1996), J. Acoust. Emission, 14, 1. * indicates paper that received AEWG Publication Award. 1.* Breckenridge, F.R., Tschiegg, C.E. and Greenspan, M., (1975), J. Acoust. Soc. Am., 57, 626. 2. Wadley, H.N.G. and Scruby, C.B., Shrimpton, G., (1981),Acta Met., 29, 339. 3. Michaels, J.E., Michaels, T.E., and Sachse, W., (1981), Materials Eval., 39, 1032. 4. Kishi, T., (1984), in Progress in Acoustic Emission II, JSNDI, Tokyo, p. 302. 5. a. Proctor, T.M., J. Acoust. Emission, 1, 173. b. Scruby, C.B., (1985), J. Acoust. Emission, 4, 9. 6. Liu, P.-L., Song, T.-H., and Wu, T.T., (1992), in Progress in Acoustic Emission VI, p. 273. 7. Hamstad, M.A., Gary, J. and O'Gallagher, A. (1998), in Progress in Acoustic Emission IX, AEWG and AE Group, Los Angeles, p. IV-48. 8. Ohtsu, M., and Ono, K., (1984), J. Acoust. Emission, 3, 27. 9.* Ohtsu, M., and Ono, K., (1986), J. Acoust. Emission, 5, 124. 10. Kim, K.Y., and Sachse, W., (1984), in Progress in Acoustic Emission II, JSNDI, Tokyo, p. 163. 11. Ohtsu, M., and Ono, K., (1988), NDT International, 21, 143. 12. Shigeishi, M. and Ohtsu, M., (1992), in Progress in Acoustic Emission VI, JSNDI, Tokyo, p. 211. 13. Ohtsu, M., this volume.

75 14. Kishi, T., Wakayama, S., Shinozaki, Y., Kagawa, Y., and Nakata, K., (1984), in Progress in Acoustic Emission II, JSNDI, Tokyo, p. 547. 15. Takemoto, M. and Hayashi, Y., (1988), J. Acoust. Emission, 7, 185. 16. Suzuki,H., Takemoto, M. and Ono, K., (1993), J. Acoust. Emission, 11, 117. 17. Suzuki,H., Takemoto, M. and Ono, K., (1996), J. Acoust. Emission, 14, 69. 18. Breckenridge, F.R., Proctor, T.M., Hsu, N.N., Fick, S.S., and Eitzen, D.G., (1990), in Progress in Acoustic Emission V, JSNDI, Tokyo, p. 20. 19. Scruby, C.B., Dewhurst, R.J., Huchins, D.A., and Palmer, S.B., (1982), in Research Tech. in Nondest. Testing, vol. 5, Academic Press, London, p. 281. 20. Masaki, R., Takemoto' M. and Ono, K., (1999), presented at AEWG Meeting, Princeton, June 1999 (to be published). 21. Fukaura, K. and Ono, K., to be presented at 15th IAES, Sept., 2000. 22. Sato, T., Takemoto, M. and Ono, K., (1999), Jpn J. Appl. Phys., 38, 3193. 23.* a. Heiple, C.R., and Carpenter, S.H., (1987), J. Acoust. Emission, 6, 177. b. Heiple, C.R., and Carpenter, S.H., (1987), J. Acoust. Emission, 6, 215. 24. a. Hsu, S.Y.S., and Ono, K., (1980), in Proc. Fifth Int. AE Symp., JSNDI, Tokyo, p. 283. b. Hsu, S.Y.S., and Ono, K., (1980), in Proc. Fifth Int. AE Symp., JSNDI, Tokyo, p. 294. 25. Hsu, S.Y.S., and Ono, K., Hatano, H., (1979), Mater. Sci. Engr., 38, 187. 26. Armentrout, D.H. and Carpenter, S.H., (1997), J. Acoust. Emission, 15, 43. 27. Okajima, K., and Ono, K., (1980), in Proc. Fifth Int. AE Symp., JSNDI, Tokyo, p. 270. 28. Ono, K., Stem, R., and Long, M., (19q0), in Acoustic Emission, ASTM-505, ASTM, Philadelphia, p. 152. 29. Fleischmann, P. and Rouby, D., (1984), in Progress in Acoustic Emission II, JSNDI, Tokyo, p. 114. 30. Fujita, Y., Higo, Y. and Nunomura, S., (1984), in Progress in Acoustic Emission II, JSNDI, Tokyo, p. 140. 31. Wakayama, S., Kishi, T. and Kohara, S., (1986), in Progress in Acoustic Emission III, JSNDI, Tokyo, pp. 653. 32. Tetelman, A.S. (1972), in Proc. the U.S.-Japan Joint Symposium on Acoustic Emission, English volume, Japan Industrial Planning Assoc., Tokyo, p. 1. 33. Teoh, H.-B. and Ono, K., (1987), J. Acoust. Emission, 6(1), 1 34. Khan, M.A., Shoji, T. and Takahashi, H., (1982), in Progress in Acoustic Emission, JSNDI, Tokyo, p. 531. 35. Yamaoka, S., Kakita, K. and Kishi, T., (1984), in Progress in Acoustic Emission II, JSNDI, Tokyo, p. 16. 36. Carpenter, S.H. and Pfleiderer, C., (1990), in Progress in Acoustic Emission V, JSNDI, Tokyo, p. 492. 37. Nakano, M., Ohshio, Y., Tsukikawa, T., Yamamoto, S. and Ueyama, H., (1980), in Proc. Fifth Int. AE Syrup., JSNDI, Tokyo, p. 510. 38. Ono, K. and Ucisik, H., (1976), Materials Eval., 34, 32. 39. Butfle, D.J. and Scruby, C.B., (1990), J. Acoust. Emission, 9, 243,255. 40. Ono, K. and Wu, J.Y., (1996), in Progress in Acoustic Emission VIII, JSNDI, Tokyo, p. 237.

76 41. Takemoto, M., Tamura, O. and Suzuki, H., (1998), J. Acoust. Emission, 16, S178. 42. Fujimoto, S., Nishino, H. and Takemoto, M., (1999), in Proc. 12th Nat. Conf. on Acoustic Emission, JSNDI, Tokyo, p. 267. 43. a. Okada, H. Yukawa, K. and Tamura, H., (1974), Corrosion, 30, 253. b. Okada, H. Yukawa, K. and Tamura, H., (1976), Corrosion, 32, 201. 44. Yuyama, S., Kishi, T. and Hisamatsu, Y., (1983), J. Acoust. Emission, 2, 71-93. 45. Speich, G.R. and Fisher, R.M.,(1970), in Acoustic Emission, ASTM-505, ASTM, Philadelphia, p. 140. 46. Ono, K., Schlotthauer, T.C., Koppenaal, T.J., (1973) in Proc. ofthe 9th Symposium Nondestructive Evaluation, Southwest Research Inst., San Antonio, TX, p. 386. 47. Takashima, K., Higo, Y. and Nunomura, S., (1984), Phil. Mag., A49, 221. 48. Takashima, K., Moriguchi, M., and Tonda, H., (1990), in Progress in Acoustic Emission V, JSNDI, Tokyo, p. 105. 49. Inamura, T., Shimojo, M., Takashima, K. and Higo, Y., (1999), in Proc. 12th Nat. Conf. on Acoustic Emission, JSNDI, Tokyo, p. 103. 50.* Gorman, M.R. and Prosser, W.H., (1990), J. Acoust. Emission, 9, 283. 51. Prosser, W.H., (1996), J. Acoust. Emission, 14, S 1. 52. Dunegan, H.L., (1997), J. Acoust. Emission, 15, 53.

77

THIRTY YEARS OF ADVANCES AND SOME REMAINING CHALLENGES IN THE APPLICATION OF ACOUSTIC EMISSION TO COMPOSITE MATERIALS M.A. HAMSTAD University of Denver Department of Engineering Denver, CO 80208, USA

ABSTRACT Based upon the author's nearly 30 years of continuous research, development, and application experience in the field, key advances in the application of acoustic emission (AE) to composites over this time period are reviewed. The aspects that are discussed are as follows: sensors, measurement equipment, wave propagation, source characterization, source location, stressing profiles and the characteristic damage states. At appropriate points, this review considers the implications of these advancements for three separate goals of AE monitoring of composites. These AE goals are: (1) nondestructive testing (NDT) to accept or reject composite items; (2) characterization of damage accumulation in particular composites as a function of load, time at load, fatigue or corrosion; and (3) research to develop the understanding of AE and composites (for example, sorting of measured AE signals by AE source type). The currently important challenges for the continued development of AE technology applications for composites are outlined. Specifically source location, source characterization, and wideband tim-with-frequency approaches are examined. KEYWORDS Acoustic emission, composite materials, wave propagation, source location, source characterization, nondestructive evaluation. INTRODUCTION A cursory examination of the number of publications relating to acoustic emission (AE) and fiber composites (long or continuous fibers) shows a dramatic rise at the beginning of the 1970s [ 1]. Prior to that time, the most well known work was a series of publications relating to

78 the application of AE during testing of large glass/epoxy filament wound rocket motor cases [2]. Thus, with only a handful of relevant publications available in 1970, it is extremely pertinent to examine the advances made in the understanding/application of AE to fiber composites based upon the development of the technology over the last 30 years. This examination is the primary goal of this paper. A secondary goal is the discussion of some of the implications of certain advances for the development and application of the technology. And finally, a third goal of this work is a presentation and discussion from a year 2000 view of some of the remaining challenges to bring to maturity the technology of AE as applied to fiber composites. The format of this paper is to present the advances followed by a discussion of implications of some of these advances. The ordering is based on the author's perspective, but is not firm in order of importance since the intended application can dictate a different order of importance. The author's desire was to give original credit to the publication of key advances, but over a 30 year career in the field of AE in composites some sources may have been forgotten or may have been overlooked at the time of preparation of this paper. Hence, the author apologizes in advance for any such unintended oversights. ADVANCES IN UNDERSTANDING AE GENERATION IN FIBER COMPOSITES It is now well known that a large number of AE events is generated during the first stressing of a virgin fiber composite sample. As for most AE generation, the number of events generated increases with increasing stress. For a composite without large stress concentrations, these events have origins that are relatively uniformly distributed throughout the composite. Further, the AE sources can typically begin to operate at stresses as low as approximately 10 percent of the macroscopic failure stress of a well designed composite item. A large fraction of this first stress-cycle AE is due to the fact that locally a fiber composite is always much stronger in one direction due to the fibers dominating the strength and stiffness. Thus, stresses transverse to the local fiber direction can cause local microscopic damage since the matrix material is relatively weak and compliant. This uniformly distributed damage which is created during the virgin stressing of a composite has been called the formation of the "characteristic damage state" [3]. Similar reasoning also applies to the regions between lamina in a composite laminate. Here, since typically there are no fibers perpendicular to the plane of the laminate, stresses acting on the lamina interfaces also result in uniformly distributed micro-damage to add to the characteristic damage state. A second stress cycle shows a dramatic drop in the number of AE signals that are generated. This result is due to the fact that the characteristic damage state has already formed and hence the AE from those sources is largely over. A second fundamental of AE generation in fiber composites is based upon the observation that during a second load cycle there is little AE generated until the stress level begins to approach the peak level of the previous stress cycle. As the second loading approaches the previous peak stress, then the rate of generation of AE begins to increase. This effect is called the Felicity effect and leads to a Felicity ratio. The Felicity ratio is calculated as the ratio of applied load at which this increase in AE generation begins as fraction of the previous cycle maximum load [4]. The Felicity ratio is typically _<1 for AE monitoring (with proper sensitivity) when the hold time at the peak load of the previous cycle is not extremely long. The Felicity ratio is also dependent on the length of time at which the test sample is held at loads (typically at zero load) below the previous load cycle peak value. The existence of the Felicity effect has taken on very significant importance due to the additional observation that, for test samples of sufficient likeness, the value of the Felicity ratio can be correlated with the

79 residual strength of the test sample. The dependence is that the lower the Felicity ratio, the lower the residual strength. A third fundamental of AE generation is based upon the observation that friction or rubbing between damaged portions of a composite can be a significant source of AE in fiber composites [5]. The observation includes the fact that increased levels of damage result in increased levels of AE generation as the test sample is loaded and unloaded. A fourth fundamental of AE generation in fiber composites relates to the fact that catastrophic failure occurs due to a concentration and accumulation of damage in a local region of the composite sample. Thus, the AE associated with this local accumulation of damage has a relatively small region of origin. Since the local damage accumulation changes the elastic properties of the composite in that region, it also results in some stress redistribution with some consequent AE generation in the other parts of the composite item. But, the locally generated damage at the region of eventual failure typically dominates the AE signal generation as compared to any other similar volume of the composite. Further, this related more globally distributed AE tends not to be concentrated in such a localized area, but rather is distributed over the remainder of the composite item. A related and very important observation is that not all local concentrations of AE sources lead to and control the catastrophic failure. This is easily demonstrated by introducing damage to a composite at a local location and then testing the composite to failure. Except for deliberate local impact damage, many other types of induced flaws do not necessarily lead to failure of the composite item. Instead, the composite item fails due to a concentration of damage growth at a different local region. Such deliberate damage (other than from impacting) still results in a concentration of AE sources originating at the induced damage location when the composite is stressed. This aspect can result in confusion about the relation of the generated AE to the residual strength of the composite. A final point relating to the accumulation of damage leading to composite failure concerns the fact that localized damage may be accumulating at more than one local region in the composite. In some cases, the accumulation of AE from multiple local regions can lead to a false interpretation of the AE data if the data from the different regions are not properly segregated and each region's generation is tracked independently. A final fundamental of AE generation in fiber composites is the fact that generation of AE can occur while the composite is at constant load [6]. This source of AE varies in its intensity depending upon the actual materials from which the composite is constructed. Glass fibers are much more sensitive to this stress rupture than graphite fibers. Hence, glass fiber composites experience considerably larger amounts of AE generation at constant load vs. graphite structures. Many fiber composites have polymer matrices. Due to the viscoelastic nature of polymers, there is creep-based generation of AE at fixed loads. As might be expected, the rate of hold or creep-based generation increases as the stress level increases. Thus, fiber composite items that have stress concentrations experience higher levels of AE generation during holds at a fixed load than items without these stress concentrations. These stress concentrations are not just geometric-based concentrations but can include stress concentrations due to damaged or flawed regions of the composite. Unless a composite item is approaching catastrophic failure under the hold-at-fixed-load stress conditions, this hold-based AE decreases with increasing time at the fixed load. But the decay is more rapid for lower stress levels, and is less rapid for a higher stress level during the hold. Thus a damaged composite item still has AE generation even after some time at the hold level, while a similar undamaged item has a significantly lower rate of AE generation at the same load level since it does not have any stress risers.

80 SOME IMPLICATIONS OF ADVANCES IN UNDERSTANDING AE GENERATION IN FIBER COMPOSITES When the goal of the application of AE to a particular composite item is nondestructive evaluation (NDE), there are several important implications of the advances with respect to AE generation. The key implication is that it is not fruitful to attach much significance to the AE generated on the first (virgin) loading cycle. It is much more fruitful to analyze one of the following: a) AE from the second or subsequent cycle (used to compute a Felicity ratio); b) AE occurring during a hold at a fixed load (in particular from glass fiber composites) during the later part of the hold period; and c) AE occurring during unloading (used to compute a Shelby ratio) from friction of damaged regions [7]. These approaches are the most valuable because the AE generated tends to be more dominated by sources originating at stress concentrations and/or where past microdamage has accumulated. Since failure is generally controlled by continued accumulation of damage at one local region (or potentially several such regions), it is important to have adequate source location accuracy. Then the Felicity ratio, Shelby ratio and hold AE values can be locally determined. Thus, the best correlations of the AE measured and the residual strength of the composite can be developed [7]. Since the NDE application requires a database that correlates measured AE with residual strength, it must be recognized that the NDE application cannot generally be applied to unique composite items (however, one could use AE to make qualitative comparisons of damaged portions of the item vs. undamaged portions of the same item). Altematively when the application of AE to a fiber composite has a goal of studying material fabrication variables and/or uniform micro-damage development as a function of stress level or hold time, then the AE generated during the virgin stress cycle is the most relevant to measure. This AE is also the most relevant to measure as a means to check the quality control of the repeatability of a composite fabrication process. Since large amounts of AE are generated in these virgin loading cases, it is usually necessary to use an AE measurement technique that is appropriate for continuous or semi-continuous AE. Further, often only one sensor is required since the AE to be measured is relatively uniformly distributed over the volume of the composite. ADVANCES IN UNDERSTANDING AE WAVE PROPAGATION AND SOURCE DYNAMICS IN FIBER COMPOSITES Wave propagation of AE signals in fiber composite samples has some features that are the same as those in isotropic metals and other features that are specific to composites. To facilitate the present discussion (and other related discussions in the remainder of this paper), wave propagation aspects will be reviewed for plate- or shell-type samples. It is left to the reader to make the appropriate straightforward extensions of the concepts presented here to rod-like samples. A dominant feature of all AE wave propagation is the loss of signal amplitude due to geometric spreading of the AE signal [8]. This phenomenon is more readily observed in composites than in metals. The reason for this is that composite AE sources are both uniformly distributed and more dense (i.e., large numbers of AE sources per unit of volume) than in metals. Thus, it is highly likely that sensors might be located on the composite surface directly above the composite AE sources. This situation is rare in metals unless the experimenter makes a deliberate effort to arrange it. In a plate-like sample, the fall-off with distance (parallel to the plate surface) is proportional to 1/47, where r is the distance from the epicenter. This results in a very large decay in the signal amplitude in the first handful of millimeters of

81 propagation from the epicemer [9]. Many experimenters do not appreciate the magnitude of this fall-off; this author speculates that their lack of appreciation of this fall-off may be due to some combination of the following four reasons. First, they have not arranged the sensors to be relatively close to each other. Thus, when they do have a sensor at the epicenter position for a particular source, the next nearest sensor is far enough away that the corresponding signal is below the electronic noise level at the next nearest sensor. Hence, they are unable to measure the amplitude decay. Second, unless waveforms are recorded and examined in detail, it is very difficult to know when an epicenter signal has been recorded. Third, much of the relatively careful laboratory type AE testing of composites has been done with tab-type tensile samples that are typically 25 mm or less in width. These relatively narrow test samples result in reflections from the edges of the samples that superimpose (perhaps multiple times) on the direct-path signals. Hence, the signals do not decay at the 1/4~" rate, which assumes the signals are from a sample of relatively large transverse dimensions so that early edge reflections are eliminated. If more experimenters used laboratory samples with large transverse dimensions, the geometric spreading effect that is typically present in field testing would be more apparent. In the field, only a very small fraction of the 360-degree "circumference" of energy is intercepted by the AE sensor. Whereas for narrow samples, a much larger fraction (which approaches 180 degrees) can be intercepted by large aperture sensors on narrow samples. Fourth, it is even possible to not observe this effect using pencil lead breaks on a plate sample with large transverse dimensions. For example, if a lead break is done on the bottom of a large plate directly below a sensor, and if the amplitude at this first sensor is compared to the amplitude at a second, similar sensor located a few tens of millimeters away, then the full geometric attenuation may not be observed. The reason for this is due to saturation of the signal in the preamplifier of the first (epicenter) sensor. This preamplifier saturation dramatically lowers the amplitude as measured at that epicenter sensor, which then results in a less-than-true measure of the decay in amplitude at the second sensor. Even if waveforms are recorded, the saturation in the epicenter preamplifier is difficult to observe due to the typical high-pass filter in the channel of the AE recording unit. The only way to observe the true falloff is to directly record the amplitude out of the epicenter sensor and the next nearest sensor, without any adulteration in amplitude or frequency content which might be imparted by a preamplifier. Attaching the outputs of the epicenter sensor and the next nearest sensor directly to a waveform recorder that has a low input capacitance and high input impedance (bypassing any amplification) can effectively accomplish this. Then a true measure of the decay in amplitude due to geometric spreading can be measured by comparing the amplitude at the epicenter sensor and the next nearest sensor. A second dominant feature of wave propagation in a composite sample is due to the typically high material-based attenuation of the higher frequency components of the signal generated by the AE source [10]. Since a large fraction of composites have polymer matrices, which are viscoelastic in nature, this more severe attenuation of high frequency components is much more apparent in composites than in elastic metal plate samples. Both metallic as well as composite plates experience a third cause of signal attenuation. This cause is frequency-based dispersion whereby different frequencies within the signal travel at different velocities. Thus the energy initially created in the AE signal spreads out in time (resulting in longer duration signals) as the propagation distance increases. This effect also results in a loss of signal amplitude as the same amount of energy is distributed over a longer period of time when the signal is sensed by an AE sensor. The propagation velocities in fiber composites not only depend on frequency but they also depend on the orientation of fibers as compared to the direction of propagation in the composite sample. This effect is most extreme in unidirectional composites. In this case, the velocities are highest in the direction of the fibers and lowest in the directions that are

82 perpendicular to the fiber direction. At other angles in the composite, the velocities vary between these limits. If a fiber composite plate is constructed with approximately an equal number of fibers oriented in all directions of the plane of the plate (i.e., a quasi-isotropic plate), then the velocities do not change significantly from one angle of propagation to another within this plane. There is an additional velocity effect that has been observed in thin fiber composite plates as compared to thin metallic plates. For such thin plates (metallic or composite), there are primarily two modes of waves. These modes are the lowest symmetric and lowest antisymmetric modes. In a metallic plate (after propagation of a sufficient distance to fully develop these fundamental modes, typically 10 to 15 plate thicknesses away), the beginning of the flexural mode travels at the bulk shear velocity which is roughly one-half the bulk longitudinal velocity. But in a fiber composite angle-ply laminate plate, the beginning of the flexural mode travels at a velocity that is close to the bulk shear velocity of the pure polymer matrix. This velocity is much less than approximately one-half the bulk longitudinal velocity in a direction that is aligned with a significant fraction of fibers. This phenomenon is evidently the result of the displacements that are present in the plate (to propagate the flexural mode) being transferred primarily by the matrix material rather than the fibers. An additional feature of wave propagation in thicker fiber composite and metallic plates is the development of multiple symmetric and antisymmetric modes that are each characterized by group velocity diagrams (velocity versus frequency). These modes are typically developed after a propagation distance of 10 to 15 times the thickness of the plate. Although the anisotropic elastic properties in fiber composites complicate the analysis, there are enough similarities to propagation in isotropic plates that specific important observations can be made. Due to the character of propagation of these normal modes, there are certain characteristic frequency ranges that propagate with sufficient amplitude to be sensed by the AE sensors. These frequency ranges vary as a function of plate thickness even when the source mechanism is identical. Thus, when observing AE signals in the far-field (i.e., at distances of more than 10 to 15 plate thicknesses), it is not reasonable to directly associate certain AE signal frequency ranges with certain source mechanisms in a way that is independent of plate thickness. Advances in the understanding of source dynamics and source characteristics of fiber composites can be found from some fundamental studies with fiber composites and from extensions from studies with isotropic materials. The first aspect deals with the AE signal dependence on the source rise time. This is not a measured rise time from the arrival time to the peak amplitude of the signal as determined in feature-based AE measurements. Instead it is the time interval over which the dipole-type source releases its elastic energy. Finite element studies with isotropic materials indicate that the rise time has two fundamental effects on the observed AE signal [11 ]. As the rise time decreases, the amplitude of the generated signal goes up exponentially. Further as the rise time decreases, the highest frequency generated by the source increases linearly based on the reciprocal of the source rise time. The opposite occurs in both aspects as the source rise time increases. It has also been noted that the capability to observe the effects of decreasing rise time is limited by the lack of current availability of sensors able to respond with high sensitivity to frequencies above about 1.5 MHz. Even if such sensors became available in the future (i.e., such that the frequencies associated with rise times of less than about 0.67 ~ts could be measured), it is likely that material attenuation due to the typical polymer matrix may limit the ability to correctly characterize the higher frequencies generated by such short rise time sources. Another feature relating to source dynamics is the existence of a radiation pattern [12]. This means that an in-plane dipole source radiates the largest amount of energy in the direction of the line-of-action of the source dipole forces. Thus, in the absence of nearby specimen edges (that result in reflections), the signal measured by a sensor located along this line of action at a

83 certain distance from the source epicenter will be significantly higher than a signal detected by a sensor located the same distance away from the source but at 90 degrees to the source line-ofaction. This result (which has been exactly calculated for an isotropic material) will be complicated by the non-isotropic elastic behavior of a fiber composite; however, the presence of a radiation pattern still must be accounted for when measurements of the AE signals are taken at different angles from the source line-of-action in the plane of the plate. Related to the radiation pattern effect of a simple dipole (with two opposing forces along a single line of action) is a shear type dipole (both with and without a net moment) and other combinations of dipoles that are needed to form more complex sources. These source differences result in changes in the radiation pattern dependent upon the orientation of the source dipoles as well as the mix of fundamental dipoles that form the source. A further aspect of source dynamics has been seen in finite element modeling of sources in isotropic plates. There will certainly be the equivalent concept in fiber composites with appropriate complications due to the non-isotropic material behavior. This aspect relates to the dependence of the relative deposition of the dipole(s) source energy into the symmetric and antisymmetric modes of far-field wave propagation. For example, simple in-plane dipole sources located nearer the top or bottom surfaces of the plate deposit relatively more energy in the antisymmetric modes (for a thin plate in the lowest mode called the flexural mode) than in the symmetric modes [13]. On the other hand, such a source located in the midplane of the plate thickness deposits relatively more energy in the symmetric modes (for a thin plate in the lowest symmetric mode called the extensional mode) than the antisymmetric modes. As a result (since the group velocity curves for the symmetric and antisymmetric modes differ), the relative energy at certain frequencies as measured in the far-field will differ depending upon the depth of the source within the thickness of the plate sample. SOME IMPLICATIONS OF ADVANCES IN UNDERSTANDING AE WAVE PROPAGATION AND SOURCE DYNAMICS IN FIBER COMPOSITES If the goal of the application of AE monitoring of a composite item is NDE, then wave propagation and source dynamics are important factors to consider. The implications of the 1 / ~ geometric signal spreading is of particular importance for the application to larger composite items. Without a sufficiently high "area density" of sensors, there may be significant portions of the composite which are monitored with relatively poor sensitivity. Also this 1/ dependence can result in relatively large signal amplitude drops from the nearest sensor to the next nearest sensor [9]. These changes are not present in the typical laboratory tab-type tensile sample due to the superposition of edge reflections upon the direct signal path. On the other hand the 1 / ~ dependence in larger samples means that the first-hit analysis approach is relatively reliable since signal level falls off rapidly. Thus, with a sufficient "area density" of sensors on such samples, the first hit is not the result of some quirk of wave propagation or even the result of faster velocities of propagation in certain directions. From the point of view of accomplishing source location, the 1 / ~ dependence often results in an insufficient number of recorded hits to be able to attempt more accurate source location calculations. This is due to the fact that, even with a relatively high "area density" of sensors, a large number of AE events only hit one sensor [14]. In addition to the difficulties in obtaining a sufficient number of hits for source location calculations, the experimenter must also be aware of the cumulative effect of the various causes of attenuation. The net result is the signal arrival times (as recorded from the different channels of an array using fixed threshold techniques) are determined using

84 different parts of the signals traveling at different velocities. This occurs in addition to the anisotropic-based directional dependence of velocities. For attempts to use AE monitoring to study different sources of AE in fiber composites, it is necessary to be aware of all the factors that can distort the source information as a result of source dynamic aspects as well as the wave propagation aspects between the source and the sensors. Thus many factors must be accounted for, including material attenuation (of higher frequencies), edge reflections, different modes, radiation patterns and source "depths" as well as other factors. These aspects are discussed in more detail later in this paper.

ADVANCES IN EXPERIMENTAL TECHNIQUES There are two relevant significant advances in experimental techniques that have been made over the last 30 years. The development of the pencil lead break technique to simulate an AE source at a particular location was very significant [15]. Although the lead break source is a monopole source (versus the typical AE source being a dipole-source), it has provided a means to understand certain aspects of the wave propagation in a particular test sample. For example, the experimenter can gain some appreciation of the shapes, durations, and attenuation of AE signals in particular specimen geometry. The lead break technique also allows one to check the repeatability of the response sensitivity of sensor coupling to the AE test specimen. The other development is a related approach (first used in 1975 [ 16]) where the ability to utilize an AE sensor as an ultrasonic pulser is taken advantage of. This development in its simplest application is based upon a modification of AE preamplifiers to allow a pulse (for example, a tone burst of say 10 to 15 cycles at a certain frequency) to be passed from a signal generator directly to individual sensors coupled to the test specimen. By successively pulsing each sensor and recording the signals received (after wave propagation) at nearby sensors, the quality of sensor coupling, sensor sensitivity and wave propagation can be assessed between like specimens or any changes in these factors as a function of time in a single specimen can be measured. This technique is especially useful when sensors/preamplifiers are not easily accessible during a test or when there are extended periods of time between AE monitoring of a single installation of sensors.

ADVANCES IN AE MEASUREMENT TECHNOLOGY AND ASSOCIATED TECHNOLOGIES The field of AE as a whole, like many technologies, has benefited from the advances in the fields of computers and analog-to-digital waveform recorders. The increase in processing speed and the availability of low-cost personal computers has both enhanced the real-time rate at which AE events and hits can be processed and increased the speed and ease of postprocessing of measured AE signals. In the last ten years, the ready availability of wide dynamic-range waveform recorders for AE measurements has greatly increased the amount of data that can be recorded from a single AE hit. Use of this technology by AE researchers has allowed detailed study of AE waveforms, which has in turn spurred the application and the study of wave propagation of various AE signals. The use of waveform records has also provided new understanding of the correctness and lack of correctness of the various signal features that are typically quantified and recorded by feature-based AE measurement systems. In addition, waveform-based AE measurement systems have generated interest in the use of nearly flat-with-frequency wideband AE displacement sensors. The use of such sensors allows the theory of AE source dynamics and wave propagation to be fully associated with

85 experimental results. Thus new signal processing techniques have begun to be developed. Although such wideband applications are more difficult due to the complications of AE wave propagation in anisotropic fiber composites, some techniques have already been developed to enhance the accuracy of experimental source location in thin fiber composite plates. The advances in lower cost and higher performance computer workstations has also stimulated and enabled the use of AE modeling codes. It is now possible to carry out finite element modeling of specific AE sources and the subsequent three-dimensional wave propagation. These calculations can be done for both infinite and finite specimens (where the reflections are calculated as well). Current work in this area has emphasized isotropic materials, but the basic code being used is written to handle anisotropic materials.

IMPLICATIONS OF ADVANCES IN EXPERIMENTAL TECHNIQUES, AE MEASUREMENT TECHNOLOGIES AND ASSOCIATED TECHNOLOGIES Compared to the tools available for AE studies in 1970, significant advances have made more sophisticated and knowledgeable AE monitoring possible. The implications of the advances center on the use of computers and waveform recording equipment. The availability of personal computers with ever increasing speeds and large memories can be combined with high-speed, wide dynamic-range waveform recorders, and programmable signal-processing chips to allow the widest possible bandwidth of AE signals to be recorded and processed. This state of affairs can only enhance the future use of AE in the 21 st century. The same trends for larger computers have made it possible to carry out extensive finite element modeling of the dynamics of AE sources and their subsequent wave propagation. This current work with dedicated workstation type computers can be expected to be carried out on parallelprocessors versions of personal computers in the coming years. This advance should allow experimental waveform results obtained from nearly fiat-with-frequency wideband AE sensors to be directly coupled to modeled results. Such an integration of modeling and experiment can be expected to contribute significantly to the development of AE technology and enhance opportunities for AE in monitoring of smart structures and other applications. As a first step, the current limitations on the speed at which waveform-based AE systems can record hits need to be removed to allow waveform-based AE systems to match the hit recording speed of the less data-intensive feature-based AE systems. ADVANCES AND DIFFICULTIES IN CHARACTERIZATION/IDENTIFICATION OF AE SOURCES IN FIBER COMPOSITES Many experimenters have attempted to develop a means to identify the source type of particular AE signals obtained while monitoring fiber composite specimens. There are two extremely different approaches to developing various levels of source identification using AE signals. One technique is an approach that couples measured features of AE signals with "supplementary information". Such "supplementary information" can include various conditions of specimen geometry, applied stress field, fiber arrangements, and microstructural observations of the test specimen. In this fashion, certain characteristics of AE signals are then determined to be useful to identify sources of measured AE signals. Examples of this approach can be found in the literature [ 17]. An alternate approach is to use the mathematical theory of AE source dynamics combined with appropriate wave propagation theory to determine source identity/characteristics [ 18]. This alternate technique as discussed below can yield the most information about an AE source and is the method least likely to yield questionable results.

86 Neither approach can currently give what a person interested in applying AE to non-specialized composite items would find most useful. What would be most useful would be the ability to identify/characterize AE composite sources based solely on the content of AE signals measured in any composite item of interest with sensors in the far field. Ideally the technique would be successful and accurate in spite of changes in sample geometry, sample thickness, sample materials, sensor types, and sensor locations. The second technique mentioned above could (after sufficient trial and error modeling) lead to the desired result. The reason for this is that if all the source information about a source were known then in theory a finite element calculation could be made for this source in a particular composite item with certain sensors located at various points. By varying the source position in three dimensions, eventually modeled signals could be determined to match the measured signals. In the paragraphs below, both approaches are separately considered in more detail. The first approach suffers from several difficulties. The signals recorded from the sensors will incorporate the significant changes in the signal resulting from wave propagation. These changes distort the fundamental source characteristics. Thus, the measured AE signal characteristics are unlikely to apply to a new specimen with different geometry when the necessary "supplementary information" is not available. A further weakness of the first approach is that the specific location of a particular event is not known due to source location accuracy difficulties to be discussed below. Thus the modifications in the recorded signal due to wave propagation (as compared to the original source signal before propagation) cannot even be estimated. For a "plate-like" sample, the location of the source though the thickness also has a significant impact on the distribution of energy within the symmetric and antisymmetric modes of wave propagation. This depth of the source has a significant effect on signal amplitude, which is a signal feature that many have tried to associate with particular source types. In addition, the wave propagation causes changes in frequency content (due to material attenuation), and reflections from sample edges result in mode conversions with likely additional changes in frequency content along with changes in amplitudes due to superpositi0n of reflected signals upon direct arrival signals. Thus, at best, such approaches might determine some AE signal features that are valid only for the following conditions: 1) specimen geometry is fixed, 2) sensor types and locations are fixed, and 3) all sources (of different types) are located at the same point in three-dimensional space in the specimen. Thus, in the author's opinion, this approach is not likely to yield what is really desired as described above. The only case in AE testing of fiber composites where as a general rule source identification by only measured AE signal features is likely to be valid is a delamination source. In this case (since the likely source is made up of a series of point sources sufficiently closely spaced in time that they all contribute to the same AE signal), the use of long signal duration as the key signal feature may be a valid approach to identify such sources. But again, due to wave propagation effects that increase the duration of signals from single point sources (e.g., propagation distance, edge reflections, and alternate signal paths), the decision about what duration qualifies as a delamination signal is not always straightforward. It is also possible that friction-based AE sources in fiber composites might be in this same category. Such friction-based AE generated by relative movement between two parts of damaged regions of a composite could also lead to similar longer duration signals. The approach at the other extreme requires the theory of point AE sources and the associated wave propagation theory. A point AE source can be characterized in its most simple and complete form by specifying four things: first, the magnitude of three orthogonal dipoles (i.e., the principal values of the moment tensor); second, the source time history of the three dipoles (lacking any evidence to the contrary it is commonly assumed that the time history is the same for each dipole); third, the orientation of the axis of the principal dipoles relative to the specimen geometry; and fourth, the location in three dimensions of the point source within

87 the specimen. The basic difficulty is that, in order to determine these four things, the AE signals from a particular source event must be measured at several locations. Then, to determine a particular source, the measured signals must be used to either calculate (via an inversion technique) the four things described above [ 19], or the signals must be shown to be the same as for an analytical calculation (forward problem) for a particular source (specified dipoles, time history and orientation) at a certain location. Both of these approaches have been accomplished in isotropic materials where the specimen geometry and sensor locations are such that direct longitudinal bulk waves from the source reach the several sensors in a manner that these first arrivals are not "contaminated" by reflections from the specimen boundaries. Since only bulk longitudinal waves are typically involved in the calculations, they are not extremely difficult to carry out for isotropic materials. As far as this author is aware, the more general case when Lamb waves (rather then direct longitudinal bulk waves) are measured has not been accomplished. For fiber composite materials, some progress has been made in developing the altemative approach. In some cases, a partial source characterization has been done [20]. Experimenters used a sample with a limited number of fibers and made the assumption that the bulk wave propagation could be considered to be through the isotropic matrix material without consideration of the effects of the fibers on the wave propagation. Then, using a test specimen with geometry such that reflections did not distort the initial bulk longitudinal waves, the radiation pattem was used to determine the dipole representation that was present for some different composite sources. In another case with a more representative fiber volume, more detailed characterization of composite AE sources has been carried out [18]. In this case, a unidirectional sample was used. The angular dependence of the bulk longitudinal velocity (pwave) was determined. Then multiple nearby sensors were used to develop data to calculate the source location of the recorded events. In addition these multiple sensors were used to determine the radiation pattem that was present from each event. Then using an iterative procedure (semi-inverse method), the complete source parameters were adjusted in an analytical calculation until the calculated waves matched those empirically measured at a displacement sensor located near the source position. In addition, a resonant sensor with a fairly broad frequency band of sensitivity was used to obtain certain characteristics of a 400 ~ts portion of the signal recorded with this sensor. The spectra and wavelet related signal characteristics were in this fashion more directly related to particular composite sources. Based on our understanding of wave propagation and source dynamics, it is to be expected that these resonant sensor determined characteristics would not apply directly to the same source mechanisms operating in different geometry specimens with the more representative fiber orientations present in typical composite angle-ply laminates. Thus considerable work is still to be done. But if full source characteristics could be developed for the different sources present in fiber composites, then as mentioned before finite element modeling could be used to determine the signals that would be present in new specimen geometries. To more accurately use such results, a sensor calibration technique needs to be developed for displacement sensors mounted on lower modulus materials (as discussed in more detail later in this paper). Also it should be noted that the time histories (of the source dipoles) based on experimental measurements need to be considered as approximations since material attenuation of the viscoelastic polymer will attenuate the higher frequencies.

ADVANCES/PROBLEMS IN SOURCE LOCATION IN FIBER COMPOSITES Some progress has been made in the development of "accurate" means of source location in composites. Use of the term "accurate" here refers to approaches meant to provide

88 more accurate results than locations based on a first-hit analysis, which only locates a source within a region surrounding each sensor such that the sensor is the nearest one to any AE sources originating in that region. In a unidirectional fiber composite, this first hit region can have some non-symmetry due to the non-uniformity of propagation velocities in different directions. In this author's opinion, most of the progress that has been made towards the improvement of source location accuracy has been related to developing a better understanding of the difficulties which must be overcome if better accuracy is to be obtained. The earliest factors which were understood were the variations of velocities with directions of propagation to the different sensors in an array, and the fact that fixed threshold measurements do not record signal arrivals which have traveled with the same velocity even when the propagation direction is constant. The latter problem is due to the significant attenuation (already described in this paper) of the AE signals. A further related problem that stems from fixed threshold arrival times is the observation that two identical sources occurring at exactly the same location in three dimensions will have considerably different sets of arrival times at the sensor array even if the sources have all their characteristics the same except for different original source amplitudes. Recognition of these problems has led to attempts to improve the situation. One approach has been to develop software to account for whether or not the fixed AE threshold was first penetrated by the extensional or flexural parts of the AE signal [21 ]. Since in a composite these different parts of the signal have substantially different velocities, some potential for improvement exists. Another approach has been developed for thin anisotropic fiber composite plates of uniform thickness [22]. In this case, group velocity curves for the plate are generated for the dispersive flexural mode. These curves can either be generated experimentally or analytically (if the required elastic constants are available). These calculated group velocity curves are developed for the different directions of propagation as well. Then signal-processing techniques are applied to calculate the source location in an iterative fashion using the arrival times and velocities appropriate to a selected frequency of the flexural mode group velocity curves for the different propagation directions. Between these two more sophisticated approaches, the latter offers the most potential for improvement; but it also needs enhancement to account for known aspects of the signals, which are generated in composites. The first aspect is the fact that the deposition of energy into the flexural mode varies as a function of the depth of the dipole source within the plate thickness. Thus signal-to-noise issues may preclude use of the technique when the source is located near the midplane of the composite plate. In addition, for different combinations of dipoles that make up a particular source, the amount of excitation of the different modes varies. Thus as yet the technique has not been shown to be robust with respect to these differences. To date the primary validation has been with surface pencil lead breaks which generate the most energy in the flexural mode and do not have the complication of depth of source changes and /or composition of source dipoles changing. Another point needs to be made with respect to experimental validation of source location techniques in fiber composites. In three ways, the pencil lead break source does not really represent typical AE sources, which are excited upon stressing a fiber composite or metallic sample. The amplitude of this simulated source is much larger than most real sources. Thus in small samples, typically the signal arrival time determined for all the sensors is for the first part of the extensional mode. This is not the case for real AE signals. Second, as mentioned above, the lack of variation in depth of the lead break source is not representative of real signals. Third, the large deposition of energy into the flexural mode by this out-of-plane surface monopole lead break source is not representative of the actual AE signals generated in

89 fiber composites. These real sources are dipoles and likely are in-plane sources that are not all on the surface of a composite plate. For the future, the development of more sophisticated and accurate source location techniques is a priority since as already discussed failure of a fiber composite item results from accumulation of damage at a particular location. Thus techniques need to be developed for thicker composite plates where modes in addition to the two fundamental modes are present. These techniques need to be validated with in-plane sources that are not only on the surface of the plates, as occurs in lead break sources. And ideally these in-plane sources should be dipoles. Thus it may be necessary to use the finite element modeling approach to develop and validate such techniques. Also, for typical laboratory type specimens, source location techniques need to be developed that are robust in the presence of early reflections from the edges of the samples. This advancement would be key for researchers who want to use AE to develop and validate damage-mechanics theories for fiber composites using tab-type tensile samples. Also, since many real composite structures have variations in plate or shell thickness that progressively occur, techniques need to be developed for accurate source location in the presence of thickness variations. It may be necessary to develop an appropriate in-plane simulated source that can vary the deposition of energy between symmetric and anti-symmetric modes to create the necessary database. Possibly a robot-source generator located by some type of global-positioning system could be used to develop an experimental reference database. Then, using some sophisticated artificial intelligence signal processing code, real AE data could be analyzed to determine accurate locations of sources.

FUTURE NEEDS FOR AE SENSORS FOR FIBER COMPOSITES To date there has not been much effort to develop AE sensors specifically to monitor fiber composites. This is probably due to the fact that the AE signals generated in fiber composites are typically of higher amplitude than those generated in metals. The promise of possible advantages of using wideband sensors to monitor the AE in fiber composites has lead to interest in improving the sensitivity of such sensors, since generally commercially produced wideband sensors have relatively low sensitivity as compared to commercial resonant sensors. Since most fiber composites have polymer matrices and the fibers are in-plane rather than in the out-of-plane direction, the resulting composite plate has relatively low stiffness in the outof-plane direction. This modulus approaches that of a typical polymer, which is some 20 to 60 times lower than most metals of structural significance. Because of this low stiffness, studies to date have shown that out-of-plane wideband AE displacement sensors do not record the full displacements of the real out-of-plane displacements in the plate [23]. The reason for this is that current sensors have a sensitive element that is considerably stiffer than the specimen material it is in contact with. Finite element modeling has shown for a polymer plate that the signal recorded by a conical sensor is well below that of the actual displacement. To attack this problem, it would be desirable to develop an absolute calibration standard for sensors mounted on low modulus materials similar to what has been developed for wideband sensors used on steel specimens. With such a wideband calibration approach in place, the response sensitivity of various sensor/preamplifier designs could be determined. Coupling these measurements with electronic noise measurements of the sensor/preamplifier designs could lead to determinations of signal-to-noise sensitivity of new alternative designs. At first thought, an optical approach might seem to be an ideal candidate. But examination of signal-to-noise sensitivity of such sensors has indicated on metal samples that such wideband sensors are (over the frequency range of interest) less sensitive by two orders of

90 magnitude or more than conical-type sensors [24]. Thus, if they were used on a low modulus material, they still would be less sensitive than current conical sensors which on polymers (when using their metal specimen calibration constant) measure displacements that are about one order of magnitude less than the expected actual values. Attempts to use in-plane or out-ofplane piezoelectric polymer (e.g., PVDF) sensor designs have not been able to surpass the sensitivity of the ceramic conical element sensor [25]. The reason seems to be directly related to the low capacitance of the PVDF sensitive element when it is made with the required small aperture. The dielectric constant of the PVDF polymer material is much less than that for PZT ceramic material. For monitoring of large composite structures, the need for more sensitive sensors of either resonant or wideband type becomes an economic issue. Fewer AE channels to cover the same area means less cost for both equipment and labor. Due to the high rates of attenuation in fiber composites, it is important to have access to higher signal-to-noise-sensors. Such sensors would also help to overcome the associated large fall-off due to geometric spreading that causes a high percentage of events to be single-hit events even in the presence of a rather high sensor density. Since it is not possible to make location calculations with only a single sensor being hit, it is highly desirable to develop sensors with increased sensitivity for composites.

CONCLUSIONS A review of the progress in AE applications to composites over the last 30 years has pointed out key advances and their implications. Also the key areas where future work is still needed have been identified. These areas include: 9 Source location accuracy 9 Source identification 9 Recording speed ofwaveform recording systems 9 Increased signal-to-noise sensors, both wideband and resonant types Since AE monitoring provides a measure of the direct damage response due to stressing a composite item, the technique has a key role to play in the 21 st century. This will especially be true until the theory of damage mechanics in fiber composites has advanced to the same point of maturity where the theories of fracture mechanics and crack growth have already advanced today in metallic and ceramic materials. REFERENCES 1. Drouillard, Thomas F. and Hamstad, Marvin A. (1983). In: First International Symposium on Acoustic Emission From Reinforced Plastics, pp. 1-59 (Session 6, 10:25-10:50), The Society of the Plastics Industry, NY, NY. 2. Green, A.T., Lockman, C.S. and Steele, R.K. (1964) MOPLA 41, 137. 3. Reifsnider, K.L. and Masters, J.E. (1978). 78-WA/Aero 4, American Society of Mechanical Engineers, NY, NY. 4. Fowler, T.J. (1977). Preprint 3092, American Society of Civil Engineers, NY, NY. 5. Awerbuch, J. and Ghaffari, S. (1986). In: Progress in Acoustic Emission III, pp. 638-652, Yammaguchi, K., Aoki, K. and Kishi, T. (Eds). The Japanese Society for Non-Destructive Inspection, Tokyo, Japan. 6. Hamstad, M.A. and Chiao, T.T (1975). In: Composite Reliability, pp. 191-201, ASTM STP 580, American Society of Testing Materials, Phil., PA. 7. Downs, K.S. and Hamstad, M.A. (1998) J. of Composite Materials 32, 258.

91 8. Downs, Karyn S. and Hamstad, Marvin A. (1995). In: AECM-5 Fifth International Symposium on Acoustic Emission From Composite Material, pp. 349-358, The American Society for Nondestructive Testing, Inc., Columbus, OH. 9. Downs, Karyn S. and Hamstad, Marvin A. (1996) J. of Acoustic Emission 14, $61. 10. Pollock, A.A. and Cook, W.J. (1976). Technical Report DE 76-10, Dunegan/Enderco, San Juan Capistrano, CA. 11. Hamstad, M.A., Gary, J. and O'Gallagher, A. (1998), J. of Acoustic Emission 16, $251. 12. Scruby, C.B. (1985), In: Research Techniques in Nondestructive Evaluation VIII, pp. 141210, Sharpe, R.S (Ed), Academic, London. 13. Hamstad, M.A., Gary, J. and O'Gallagher, A. (1996) J. of Acoustic Emission 14, 103. 14. Downs, K.S. and Hamstad, M.A. (1995) J. of Acoustic Emission 13, 56. 15. Hsu, N.N., Simmons, J.A. and Handy, S.C. (1977), Materials Evaluation, 35, 100. 16. Green, A.T. (2000) Personal communication to M.A. Hamstad about Acoustic Emission Technology Corporation Model RTM 4900. 17. Mehan, R.L. and Mullin, J.V. (1971) J. of Composite Materials 5, 266. 18. Saito, Naoya, Takemoto, Mikio, Suzuki, Hiroaki and Ono, Kanji (1998) J. of Acoustic Emission 16, $289. 19. Ohira, T. and Pao, Y-H. (1987), In: Solid Mechanics Research for Quantitative Nondestructive Evaluation, pp. 411-423, Achenbach, J.D. and Rajapakse, Y. (Eds), Martinus Nijhoff Publishers, Boston. 20. Enoki, Manabu and Kishi, Teruo (1991), In: Review of Progress in Quantitative Nondestructive Evaluation, Vol lOB, pp. 1499-1506, Thompson, D.O and Chimenti, D.E (Eds). Plenum Press, New York. 21. Ge, Maochen (1997) Paper presented at the Acoustic Emission Working Group 40th Meeting, Chicago. 22. Ziola, S.M. (1991). Ph.D. Thesis, Navel Postgraduate School, Monterey, CA. 23. Hamstad, M.A. (1995), In: AECM-5 Fifth International Symposium on Acoustic Emission From Composite Materials, pp. 111-119, The American Society for Nondestructive Testing, Inc., Columbus, OH. 24. Boltz, E.S, Fortunko, C.M., Hamstad, M.A. and Renken, M. C.(1995). In: Review of Progress in Quantitative Nondestructie Evaluation Vol. 14, pp. 967-974, Thompson, D.O. and Chimenti, D.E, (Eds). Plenum Press, New York. 25. Hamstad, M.A. (1997) Wood and Fiber Science 29, 239.

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93

ACOUSTIC EMISSION FOR THE DETECTION OF FATIGUE DAMAGE

O.-Y. KWON and K. LEE

Department of Mechanical Engineering, lnha University lnchon 402-751, Republic of Korea

ABSTRACT Acoustic emission (AE) has certain advantage to detect the onset of fatigue damage with its high sensitivity, real-time monitoring and remote-sensing capability. Although AE monitoring of fatigue has been mostly focused on fatigue crack growth, the detection of fatigue crack initiation becomes more important than ever. Spatial discrimination based on AE source location is a must in the modem computerized AE instruments, which appears to be quite successful for the elimination of noise originating outside of the region of interest during fatigue crack propagation. AE measurement during the crack initiation stage has been known very difficult because the signal-to-noise ratio (SNR) is very low. In addition to the improvement of AE instrumentation, a new version of digital filtering technique to separate AE signals due to fatigue crack initiation from background noise is necessary. Wavelet transform de-noising is a potential candidate for such a purpose, which provides a significant improvement of SNR to the inherently noisy fatigue signals. The basic treatment of the technique is introduced and some results obtained from the onset of fatigue damage are summarized. KEYWORDS Fatigue damage, crack initiation, short fatigue cracks, wavelet de-noising INTRODUCTION It has been known for decades that a great portion of total fatigue life is actually consumed by the crack initiation process unless there has been any pre-existing crack. Davidson [ 1] suggested that the total fatigue life, Nfcan be the sum of the number of cycles to initiate short cracks (Ni) and the number of cycles for short cracks (N~) to grow into long cracks and the number of cycles for the long crack growth (N~c) until fracture. Although the exact number or fraction of the period depends upon the microstructure of materials, the applied stress and other environmental conditions, it has been known that short cracks are initiated as early as at 10% of the total fatigue life. During the rest of the fatigue life, any short crack grows and coalesces with other short cracks to be a long crack which continues to grow up to the critical size to eventually cause the catastrophic failure. Although the early stage of fatigue damage is a process governed by microscopic features

94 such as dislocations, slip band, extrusion and intrusion, grain boundary, and so on, fatigue damage at a detectable level can be manifested by microcracks, or in general, short fatigue cracks. As a rule of thumb, short cracks are in the size range from several micrometers to half a millimeter, which is about the lower bound of the detectability of conventional nondestructive testing. According to the ASTM Standards [2], cracks can be classified as long cracks and short cracks. Long cracks are generally visible with naked eye or the aid of magnifiers, and routinely measured to be utilized as the basic data for fracture mechanics whereas short cracks are in the size range impossible or difficult to be measured by optical means during fatigue loading. Being defined more precisely in terms of continuum mechanics, short cracks are those shorter than the dimension of plastic zone or plastic strain field. They are again divided into two groups, mechanically short cracks and microstructurally short cracks. The latter are short cracks smaller than the characteristic dimension of microstructure such as the grain size. Cracks longer than both of these short cracks but still the length of which less than a millimeter are often called as physically short cracks, merely differentiating from chemically short cracks. Related studies [3-9] showed that the growth rate of short cracks could be significantly different from of long cracks. The linear elastic fracture mechanics (LEFM) is no longer applicable for small cracks. Current approach to the fatigue crack propagation based on LEFM can provide a fairly accurate estimation of fatigue life for many materials and structures when the initial crack size is long enough. In practice, however, there are many components such as turbine discs or airframe structures critical to fatigue cracks shorter than the dimension employed in typical fatigue experiments. The direct application of such experimental data to the design of flaw-critical components can, therefore, lead to nonconservative estimation of fatigue lives.

,,

",

ao =-~-

faO'e

-%% ~ . -,,

! -.,,.

",i log(aft)

\

",,, [ \ %%%% N =

a~

ao

a2

log a Fig. 1 A schematic presentation of the threshold stress-crack length diagram with three critical crack lengths.

95 It was Pearson [ 10] who reported the first observation of the accelerated growth of short fatigue crack in an aluminum alloy. Numerous investigations including those mentioned above have been carried out since then, many of which can be found in the proceedings [11-14] of the topical conferences on short or small fatigue cracks. The anomalous behavior of short cracks has not been fully understood, however. The critical crack size, ao below which short-crack effects become potentially significant is still determined by the diagram as shown in Fig. 1 originally proposed by Kitagawa [9] more than twenty years ago. Besides, it is generally impossible to detect the crack initiation during fatigue loading by conventional measurements and thus to accurately determine the effective threshold stress intensity, A Kth,eff. The values of crack size, a~ and a 2 are very much dependent on microstructure and therefore vary from material to material. Cracks shorter than a2 can be short cracks and those shorter than a~ can be regarded as the microstructurally short cracks. Various measurement techniques [2,14], either destructive or nondestructive, have been devised to measure the short cracks. To name a few, photomicroscopy [15], replication [16], potential difference [17], ultrasonics [18], laser interferometry [19], scanning electron microscopy [20], and constant Kmx-decreasing A K method [21 ] are those widely employed. There are some other methods to detect the onset of fatigue damage [22] including the stress amplitude drop, the variation of elastic modulus, density, and microhardness, and acoustic emission. AE MONITOR/NG OF FATIGUE DAMAGE The early detection of fatigue damage can be achieved by the detection of short crack initiation and propagation. It is highly desirable to perform such a task in real-time and without interfering with the normal function of structures under fatigue loading. A series of nondestructive evaluation (NDE) techniques specialized for the detection of short fatigue cracks has been developed. Readers may also refer to a few review articles [18,23,24] on this subject. Since the interaction between the microstructural features and the short crack appears far greater than is normally expected from "long" fatigue crack propagation [1], a microstructure-sensitive NDE technique would provide a better resolution for the earlier detection of fatigue damage. With its extremely high sensitivity and the real-time capability, AE can be a unique means to detect the onset of fatigue damage. Green and Duke [25] suggested the potential applicability of AE as well as ultrasonics to the early detection of fatigue damage some twenty years ago. They concluded that, however, AE could not be optimally suited for inservice fatigue damage detection unless an efficient filtering technique to separate signals due to short fatigue cracks from background noise should be developed. Numerous investigations have been carried out on AE monitoring of fatigue. Majority of the studies [26-35], however, focused on the monitoring of slow crack propagation except a few [36-40] reported very recently on the monitoring of fatigue crack initiation of some special alloys. AE measurement during the initial stage of fatigue damage is rather difficult because the SNR is very low. Spatial discrimination based on AE source location appears to be quite successful for the elimination of noise during slow crack propagation but not enough for that during crack initiation. Although it has certain advantages such as high sensitivity and remote-sensing capability, AE showed a critical drawback of the difficulty in separating AE signals due to short fatigue cracks from background noise. During the last two decades, however, there has been a significant improvement in AE instrumentation and signal processing as well. The

96 discrimination between AE signals and background noise during fatigue loading still appears to be difficult but not impossible nowadays. One of the potential solution for such a problem is the wavelet transform (WT) de-noising which can provide a significant improvement of SNR to the inherently noisy AE signals during fatigue. The basic treatment of this technique and some experimental verification from AE monitoring of fatigue damage are to be discussed. WAVELET DE-NOISING OF AE SIGNALS Wavelet transform is a new type of time-frequency analysis that can provide a practical means for the digital filtering of transient signals [41,42]. WT can be defined as the correlation of the signal, s(t) and a set of short waveforms or wavelets, g(t) and is expressed by the following relationship in digital domain [43]: (1)

C(a,b) = C ( j , k ) = ~_~S(n)g j.k (n) neZ

The analysis starts from s(n) and results in the wavelet coefficients C(a,b). The function, g(n) is called mother wavelet. The synthesis is the reciprocal operation of analysis and starts from the coefficients C(a,b) to reconstruct s(n) as follows:

S(n) = ~ ]~_.C(j,k)gj,k (n) j~Z

(2)

k~Z

Equation (1) and (2) are also known as the discrete wavelet transform (DWT) and the inverse discrete wavelet transform (IDWT). For the WT to be applied to the de-noising of transient signals, the original signal, S is decomposed into a series of details, D's and approximations, A's. Mallat [43] introduced an algorithm for the calculation of wavelet coefficients of details and approximations, which is basically a two-channel subband coder as shown in Fig. 2(a). The decomposition can be continued as many steps as needed to form a decomposition tree as shown in Fig. 2(b). Wavelet coefficients for each detail are then filtered by the appropriate setting of threshold. Finally a de-noised signal is reconstructed by combining the original approximation and the modified details.

Filters Iowpass

highpass

i

[AI

t

(a)

I DI

mIsl-] FIA, I] ,o,, I 1-] !o21 F 1o31 (b)

Fig. 2. The wavelet decomposition process: (a) filtering by a two-channel subband coder; (b) the wavelet decomposition tree.

97 One should note that the characteristics of mother wavelet greatly influences both the decomposition and reconstruction procedure. You may build your own wavelet for the particular set of signals but there are many different types of wavelets available in the literature including Internet [44]. The daubechies, symmlets, and bi-orthogonal wavelets were examined for the data sets in the present work but most of the results will be presented with symmlet. Symmlets are the modifications of daubechies families so that the properties of the two wavelet families are very similar [45]. We employed symmlet 7, which is not quite symmetric but nearly symmetric, as shown in Fig. 3. Figure 3(a) is the scaling function and Fig. 3(b) is the symmlet-7 mother wavelet. The scaling function is also known as a father wavelet.

0.5

0.5

0 -O.G

-O.G

-1 0

2

4

6

8

10

12

0

2

4

(a)

6

6

10

12

(b)

Fig. 3. The symmlets families: (a) father wavelet; (b) mother wavelet. Another important factor that affects the de-noising quality is the selection of the threshold function. Two basic functions are hard threshold and soft threshold defined by

(

sig <x> lxl- >

=

if if

ixl> / Ixl-

(3a)

and

x

/f Ixl> )

o

iy Ixl - )

(3b)

They are also known as the shrinkage ftmctions and the difference between the soft and the hard functions can be easily found in Fig. 4. The hard thresholding is so named because the shrinkage ftmction has a discontinuity: values x which are above the threshold, ik are untouched. On the other hand, the soft thresholding utilize the continuous shrinkage function and values x above the threshold are shnmk. The soft thresholding is employed in the present study since the noise of unknown characteristics is supposed to affect all wavelet coefficients. Many different algorithms for the thresholds are available from the literature. To name a few, universal, minimax, rigorous SURE, heuristic SURE, cross validation etc. are the most commonly used. SURE stands for Stein's Unbiased Risk Estimator [45]. There is no easy and simple rule for which threshold to use, so as to employ which mother wavelet - it depends on the nature of your signals. We first examine the entire threshold algorithms

98 above with signals presumably from fatigue crack propagation in the latter stage of each test. By doing that we selected the automatic soft-thresholding based on the universal algorithm for the present work. 1

1

/ //

0.5

0.5

////

-1

L

/,J'/

/

-0.5

/-

0

/

/ -0.5:

-1

-1

/

/

l/ 1

(a)

(b)

Fig. 4. The thresholding scheme with shrinkage functions applied to the original signal of a linear function: (a) hard thresholding; (b) soft thresholding. Before the actual application of wavelet de-noising to AE experiments, we have tested the efficiency of procedure we have adopted. First of all, an artificial signal as shown in Fig. 5(a) was generated by pencil lead break. The signal was then covered by a random noise as shown in Fig. 5(b). Finally, the original signal was recovered by using wavelet denoising procedure we have adopted in the above. Fig. 5(c) shows the recovered signal appears to be nearly the same as the original one. 0.6 | |

o

.N

,

i i

--112). 115

Timme

(rams)

(a)

:=:.,-

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Fig. 5. Test of the efficiency of wavelet de-noising procedure: (a) an artificial AE signal synthesized from pencil lead breaks; (b) the signal covered up by random noise; (c) the signal recovered by using wavelet de-noising scheme.

99 EXPERIMENTAL RESULTS Thick Section with A P I 5L Gr. 42 Steel

Compact tension specimens with a keyhole at the notch simulating the blunt notch or smooth surface were prepared from a typical pipeline steel, API 5L Gr. 42. Specimens were cut out of the heat-affected zone (designate as HZ) of a welded pipe whose wall thickness is 12mm. High-cycle fatigue tests were carried out at 5Hz by using a servohydraulic testing machine. Fatigue loading was controlled by constant stress amplitude at 10% of the ultimate tensile strength of specimens and R-value was 0.13. Since the crack was expected to start at the horizontal centerline of keyhole, two broadband AE sensors were attached at each epicenter position perpendicular each other. Detected AE signals were first amplified by 40dB and filtered by 100kHz-1.2MHz passband, then processed by an AE digital signal processor at the expansion slot of personal computer and recorded at hard disk for further analysis. The detection threshold was set at 49dB. A schematic diagram of experimental set-up can be found elsewhere [46]. The polished surface of keyhole was also continuously monitored by a traveling microscope for the incipient fatigue crack.

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-0.05 -0.1 -O.

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(b) Fig. 7. A trainer signal utilized for the selection of de-noising procedure: (a) original signal from the specimen HZ01 and (b) the de-noised signal.

100 Figure 6 is a typical example of cumulative AE events with fatigue cycles up to failure. To select the mother wavelet and the thresholding schemes which are the most suitable combination for wavelet de-noising, we first apply various combinations to those signals from the stage 3 in Fig. 6. At this stage, SNR is much higher than the earlier stage and the fatigue crack growth is readily observed with AE data acquisition. Therefore, the signal shown in Fig. 7 is presumably a typical AE signal due to fatigue cracks. This type of signals is employed as the trainer for the selection of mother wavelet and threshold algorithm for de-noising procedure. The earlier stage of fatigue damage can also be recognized a sudden increase in cumulative AE events versus fatigue cycles curve as shown in Fig. 8. In this particular one, test was interrupted at about 125,000 cycles after a couple of sudden increase in the curve appeared. The rest of the curve drawn in dotted line is only a prediction based on the data from the other specimens of same materials tested under same condition but uninterrupted up to failure. For the convenience of analysis, we divide the curve into several stages and designated from 1 to 5, although waveforms recorded from stages 1 and 2 are mostly analyzed since we are interested in the short fatigue cracks or the crack initiation. ~"

[ API 9~ Gr.42 (CT) I

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50000

100000

.

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200000

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(b)

Fig. 8. Cumulative AE events vs. fatigue cycles curves: (a) a curve corresponding to the waveform data analyzed (specimen HZ 11); (b) a composite of four curves obtained from the interrupted tests for short fatigue cracks. The total number of waveforms recorded were 18,467 up to 125,000 cycles, out of which 4,673 waveforms up to 63,500 cycles were subjected to analysis. Most of them, however, appeared to be background noise of various sources since they are very low in amplitude or frequency content or both. For the stage 1, 15 out of 233 waveforms were analyzed by the wavelet de-noising, whereas 180 out of 4640 waveforms analyzed for the stage 2. Figure 9 shows a typical result of the analysis, where the six original waveforms detected during the stage 2 are shown in Fig. 9(a) and the de-noised waveforms are shown in Fig. 9(b). All of them can be regarded as the AE signals due to short cracks since they are fairly high in amplitude and frequency content. By the analysis with wavelet de-noising, however, it is clearly shown that three signals, HZ11-530, -539, -2687 were due to crack initiation or extension whereas the other three signals, HZ11-478, -2947, -3096 were due to some other source mechanisms. At the moment, we cannot tell exactly what is the sourcemechanism for the latter but the reduction of noise level by WT is significant [47]. In the analysis with wavelet de-noising, first we trained the scheme by using AE signals like in Fig. 7 as the reference for crack-induced signals which was detected during the latter stage of fatigue failure.

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Fig. 9. Six representative AE waveforms detected during the initial stage of the fatigue test of API 5L steel: (a) the original signals; (b) the de-noised signals. These specimens with interrupted tests to verify the existence of fatigue cracks were subjected to the examination by scanning electron microscopy (SEM). At the centerline of keyhole surface of the specimens HZ11 whose data were analyzed above, a short fatigue crack was found as shown in Fig. 10(a). Since the crack length utilized in fracture mechanics is measured in perpendicular to the crack shown in the SEM micrograph, we cut the specimen so that the crack length can be measured in Fig. 10(b) as about 3 50/~qn.

Fig. 10. SEM micrographs of a short fatigue crack from the interrupted test: (a) at keyhole surface; (b) at cross section. (API 5L Gr. 42 steel CT specimen)

102 Thin Plate with Alclad 2024-T3

Single edge notched (SEN) specimens with a semi-circular notch of 10mm diameter were prepared from 1/16 inch thick plate of aluminum alloy, Alclad 2024-T3. Specimen and experimental set-up is shown in Fig. 11. The specimen surface at the root of notch was polished to be ready for any microscopic examination during and after the fatigue test. Most of the loading conditions are similar to those for API steel specimens in the previous results. Since the crack was expected to start at the notch root at the center of specimen, two broadband AE sensors were attached at equal distance apart from the center. By utilizing the delta-T discrimination based on the linear source location, only the signals originated from the region of interest were recorded. Any erroneous noise from outside the region, for example, noise from grip, can be effectively rejected. The polished surface at the notch was also continuously monitored by a traveling microscope for the incipient fatigue crack.

~024-T3 AE ~enaor8

AE D S P

Load Slope Into.

Fig. 11. Specimen and experimental set-up for AE data acquisition during the fatigue test of Alclad 2024-T3 SEN specimens. Figure 12 is a typical example of cumulative AE events with fatigue cycles up to failure. Comparing to the case of pipeline steel, the aluminum alloy was not very noisy. Also note that the specimen goes to fail very quickly once any small crack initiated at the notch root. Therefore, the total number of events up to failure is a few hundred at most. It was quite difficult to monitor the earlier stage of fatigue test of such a precipitation hardened alloy because it failed quickly once we recognize the crack initiation. As shown in Fig. 12, there was no AE events detected until 92,500 cycles, or more than 90% of the fatigue life. On the other hand, the crack growth becomes quickly unstable after 97,500 cycles. From cumulative AE events curve, the period of stable crack growth is equivalent to just 5,000 cycles, shorter than twenty minutes.

103 AA2024-T3 SEN .~

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Fig. 12. A cumulative AE events vs. fatigue cycles curve from a full-range fatigue test up to failure of an Alclad 2024-T3 specimen. For the purpose of examining short fatigue cracks, tests were typically interrupted at less than 100,000 cycles. The total number of events up to 100,000 cycles was typically less than 60 as shown in Fig. 13(b). The number of recorded waveforms were as small as 10. The one mostly analyzed in this study had 12 waveforms recorded as shown in Fig. 13(a). We analyze 12 waveforms all together with the same procedure applied for steel specimens, first. We have also checked if there is any better combination of a mother wavelet and any thresholding scheme. No better result could be expected at the moment. AA2024-T3 SEN Interrupted 40 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42

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Fig. 14. Six representative AE waveforms detected during the initial stage of a fatigue test: (a) the original signals; (b) the de-noised signals. (Alclad 2024-T3) Buck [24] suggested that SEM can detect the fatigue damage as early as at 10% of life whereas certain NDE methods can detect that at about 50% of life, at best. SEM seems to provide the highest resolution among all of the measurement technique for fatigue damage. Those specimens with interrupted tests were subjected to SEM examination to verify the existence of and to accurately determine the size of short fatigue cracks. Since the specimen is relatively thin, any optical measurement including SEM examination can be done at the surface near notch root. A long crack of about 5 mm and a short crack of about 500 /an were found at individual specimen tested interrupted manner. It appeared that the crack path is not uniquely defined but distributed over the range of damage as shown in Fig. 15(b). SUMMARY AND DISCUSSION For the detection of fatigue damage at the earliest stage, we have applied wavelet denoising technique to improve the SNR of AE signals during high-cycle fatigue testing of a pipeline steel, API 5L and aluminum alloy, AA2024-T3, respectively. In the analysis of AE signals with wavelet de-noising, first we trained the scheme by using AE signals detected during the latter stage of fatigue failure. At this stage, SNR is much higher than the earlier stage and the fatigue crack growth is readily observed concurrently with AE data acquisition. Therefore, the signal shown in Fig. 7 is presumably a typical AE signal due to fatigue cracks. The onset of fatigue damage appeared to be detectable by any means introducing a newly developing technology.

105

Fig. 15. SEM micrographs of fatigue cracks from the interrupted tests: (a) a long crack (b) a short crack. (Alclad 2024-T3 SEN specimens) It has been quite obvious that SNR can be significantly improved by the new digital filtering such as wavelet de-noising, split spectrum processing [48], etc. It is also true, however, that the digital filtering itself cannot provide a direct means to detect and evaluate the fatigue damage. Since the early stage of fatigue damage is a process governed by microstructural features such as dislocations, precipitates, slip band, grain boundary and so on, digital signal processing or continuum mechanics alone cannot provide a proper solution to such a complicated problem. AE can be uniquely suited for the detection of fatigue damage, but it also has a critical drawback of difficulty in separating AE signals due to short fatigue cracks from background noise. In addition to the significant improvement in AE measurement system during the last two decades, a new version of digital filtering technique has to be developed for AE to be optimally suited for the real-time detection of fatigue damage. Wavelet transform de-noising appears to be one of the potential candidates for such a purpose. Signals due to the crack closure are also important for AE monitoring of short fatigue cracks although they were inadvertently skipped in this article. It appears that a more systematic and integrated approach is necessary to thoroughly understand this interdisciplinary subject. CONCLUSION The onset of fatigue damage could be more accurately evaluated if the problem would be tackled by the interdisciplinary manner utilizing mechanics, materials science, physics, signal processing, and so on. By introducing the wavelet de-noising scheme, AE signals due to fatigue crack initiation or short cracks appeared to be separable from background noise. As the detection of short fatigue cracks becomes important nowadays, AE as well as any nondestructive technique that is sensitive to the variation of microstructure should be developed further.

106 REFERENCES 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21. 22. 23. 24. 25. 26. 27. 28.

Davidson, D.L. (1996) J. of Nondestructive Evaluation 15, 101-106. ASTM E647-95 (1995). Annual Book of ASTMStandards 03.01, pp. 578-614. Hussain, K. (1997) Engineering Fracture Mechanics 58, 327-354. Miller, K.J. (1993) Materials Science and Technology 9, 453-462. Hudak, S.J., Jr., Davidson, D.L., Chan, K.S., Howland, A.C. and Walsch, M.J. (1988). Growth of Small Cracks in Aeroengine Disc Materials (AFWAL-TR-88-4090). Wright-Patterson Air Force Base, Ohio. Ritchie, R.O. and Lankford, J. (1986). In: Small Fatigue Cracks, R. O. Ritchie and J. Lankford (Eds). The Metallurgical Society, Warrandale, PA, pp. 1-5. Suresh, S. and Ritchie, R.O. (1984) International Metals Review 29, 445-476. Newman, J.C., Jr. (1983). A Nonlinear Fracture Mechanics Approach to the Growth of Small Cracks (AGARD-CP-328). Paris, France, pp. 6.1-6.26. Kitagawa, H. and Takahashi, S. (1976). In: Proceedings of the 2nd International Conference on Mechanical Behavior of Materials, Boston, MA, pp. 627-631. Pearson, S. (1975) Engineering Fracture Mechanics 7, 235-247. Ritchie, R.O. and Lankford, J. (Eds). (1986). Small Fatigue Cracks, The Metallurgical Society, Warrandale, PA. Miller, K.J. and Rios, D.L. (Eds). (1986). The Behavior of Short Fatigue Cracks EGF 1, Mechanical Engineering Publications, London. Miller, K.J. and Rios, D.L. (Eds). (1992). Short Fatigue Cracks, ESIS 13, Mechanical Engineering Publications, London. Larsen, J.M. and Allison, J.E. (Eds). (1992). Small-Crack Test Method, STP 1149, American Society for Testing and Materials, Philadelphia, PA. Larsen, J.M., Jira, J.R. and Ravichandran, K.S. (1992). In: Small-Crack Test Method, STP 1149, American Society for Testing and Materials, Philadelphia, pp. 57-80. Swain, M.H. (1992). In: Small-Crack Test Method, STP 1149, American Society for Testing and Materials, Philadelphia, pp. 34-56. Gangloff, R.P., Slavik, D.C., Piascik, R.S. and Van Stone, R.H. (1992). In: SmallCrack Test Method, STP 1149, American Society for Testing and Materials, Philadelphia, pp. 116-168. Resch, M.T. and Nelson, D.V. (1992). In: Small-Crack Test Method, STP 1149, American Society for Testing and Materials, Philadelphia, pp. 169-196. Sharpe, W.N., Jr., Jira, J.R. and Larsen, J.M. (1992). In: Small-Crack Test Method, STP 1149, American Society for Testing and Materials, Philadelphia, pp. 92-115. Davidson, D.L. (1992). In: Small-Crack Test Method, STP 1149, American Society for Testing and Materials, Philadelphia, pp. 81-91. Herzberg, R., Herman, W.A., Clark, T. and Jaccard, R. (1992). In: Small-Crack Test Method, STP 1149, American Society for Testing and Materials, Philadelphia, pp. 197-220. Lemaitre, J. (1996) A Course on Damage Mechanics, 2nd Ed., Springer-Verlag, Berlin, Germany, pp. 19-37. Dobmann, G. (1995) Rev. of Progress in Quantitative NDE 14A, pp. 2003-2010. Buck, O. (1998) Rev. of Progress in Quantitative NDE 17A, pp. 1-13. Green, R.E., Jr. and Duke, J.C., Jr. (1979) International Advances in Nondestructive Testing 6, 125-177. Moore, J.F., Tsang, T. and Martin, G. (1971). The Early Detection of Fatigue Damage, (AFML-TR-71-185). North American Rockwell Co., Los Angeles. Harris, D.O. and Dunegan, H.L. (1974) Experimental Mechanics, 71-81. Yuyama, S., Kishi, T., Hisamatsu, Y. and Kakimi, T. (1982). In: Progress in Acoustic Emission, M. Onoe, K. Yamaguchi and T. Kishi (Eds). JSNDI, Tokyo, Japan, pp. 126-

107 133. 29. Baram, J. (1984) Engineering Fracture Mechanics 19, 181-185. 30. Scala, C.M. and Cousland, S.M. (1985) Materials Sci. and Engrg. 76, 83-88. 31. McBride, S.L. and Harvey, J.L. (1987) Rev. of Progress in Quantitative NDE 6A, D.O. Thompson and D.E. Chimenti (Eds). Plenum, New York, pp. 353-360. 32. Bowles, S.J. (1989) NDTInternationa122, 7-13. 33. Buttle, D.J. and Scruby, C.B. (1990) Jr. of Acoustic Emission 9, 243-254. 34. Ono, K. and Wu, J.-Y. (1996). In: Progress in Acoustic Emission VIII, T. Kishi, Y. Mori, Y. Higo and M. Enoki (Eds). JSNDI, Tokyo, Japan, pp. 237-242. 35. Hamstad, M.A. and McColsky, J.D. (1997) Jr. of Acoustic Emission 15, 1-18. 36. Kohn, D.H. and Ducheyen, P. (1992)Jr. of Materials Science 27, 1633-1641. 37. Fang, D. and Berkovits, A. (1993) Jr. of Acoustic Emission 11, 85-94. 38. Granata, D.M., Kulowitch, P., Scott, W.R. and Talia, T. (1993) Rev. of Progress in Quantitative NDE 12B, D.O. Thompson and D.E. Chimenti (Eds). Plenum, New York, pp. 2183-2190. 39. Berkovits, A. and Fang, D. (1995) Engineering Fracture Mechanics 51, 401-416. 40. Shi, Z., Jarzynski, J. and Bair, S. (1999) Rev. of Progress in Quantitative NDE 18A, D.O. Thompson and D.E. Chimenti (Eds). Plenum, New York, pp. 395-401. 41. Gueller, E., Sankur, B., Anarim, E., Mendi, C.D., Alkin, O., Kahya, Y.P. and Engin, T. (1994). In: Advances in Signal Processing for NDE of Materials, P.V. Maldague (Ed.). Kluwer, Dordrecht, Netherlands, pp. 269-283. 42. Suzuki, H., Kinjo, T., Hayashi, Y., Takemoto, M. and Ono, K. (1996) Jr. of Acoustic Emission 14, 69-84. 43. Mallat, S.G. (1989) IEEE Trans. Pattern Anal and Machine Intell. 11,674-693. 44. For example, Wavelet Digest at 45. Bruce, A. and Gao, H.-Y. (1996). Applied Wavelet Analysis with S-Plus, Springer, New York. 46. You, H., Kwon, O.-Y. and Lee, K. (1999). In: Nondestructive Characterization of Materials 1X, R.E. Green, Jr. (Eds). AIP Conference Proceedings 497, American Institute of Physics, New York, pp. 48-53. 47. Abbate, A., Frankel, J. and Das P. (1996) Rev. of Progress in Quantitative NDE 15A, D.O. Thompson and D.E. Chimenti (Eds). Plenum, New York, pp. 741-748. 48. Karpur, P. and Resch, M.T. (1991) Rev. of Progress in Quantitative NDE 10A, D.O. Thompson and D.E. Chimenti (Eds). Plenum, New York, pp. 757-764.

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109

ACOUSTIC EMISSION/MICROSEISMIC TECHNIQUE: REVIEW OF RESEARCH IN THE 20 TM CENTURY AND FUTURE ASPECTS Hiroaki NI1TSUMA Graduate School of Engineering, Tohoku University, Sendai 980-8579, Japan

ABSTRACT Subsurface acoustic emission (AE)/Microseismic (MS) technique has a long history since 1910 and has been effectively used for rock burst prediction and structural integrity of underground cavern such as mines and tunnels, mapping and characterizing petroleum and geothermal reservoirs, monitoring and controlling of hydraulic fracturing, and the other various applications. The technique provides detailed information about reservoirs and fracture systems in real time at distant locations. There have been considerable improvements in AE/MS technology during the last 20 years especially for the digital data acquisition techniques, downhole multicomponent sensors, digital signal processing, precise mapping techniques such as the "collapsing" and the "doublet analysis", and for imaging techniques such as the "AE reflection method" and the "Seismic While Drilling". The AE/MS method, however, only provides limited information on seismically active fractures or failures in underground. Then, it is essential to make interdisciplinary works in future between AE/MS, geomechanics and hydraulics for a comprehensive understanding of the fracture distribution and hydro geomechanical processes in the fields.

KEYWORDS Acoustic Emission, Microseismicity, induced seismicity, geologic structures, mapping, imaging

INTRODUCTION Subsurface acoustic emission (AE)/Microseismic (MS) technique has been effectively used for rock burst prediction and structural integrity of underground cavern such as mines and tunnels, characterizing petroleum and geothermal reservoirs, and for various subsurface applications over the last several decades. In these areas, a comprehensive understanding of the fracture systems on geometry, and hydraulic and geomechanical behavior during operations is essential. Some of the information can be obtained from well logs but they only provide direct information about conditions near wellbores. The AE/MS technique can be primary methods

110 for obtaining detailed information about reservoirs and fracture systems in real time at distant locations up to several kilometers from the surface and boreholes. There have been considerable improvements in AE/MS technology especially for data gathering techniques, mapping/imaging methods and for understanding of AE/MS phenomena during the last 20 years. In this article, I will overview the historical progress and current status of the AE/MS technique in the 20th century as well as its future aspects.

HISTORY Table 1 summarizes a history of the AE/MS applications. Earthquakes of magnitudes less than 3 are generally referred to as "microearthquake" or "microseismicity" in earthquake seismology [1, 2, 3]. MS monitoring has been done for the study of main and aftershock of earthquakes, and for earthquake prediction since 1948 [1]. However, observation of MS with an engineering objective started long before that. It was known among the people in deep mines that "rock tremors" are often heard and they are a precursor of the most dangerous "rock bursts" in the mines. An early example of MS observations is the Witwatersrand gold mine in South Africa [3], where a seismometer was installed in 1910 in order to monitor the rock bursts precursors. In 1939 a seismic network, which consists of 5 mechanical seismometers, was installed in the area in order to locate the tremors. In 1970s MS monitoring was widely introduced to various mines for the prediction of rock bursts [4, 5, 6]. On the other hand, the monitoring of induced MS in man-made water reservoirs has also a long history. In 1938 more than 4000 MS events were observed in Lake Mead, Arizona [3]. MS networks are now routinely deployed to monitor seismicity before, during and after reservoir filling. Since 1972 MS is also monitored in conventional geothermal fields in order to get information on location ~nd dynamics of geothermal reservoirs [7]. It should be noted that the detection and identification of nuclear weapons tests is one of the most utilized application of the MS technique. In 1984, a worldwide MS network of 75 MS stations in 37 countries was installed [8]. On the other hand, it is said that Obert and Duvall made the first observation of subsurface AE in 1938 [9]. During an acoustic test of a mine pillar using a transmitter and receiver, they found that acoustic signals from the pillar were detected in the receiver in spite that the transmitter was turned off. These were the AE events generated in the stressed pillar. Because of this fact, AE used to be called "rock noise" or "rock talk." In 1970s, the AE technique was widely introduced in laboratory tests of rock sample and in field tests corresponding to the development of the AE technique for non-destructive testing of metallic structures [10, 11]. In 1973, a field AE measurement of underground gas storage was conducted by Hardy et al. in order to monitor and control the instability of the reservoir[12]. The technique was also employed in the first Hot Dry Rock (HDR) geothermal project of Los Alamos Scientific Laboratory (LASL, currently LANL). A downhole triaxial AE

Table 1" History o f A E / M S t e c h n o l o g y 1910

1920

1930

1940

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Kaiser's tin cry ( 1953) .

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Observation of rock tremors in South Africa (1910)

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Rock burst monitoring in mines Monitoring in coal mine-by Blake ( 1971)

Seismic Network in South Africa (1939)

AE measurement in labo. by Mogi (1962) Earthquake during, water rejection in Denver(1962)

Discovery of rock no~se (1938)

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Gas storage monitoring by Hardy (1973) AE monitoring of hydraulic fracturing in HDR development LASL CSM Hijiori Ogachi Soultz (1976) (1982) (1986) (1991) (1993) AE monitoring in oil & gas field m

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Clinton (1993) .

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MS monitoring of dams Arizona, US (1938)

Kurobe, JPN (1963) Geysers (1972) Fukui (1948)

MS observation in geothermal fields Kakkonda (1977)

Microeathquake observation Matsushiro New Mexico (1956) (1962) Test ban monitoring (1963) GSETT- 1 (1984)

GSETT-2 GSETT-3 (1991) (1995)

, v

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112 measurement tool was developed and used in the project for the monitoring of a hydraulically induced HDR reservoir in 1976 [13]. The AE technology used to be based on the ultrasonic technology. The frequency range of the subsurface AE measurement was originally 10 k H z - 100 kHz and expanded downward to several 10s Hz according to the development of practical applications. On the other hand, the MS technology is an elongation of the earthquake seismology, and the MS observation expanded their range upward from originally 0.1 Hz - 10 Hz to several 100s Hz according to the development of wide band and high sensitive systems. Therefore, the frequency ranges of AE and MS in field measurements are overlapped with each other. While the MS technique originally observed natural seismicity and was mainly used for understanding natural phenomena, the AE technique observed artificial or induced seismicity and was employed mainly for engineering objective. However, both techniques are passive measurements of elastic wave propagating in the underground, and there is essentially no difference between them. Actually, recent subsurface AE technique employs various methods that originated from seismology. In these meaning, both techniques have almost fused with each other especially for the engineering applications. In this article, I will site generically them as "AE/MS technique".

AE/MS T E C H N I Q U E A E / M S sources

AE/MS measurement is usually done with an engineering objective: to monitor and control responses in underground caused by human activities such as mining, tunneling, drilling, hydraulic fracturing, production of oil, gas and geothermal fluid, injection of fluid, civil engineering construction, and so on. Therefore, AE/MS sources to be observed are in most cases induced seismicity by the artificial operations. Seismic waves associated with drilling or machining is also utilized for process monitoring of the operations and for imaging surrounding areas.

Signal collection

In AE/MS measurement source parameters such as source location and distribution, source configuration, source activity, and source dynamics are examined. In order to get the information on the source parameters, a seismic network, which consists of several sensors or stations, is deployed. Geophones (moving coil type transducer) or piezoelectric sensors are normally used for the seismic network. Since the frequency range of field AE/MS measurement is less than 1 kHz in most of the case, and a quantitative analysis of full wave is preferable, non-resonance type transducers such as velocity meters or accelerometers are usually used for the measurement.

113 Hydrophones are also used as sensors. Both surface and downhole sensors are used in the measurement. A high sensitive and high fidelity measurement without the effect of weathering layer and the surface noise can be realized by a downhole measurement, although this is costly because a special package for downhole use is required and also the drilling of wells. The downhole observations can detect up to two orders of magnitude more events than can surface networks when the AE/MS sources are located at 2000 m deep [ 14]. Downhole multicomponent measurements and multicomponent signal-processing techniques are effective for precise detection of wave arrivals [15], wave mode detection, and finding coherent phases such as direct and reflected arrivals [16]. However, the reliability of multicomponent detectors is very sensitive to the system design and not all detectors used today produce reliable recording of particle motion associated with wave arrivals. Moriya and Niitsuma devised a method using the spectrum matrix to evaluate in-situ characteristics of downhole sensors [17]. Figure 1 (a) and (b) show examples of this evaluation. These figures show direction and linearity of hodogram (3D Lissajous' figure between X, Y and Z component) as a function of time and frequency at a P-wave onset. It is seen that the sensor (b) detects P-wave onset more stably and in a wider frequency range than the sensor (a). This method provides diagnostic information about sensors and about reliability of specific deployments of a given sensor. It is possible to optimize the geometry of a seismic network to yield reliable source locations. The optimum network geometry depends on the objective of the measurement, and the choice of the design of a network must be carefully considered [ 18]. It will always be necessary to

Fig. 1: Evaluation of in-situ characteristics of downhole three component sensor by using the spectrum matrix. (a) proto-type sensor, (b) improved sensor. The color shows linearity of hodogram.

114 compromise with the optimal network design due to the high cost of installation of sensors. Thus, we must concentrate our effort on developing methods to obtain the maximum amount of information from the detected signals.

Source location A primary result of the AE/MS method is a map showing 3D location of sources. Determining the location of microseismic sources is an inversion problem in which a model is used to fit data and certain parameters are predicted. The technique has been well developed by earthquake seismologists and its limitations are well known. The factors that determine the accuracy and reliability of source location are: (1) the number and distribution of the sensors, (2) the complexity of and knowledge about the local velocity structure, (3) signal-to-noise ratio, (4) signal bandwidth, (5) the transfer function of the measurements system, (6) the accuracy in picking wave onsets, and (7) the inversion method [ 19]. The conventional least-squares method, called "arrival-time difference method", is applicable for the source location. The method requires knowledge of arrival times of P (and S) wave at each sensor as well as velocity structure and station corrections. The Joint Hypocenter Determination (JHD) method [20] yields improved information about the relative locations of events. Calibration of seismic networks using artificial sources placed at known locations is essential for obtaining reliable source locations. Recently, Stewart et al. [21] proposed a method based on the calculation of a semivariogram for testing the validity of the velocity structure and station corrections used to find the source locations. The hodogram method [22] of locating events is based on the use of particle motion measured at one or more multiple component sensors to obtain the direction to the source. The distance to the source is determined from the difference between P- and S-wave arrival times at one or more sensors. Under optimal conditions, the method can locate events as reliably as can be found by the arrival-time difference method using many sensors [23]. However, the method can be easily affected by the performance of the downhole sensor, by local heterogeneity of velocity, and by the signal processing method.

Signalprocessing Signal processing techniques have advanced significantly since 1980 due to the development of digital data acquisition, storage, and processing hardware. Significant advances have been made in time-frequency analysis of non-stationary data [24]. Because AE/MS signals are non-stationary, further progress can be expected in areas such as precise picking of wave arrivals and multicomponent full wave analysis. Due to these expected advances, downhole wide-band multicomponent measurements are desirable. Automatic picking of wave arrivals and mapping have been developed for both the arrival-time difference [25, 26] and hodogram methods [27]. However, manual analysis is needed for more accurate and detailed analysis of event locations.

115

Source analysis Since AE/MS signal is the by-product of subsurface phenomena and does not directly represent a physical property, an interpretation of the meaning of AE/MS is essential. Characterizing the number, energy of AE/MS events provides information about the stability, scale and energy release rate of fractures. AE/MS source analysis gives an equivalent motion at the source but the source motion may not directly correspond to the actual dynamics of individual fractures. Brune's model [28] has conventionally been used to determine source size and fault slip for AE/MS. The results should, however, be interpreted with caution in AE/MS applications. While the method is reliable for moderate size regional earthquakes, mine back experiments in gold mines have shown that seismic estimates of the source radius of AE/MS are one order larger than actual sizes [29]. The apparent corner frequency is affected by the transfer function of the propagation medium and the measurement system. We usually assume we can correct for these factors [30], but some results show that the corrections may not always be adequate. Magnitude may be a reliable estimate of source radius, and variations in magnitude give some indication of variations in sizes of slip area. Waveforms of most AE/MS events observed at several kilometers depth are dominated by the shear component of motion. This is confirmed by fault plane solutions that are double couple and the average ratio of P-to-S wave radiation, which is similar to that expected for shear failure [30]. If tensile failure accompanies the events, it has little influence on observed waveforms, presumably due to the low seismic efficiency of the tensile component of fracturing [31, 32, 33]. Fault plane solutions are useful for estimating stress state and interactions of fluid with the stress field [34, 35]. Combined focal mechanism analysis and the precise mapping using methods such as doublet analysis will provide an estimate of the regional stress field [36].

APPLICATIONS

Mine, tunnel and underground cavern The AE/MS technique has been used in mines for rock burst or gas burst prediction, where increase occurrence rate and spatial concentration of AE/MS are usually examined [37, 38]. The technique has also been employed during excavation of tunnels [39] and underground caverns [40, 41] in order to detect and characterize induced fractures associated with the excavation and other treatments, and to monitor stability of existing faults and joints. Figure 2 shows an example of AE/MS source distribution during the Tunnel Sealing Experiment in the Underground Research Laboratory for nuclear waste disposal, Manitoba, Canada [41]. A sealed chamber in excavated tunnel at 420 m deep was pressurized up to 4 MPa, which corresponds to the representative ambient pore pressure. MS measurement with 10 Hz to 10kHz frequency range and AE measurement with 50 kHz to 250 kHz were conducted.

116

Fig. 2: AE (left) and MS (right) distributions during the Tunnel Sealing Experiment in the URL for nuclear waste disposal [41 ].

Figure 2 (a) and (b) is the distribution of MS and AE sources, respectively. cracking zone was estimated in this experiment.

Induced micro

Geothermal It was known that MS is often observed in geothermally active areas. In 1972, a seismic network was installed in the Geysers geothermal field, USA. After that, MS observations have been made in almost every geothermal power plant in order to investigate the distribution of the reservoir and its changes. In the HDR development, AE/MS monitoring has been used for in-situ monitoring of hydraulic fracturing and circulation tests as a principle means by which the creation and exploitation of a man-made geothermal reservoir can be monitored in real time. It can provide information about the size of the reservoir, locations of fractures, and whether the reservoir volume is expanding during reservoir circulation. Recently, hydraulic fracturing and water re-injection are often conducted also in the conventional geothermal plants in order to increase productivity, where AE/MS technique is employed as a most useful means [42, 43]. The AE/MS technique is also used to control reservoir stability during build-up tests of production well where pore pressure increases according to the valve closing [44].

117 Oil and gas

After the discovery of oil reservoirs in hard rock, AE/MS technique is highlighted recently for the monitoring of reservoir dynamics during production and treatment [45, 46]. The same technique to that for geothermal field can be applied for oil and gas reservoirs. Figure 3 shows a result of AE/MS mapping in the Clinton oil field during oil production.

Seismic While Drilling (SWD) Methods of inverse VSP (Vertical Seismic Profiling) using continuous AE signal associated with drilling as a seismic source have been developed [47, 48, 4 9 ] . It is known that these techniques work well in drilling hard rock. This technique is valuable for in-situ monitoring of the drilling process and for estimating the structure and drilling target below the drill bit, and is used in oil [50] and geothermal fields [51]. The method has been shown to give results that are consistent with those obtained using the AE reflection method and well logging in the Soultz HDR field [52].

Fig. 3: Perspective view of fracture planes defined by AE/MS during oil production in Clinton County Oil Field, Kentucky [45].

118 NEW T E C H N I Q U E

MTC Project As a result of the numbers of projects in different geological settings, there is now considerable breadth of experience in applying AE/MS techniques. In an attempt to share the experience gained by various groups, an international collaborative effort named the MTC (More Than Cloud) Project was initiated in 1993 between HDR/HWR research groups in the USA, the European Community and Japan [53]. The objectives of this project are to share the experience and ideas gained from investigations in different systems and to establish new mapping/imaging techniques that will provide detailed information on subsurface fractures. This project includes cooperative research, exchange and utilization of the field data sets obtained by each research group, joint field data acquisition efforts, researcher exchange, and annual meetings. By 2000 the project included 15 groups from the USA, UK, France, Japan, Sweden, Germany, Switzerland and Australia. In the project new mapping/imaging techniques such as "collapsing", "doublet analysis", and "AE reflection method" have been developed.

Collapsing Jones and Stewart [54] proposed a method called "collapsing" to focus a seismic cloud. This method is based on the concept that there is equal probability that the location of an event can occur anywhere within some error ellipsoid surrounding the event. The error ellipsoid is a function of the geometry of the seismic network and the misfit between the measured arrival times and those predicted using the estimated event location and origin time. The collapsing method is applied by shifting locations within their error ellipsoids towards the center of mass of the events that fall within the ellipsoid. Shifting of event locations proceeds until the distribution of movement vectors for all events in the cloud approximates that for normally distributed location uncertainties. This method does not make the uncertainties in the data any smaller nor does it provide more accurate event locations; it simply highlights structures already inherent within the unfocused data set. An example of application of the collapsing method to the earthquakes in California, USA by Jones [53] is shown in Fig. 4. Structures in the AE/MS cloud have been revealed by this method.

Doublet analysis Doublet analysis [55] is another mean to find and evaluate structures in seismic clouds. A pair or group of events that have similar or almost identical waveforms is called a doublet or multiplet. The similarity of waveforms makes it possible to precisely detect relative delays by means of the modern time-delay estimation methods [56, 57]. Figure 5 shows a result of the doublet analysis applied to the AE/MS observed in Hijiori HDR field, Japan [58]. The left hand plot shows locations determined using conventional location technique and the right hand

119

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plot shows locations determined using the doublet analysis. by this analysis.

AE reflection

Clear planar structures are found

method

Soma and Niitsuma [59] developed a method to image three-dimensional subsurface structures by a reflection survey, which uses AE/MS events as a wave source. Hodogram linearity is examined to identify coherent reflection phases in coda of AE/MS waveform in the method. 3D imaging is made by an inversion of the onsets and polarization direction of reflected waves. Figure 6 shows a result of this method applied for AE/MS events observed in the Kakkonda geothermal field, Japan [60]. The deeper structure of conventionally utilized geothermal reservoir, where the events are located, is imaged by this method. This technique was also successfully applied for the data in the Soultz HDR field, where the existence of deeper fracture layers was predicted and that was confirmed by drilling afterward [61 ]. Microsensor

Seismic networks consisting of as many observation stations as possible are necessary to improve the detectability and quality of information that can be inferred. Therefore, it is essential to develop means to enable us to deploy seismic network at low cost. Recently, the silicon micro machining technology has remarkably progressed, and various kinds of micro sensors, actuators and micro systems especially for industrial and medical use have been

120

Fig. 5: Mapping of doublet at Hijiori, Japan. The injection well (center) and production wells (besides) are shown as well as feed points (solid rectangular in the production wells).

Fig. 6: Image of deep subsurface structured in Kakkonda geothermal field, Japan by the AE reflection method [60].

121 developed. By means of the micro machining technology, a mass production of fine structured sensors which have identical structure and same quality is possible at very low cost. By using micro sensors, high quality and highly reliable measurements with many sensors can be done at low cost. A high sensitive and wide-band micro accelerometer for AE/MS measurements has been developed [62]. A combined micro sensing technology with the micro-hole drilling technology [63] will bring a drastic change in AE/MS measurements.

FUTURE PROBLEMS To develop a new technique which make it possible to measure AE/MS events by as many and high sensitive stations as possible at low cost is necessary, because the technique is passive and there is no mean if no event is detected. Although the AE/MS mapping technology has been considerably improved, there is still a considerable gap between seismic and borehole data, and we do not know how to unify results from AE/MS and borehole data obtained independently into a consistent interpretation. On the other hand, the AE/MS technique only provides information on seismically active fractures or failures. It is known that aseismic fractures exist and they are sometimes highly permeable [64]. In the applications of AE/MS technique, main objective is normally the understanding of the fracture distributions and their hydro geomechanical properties. Then, it is quite important to study further the mechanism of seismic efficiency of fractures, and to develop means to estimate and characterize the aseismic fractures. Since the AE/MS method only provide limited aspects of subsurface phenomena, it is essential to make an effort to unify complementary information from other means. In this meaning, interdisciplinary works between AE/MS, geomechanics and hydraulics are required.

CONCLUSION The AE/MS technique has a long history since 1910 and has been effectively used as a primary method for obtaining detailed information about reservoirs and fracture systems in real time at distant locations for various areas such as mines, oil and geothermal fields. There have been considerable improvements in AE/MS technology especially for the data gathering techniques, the precise mapping techniques and for the imaging techniques during the last 20 years. However, the AE/MS method only provides limited information on the seismically active fractures or failures in underground. Then, it is essential to make interdisciplinary works in future between AE/MS, geomechanics and hydraulics for a comprehensive understanding of the fracture distribution and hydro geomechanical processes in the fields.

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123 earthquakes", Computers and Geosciences, 5, 387-389. 21. Stewart, R., Jones, R., Niitsuma, H., Sasaki, S. and Kaieda, H. (1996): "A reexamination of microseismic data from the Hijiori HDR project", Abstr. In: 3r~ Intern. HDR Forum, 19-20. 22. Albright, J. N. and Pearson, C. F. (1982): "Acoustic emission as a tool for hydraulic fracture location: Experience at the Fenton Hill Hot Dry Rock site", Soc. Petr. Eng. J., 22, 523-530. 23. Nagano, K., Moriya, H., Asanuma, H., Sato, M., Niitsuma, H. and Kaieda, H. (1994): "Downhole AE measurement of hydraulic fracturing in Ogachi HDR model field", J. Geotherm. Res. Soc. Japan, 16, 85-108. (in Japanese) 24. Cohen, L. (1989): "Time-frequency distributions - a review", Proc. IEEE, 77, 941-981. 25. Baer, M. and Kradolfer, U. (1987): "An automatic phase picker for local and teleseismic events", Bull. Seismological Soc. America, 77, 1437-1445. 26. Stewart, S. W. (1977): "Real time detection and location of local seismic events in central California", Bull. Seismological Soc. Am. 67, 433-452. 27. Nagano, K., Niitsuma, H. and Chubachi, N. (1989): "Automatic algorithm for triaxial hodogram source location on downhole acoustic emission measurement", Geophys., 54, 508-513. 28. Brune, J. N. (1970): "Tectonic stress and the spectra of seismic shear waves from earthquakes", J. Geophys. Res., 90, 4997-5009. 29. McGarr, A., Spottiswoode, S., Gay, N. and Ortlepp, W. (1979): "Observations relevant to seismic driving stress, stress drop and efficiency", J. Geophys. Res., 84, 2251-2261. 30. Fehler, M. C. and Phillips, W. S. (1991): "Simultaneous inversion for Q and source parameters of micro earthquakes accompanying hydraulic fracturing in granitic rock", Bull. Seismological Soc. Am., 81,553-575. 31. Hayashi, K., Motegi, S. and Abe, H. (1988): "Characteristics of energy of elastic waves due to sudden growth of subfurface reservoir cracks for geothermal heat extraction", Progress in Acoustic Emission, JSNDI, 147-152. 32. Baria, R., Green, A. S. P,. and Jones, R. H. (1989): "Anomalous seismic events observed at the CSM HDR Project", Int. J. Rock Mech. Min. Sci. Geomech. Abstr., 26, 257-269. 33. Fehler, M. C. (1989): "Stress control of seismicity patterns observed during hydraulic fracturing experiments at the Fenton Hill Hot Dry Rock geothermal energy site, New Mexico", Int. J. Rock Mech. Min. Sci. Geomech. Abstr., 26, 211-219. 34. Gephart, J. and Forsyth, W. (1984): "An improved method for determining the regional stress tensor using earthquake focal mechanism data: Application to the San Fernando earthquake sequence", J. Geophys. Res., 89, 9305-9320. 35. Comet, F. H. and Julien, P. (1989): "Stress determination from hydraulic tests data and focal mechanisms of induced seismicity", Int. J. Rock Mech. Min. Sci. Geomech. Abstr., 26, 235-248. 36. Moriya, H., Niitsuma, H., Rutledge, J. T. and Kaieda, H. (1996): "Subsurface stress field determination using multiplets in downhole three-component microseismic measurement", Proc. 2 n~ North Amer. Rock Mech. Symp., 853-858. 37. Brink, V. Z. and O'Connor, D. (1984): "Rock burst prediction research - Development of a practical early-warning system", Acoustic Emission III, Proc. of 3rd Conf. on Acoustic Emission/Microseismic Activity in Geologic Structures and Materials, Trans Tech Publication, 269-282. 38. Urbancic, T. I. and Trifu, C. I. (1996): "Microseismic identification of stress transfer as related to rockburst occurrences", Acoustic Emission VI, Proc. of 6th Conf. on Acoustic Emission/Microseismic Activity in Geologic Structures and Materials, Trans Tech Publication, 358-369. 39. Hirata, A., Tanaka, Y., Arai, N. and Hirano, T. (1991): "Relationship between discontinuities in rock mass and AE induced by tunnel advance", Proc. 4th Domestic Conf. on Subsurface and Civil Eng.

124 Acoustic Emission, MMIJ, 102-106. (in Japanese) 40. Eisenblatter, J., Manthei, G~. and Meister, D. (1996): "Monitoring of micro-crack formation around galleries in salt rock", Acoustic Emission VI, Proc. of 6th Conf. on Acoustic Emission/Microseismic Activity in Geologic Structures and Materials, Trans Tech Publication, 228-243. 41. Young, R. P. and Cllins, D. S. (1999): "Monitoring an experimental tunnel seal in granite using acoustic emission and ultrasonic velocity", Rock Mechanics for Industry, Amadel, Kranz, Scott & Smeallie (eds), Balkema, 869-876. 42. Doi, N., Kudo, H., Takahonashi, M. and Niitsuma, H. (1988): "AE measurement and fracture behavior during hydraulic fracturing in the Kakkonda geothermal field, Japan", J. Geoth. Res. Soc. Japan, 10, 237-249. (in Japanese with English abstract) 43. Rutledge, J. T., Anderson, T. D., Fairbanks, T. D. and Albright, J. N. (1999): "Downhole seismic monitoring at The Geysers", Geoth. Resources Council Trans, 23,295-299. 44. Niitsuma, H., Chubachi, N. and Takanohashi, M. (1987): "Acoustic emission analysis of a geothermal reservoir and its application to reservoir control", Geothermics, 16, 47-60. 45. Rutledge, J. T., Phillips, W. S. and Schuessler, B. K. (1998): "Reservoir characterization using oil-production-induced microseismicity, Clinton County, Kentucky", Techtonophysics, 289, 129-152. 46. Phillips, W. S., Fairbanks, T. D., Rutledge, J. T. and Anderson, D. W. (1998): "Induced microearthquake patterns and oil-producing fracture systems in the Austin chalk", Techtonophysics, 289, 153-169. 47. Asanuma, H., Niitsuma, H. and Chubachi, N. (1990): "An analysis of three dimensional AE Lissajou pattern during well-drilling and estimation of source direction", Proc. 10th Intern. AE Sym., Tokyo, JSNDI, 436-443. 48. Asanuma, H. and Niitsuma, H. (1992): "Triaxial inverse VSP uses drill bits as a downhole seismic source", Expanded Abstr., SEG 62nd Ann. Int. Mtg., 108-111. 49. Rector, J. W. and Marion, B. P. (1991): "The use of drill-bit energy as a downhole seismic source", Geophysics, 56, 628-634. 50. Naville, C., Layotte, P. C. and Guesnon, J. (1994): "Well seismic - application of the TRAFOR MWD system to drill-bit seismic profiling", Proc. EAEG 56th Mtg., G045. 51. Tateno, M., Takahashi, M., Suzuki, I., Niitsuma, H., Asanuma, H. and Uchida, T. (1998): "Estimation of deep reflectors using tri-axial drill-bit VSP in NEDO "Deep-seated geothermal reservoir survey" in Kakkonda", Geothermics, 27, 647-661. 52. Asanuma, H., Liu, H., Niitsuma, H. and Baria, R. (2000): "Discrimination of polarization of reflected waves in the triaxial drill-bit V SP and imaging of subsurface structure at Soultz, France", SEG Expanded Abstr., SEG 70th Ann. Inter. Mtg.. (in press). 53. Murphy, H., Niitsuma, H. and Asanuma, H. (1999): "Results and next steps of the More-Than-Cloud and successor projects: International Joint Research on New Mapping and HDR/HWR reservoir development technologies", Geoth. Resources Council Trans., 23, 289-293. 54. Jones, R. H. and Stewart, R. (1997): "A method for determining significant structures in a cloud of earthquakes", J. Geophys. Res., 102, 8245-8254. 55. Poupinet, G., Flangeaud, F. and Cote, O. (1982): "P-time delay measurement of a doublet of micro earthquakes", Proc. IEEE, ICASSP82, 1516-1519. 56. Carter, G. C. (1993): "Coherence and time delay estimation: Applied tutorial for research, development, test and evaluation engineers", Carter (Ed.), IEEE Press, New Jersey. 57. Moriya, H., Nagano, K. and Niitsuma, H. (1994): "Precise source location of AE doublet by spectral matrix analysis of triaxial hodogram", Geophysics, 59, 36-45. 58. Tezuka, K. and Niitsuma, H. (1997): "Integrated interpretation of microseismic clusters and fracture

125 system in a hot dry rock artificial reservoir", Expanded Abstr., SEG 67th Ann. Inter. Mtg.. 59. Soma, N. and Niitsuma, H. (1997): "Identification of structures within the deep geothermal reservoir of the Kakkonda field (Japan) by a reflection method using acoustic emission as a wave source", Geothermics 26, 43-64. 60. Soma, N., Sato, K., Niitsuma, H., Tateno, M. and Ohminato, T. (1999): "Estimation of deep geothermal reservoir structure by use of the acoustic emission (AE) reflection method in Kakkonda geothermal field, Japan", Geoth. Resources Council Trans., 23, 301-306. 61. Soma, N., Niitsuma, H. and Baria, R. (2000): "Estimation of Deep Subsurface Structure in European Hot Dry Rock Test Site, Soultz-sous-Forets, France, by Means of the AE reflection Method", Proc. 25th. Workshop Geothermal Reservoir Engineering, Stanford University, Stanford, California, SGP-TR- 156. 62. Nishizawa, M., Niitsuma, H. and Esashi, M. (2000): "Miniaturized downhole seismic detector using micromachined silicon capacitive accelerometer", SEG Expanded Abstr., SEG 70th Ann. Inter. Mtg.. (in press). 63. Dreesen, D. D. and Albright, J. N. (2000): "Models support potential for drilling deep microholes", Oil & Gas J., Jan. 17, 56-61. 64. Comet, F. H., Herm, J., Poitrenaud, H. and Etchecopar, A. (1997): "Seismic and aseismic slips induced by large-scale fluid injections", Pure Appl. Geophys, 150, 563-583.

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127

ACOUSTIC EMISSION IN ROCK MECHANICS

STUDIES

GERD MANTHEI t, JURGEN EISENBLATTER t, THOMAS SPIES tt t GMuG Gesellschaft ftir Materialprtifung und Geophysik, Dieselstrat3e 6a, D-61239 Ober-M0rlen, Germany ttFederal Institute for Geoscience and Natural Resources, Stilleweg 2, D-30655 Hannover, Germany ABSTRACT This paper reviews acoustic emission measurements in rock in a frequency range between 1 kHz and 100 kHz, i.e. between microseismic measurements and the usual acoustic emission range as used for instance in pressure vessel testing. After presenting briefly the historical background, the paper describes some typical examples which demonstrate the methods application for monitoring crack formation and crack growth in mines. The first example refers to crack formation in granite caused by stress redistribution around a newly excavated tunnel. The second example shows microcrack formation in the neighborhood of large underground openings in salt rock. In this case microcracking is due to deviatoric stresses caused by convergence of the rock. In both cases the linear dimension of the region covered by the measurements was about 100 m. A second topic of this paper is the application of acoustic emission measurements to hydraulic fracturing tests in rock. The typical linear dimension of the covered region is about 10 m. Those tests are used for in-situ stress measurements in mines. Examples are shown from measurements in siliceous sandstone and rock salt. These measurements were used to find the orientation of the macroscopic crack plane and to determine the source mechanism (tensile crack or shear crack).

K E Y W O R D S : acoustic emission, rock mechanics, microcracking, source location, source mechanism HISTORICAL BACKGROUND Acoustic emission (AE) or high-frequency microseismic events are generated in rock by crack formation under high stress. This phenomenon provides the basis for a method which is able to detect areas of microcracking or macrocracking or highly stressed zones in rock without any knowledge of the mechanical properties or the state of stress. The "microseismic method" was discovered by Obert (1938)[1] from the U.S. Bureau of Mines, Denver/Colorado, rather by chance. An experiment was being conducted in a lea&zinc mine in order to determine whether the seismic velocity in mine pillars was dependent on stress. These measurements were often disturbed by signals which obviously were generated by the pillar itself. During the late 1930s and early 1940s, Obert and Duvall [2] showed in the laboratory as well as in the field that the rate of such events increased greatly as the structure

128 became more highly loaded. The original monitoring facilities consisted of a geophone (with a Rochelle Salt cantilever beam element in a metal cylinder), a battery operated amplifier and a paper recorder. A set of headphones provided also an audible means of monitoring AE activity. In practice the geophones were located underground in boreholes. This monitoring system was sensitive to acoustic signals in the region of 1000 Hz. Obert and Duvall [3] carried out an extensive study of acoustic emission in nine mines including two deep mines in Canada. Using multi-channel recording systems they were able, by comparison of amplitudes and coincidences, to roughly locate zones of higher acoustic activity. Considerable application of AE measurements in underground mines started in the 1950s and 1960s in Canada, Europe, and South Africa. All applications of acoustic emission associated with geologic materials were initiated in order to study the stability of underground structures like mines and part of mines. The main goal of all these investigations was the prediction of rockbursts and roof falls as well as the indication of areas of higher stress. During the above-mentioned period more sophisticated techniques for monitoring acoustic emission activity, in particular techniques for accurate source location were developed. Considerable is the work of Cook (1963)[4], who developed a monitoring system which was capable of recording signals of up to 16 transducers on magnetic tape for a continuous period of about one day. Cook located the events by re-recording the signals on a multichannel oscilloscope to determine a series of travel time differences of the P- and S-wave onsets, respectively. Astonishing is in particular the good source location accuracy, which he attained without any digital signal recording system and without any computers. By the way, rockburst prediction like earthquake prediction has remained an unsolved problem up to now. By the late 1960s a number of important acoustic emission studies were done. These studies were associated with mining, civil, petroleum and natural gas engineering, and other geomechanics areas. Many studies have been conducted in North American coal mines and European coal mines in Poland, Czechoslovakia, Germany, and Russia. These coal mines were relatively deep (up to 1000m) and therefore the stress conditions were very high and in many cases rockbursts occurred. Successful applications in hard rock mines were done by the U.S. Bureau of Mines in Idaho. These studies were carried out by Blake (1971)[5] and by Blake and Leighton (1970)[6]. They utilized commercially available piezoelectric accelerometers as transducers in the frequency range of 20 Hz up to 10 kHz. The transducers were located in boreholes and the signals were transmitted by cable to an underground monitoring system. The data were used to calculate the source location and relative amplitudes. Other studies in hard rock were done in South Africa and to a limited extent in Europe (Sweden, East Germany, and Poland). The majority of research has been in South Africa (Salmon and Wiebold, 1974)[7]. Further details about geotechnical field applications of AE/Microseismic techniques until the late 1980s are given in a review article by Hardy [8], about both the laboratory and field developments, and in the proceedings of the six Penn State AE/MS Conferences [9-14]. The First International Symposium on Rockburst and Seismicity was held in Johannesburg in 1982 [15]. Six years later in 1988 followed the Second International Symposium in Minneapolis [16]. The Third and Fourth International Symposium took place in Montreal 1993 [17] and in Krak6w 1997 [18].

F R E Q U E N C Y R A N G E S OF A C O U S T I C E M I S S I O N S O U R C E S In principal the mechanism for the origin of seismic sources for instance earthquakes, and the formation of acoustic emission sources are the same, in spite of the fact that magnitudes

129

and frequencies differ by orders of magnitude. Both source types always occur when slip suddenly takes place over a certain area (the so-called focal area) and thereby stored energy is set free. The primary events are therefore characterized by 9 the slip area, 9 the displacement or dislocation at the slip area, 9 the slip velocity, and 9 the stress drop. Table 1 shows a comparison of some parameters of small and large seismic events. The size of the source determines the duration of the primary pulse and thus the upper limit of frequency spectra which corresponds with the reciprocal of the pulse duration. On the other side with increasing frequency, i.e. with smaller source dimensions, the mean attenuation of elastic waves increases and, therefore, the distance of sensors from sources and the size of the covered area decreases. Table 1: Comparison of seismic signal parameters for large events and small events. Parameters duration of the primary pulse frequency range of elastic waves seismic energy frequency of occurrence covered area

Large events long low high rare events large

Small events short high low frequent events small

The frequency range of acoustic emission phenomena in the broadest sense extends from the infrasonic (less than 16 Hz) far into the ultrasonic range (greater than 16kHz). The largest and therefore the longest events as well, namely earthquakes, are found at the lowest end of the scale (Fig. 1). The focus length and displacement of an earthquake can amount to more than several hundred kilometers and up to many meters, respectively. On the other side, the highest ultrasonic frequencies may be generated by events in the microscopic region, for instance by dislocation movement in metals. In this case the source area may extend some micrometers and the displacement (Burgers Vector) is to be measured in nanometers. The ranges of microseismic measurements and usual acoustic emission measurements lie between these frequency ranges of seismology and dislocation movement. Some examples found in the literature of characteristics of microseismic and acoustic emission measurements are given in Table 2. This table shows the monitored area, the linear dimension of the covered area, the type of transducers used, the rate of events, the upper limit of the frequency region of the transducers and the Richter magnitude, if available. The lower microseismic measurement range between 5 Hz and 100 Hz is used for surveillance of whole mines or whole mining regions like the Ruhr coal basin in Germany or the Upper Silesian coal fields in Poland and Czech Republic. In this case seismometers or geophones are utilized. Measurements in the frequency range from 100 Hz up to 5 kHz, so-called high-frequency microseismic measurements, are applied for monitoring smaller mine segments up to linear dimensions of some hundred meters. For these measurements geophones or accelerometers are in use. Above these frequencies begins the frequency range of acoustic emission measurements in rock as used in this review. In this frequency region of about i kHz up to 100 kHz

T a b l e 2: C h a r a c t e r i s t i c s of m i c r o s e i s m i c a n d a c o u s t i c e m i s s i o n m e a s u r e m e n t s in rock. r

r a t e of e v e n t s I typical seismic n e t w o r k distance microseismic m e a s u r e m e n t s (lower f r e q u e n c y ) 113 South 200 km 7 seismometers

authors

monitored area

McGarr et al. [19]

Yaramanci [28]

Witwatersrand Basin, Africa 50 km 4 arrays of 4 seismometers each potash basin in Germany 50 km 6 one-componend seismometers Sudbury Basin, Canada upper Silesian coal basin, Poland 10 km seismometers high-frequency microseismic measurements 7 geophones I km salt mine Asse, Germany 49 uniaxial and 5 triaxial Strathcona mine Sudbury, 200m accelerometers Canada triaxial goephones Sunshine mine, Kellogg, United 1 km States 17 three-component geophones coal mine in the Ruhr district, lOOm Germany acoustic emmsion m e a s u r e m e n t s in r o c k 16 triaxial accelerometers underground research labora- 50m tory, Canada 7 triaxial accelerometers 300 m salt mine Asse, Germany

Niitsuma et al. [29]

Kamaishi mine, Japan

30m

triaxial meters

Ohtsu [30]

underground tunnel, Japan

lOm

17 accelerometers, 17 piezoelectric transducers

Eisenbl~ttter et al. [31]

salt mine Asse, Germany

lOOm

29 piezoelectric transducers

Manthei et al. [32]

salt mine Bernburg, Germany

10m

8 piezoelectric transducers

Ahorner et al. [20] Talebi et al. [21] Mutke et al. [22] Hente et al. [23] Trifu et al. [24] Scott et al. [25] Will [26]

Martin et al. [27]

I

piezoelectric

accelero-

corner I frequency ]magnitude 100 Hz

0 to 3.0

not available 28 in one year 50,000 in 22 years

50 Hz 40 Hz 100 Hz

-2.0 to 2.6 1.5 to 3.0 up to 4.5

209 in two years 1503 in two months

300 Hz 10 kHz

-2.3 to 1.7 0.5

31 in three months

500 Hz

0.5 to 2.5

1000 in four months

400 Hz

not avaliable

3500 locations in 10 months 3407 locations in 14.5 months 234 locations during 4 hydraulic fracturing tests 200 locations during 4 hydraulic fracturing tests 250,000 locations in 11 months 1500 locations during 11 hydraulic fracturing tests

20 kHz

-3.6 to-1.9

10 kHz

-2.3 to-5.7

10 kHz

not available

100 kHz

not avaliable

100 kHz

not available

250 kHz

not

avaliable

131 accelerometers or typical AE sensors are used. In spite of these high frequencies the typical distances between sensors may amount up to 20 to 50m and the covered area may have linear dimensions of 100m.

Acoustic Emission Measurements in Rock Seismology

10-2

1/'~

101~

Microseismic Measurements I-IF 1~]

10I~

1103

Acoustic Emission Measurements

1104

1~

1106

1107

110s

Frequency [Hz]

Fig. 1: Frequency range of acoustic emission measurements in rock as compared with seismology and microseismic measurements (HF: high frequency).

ACOUSTIC EMISSION MEASUREMENTS

IN G R A N I T E

A research program has been undertaken by the Atomic Energy of Canada Limited to develop the technology needed for the safe and permanent disposal of nuclear waste. 4.6-~ler.-.~

Fig. 2: Site of the Mine-by test tunnel with the locations of the acoustic emission tranducers (filled circles) and the orientation of the principal stresses after Martin et al. 1993. [27/ The main objectives were to investigate the response of a rock mass to excavation and to study the long term stability of underground openings. Therefore, in the Underground Research Laboratory (URL) located in Pinawa, Manitoba, a "Mine-by" experiment at 420m depth in the Canadian shield granite has been carried out (Fig. 2). In the experiment a 46 m long tunnel has been excavated in 1 m or 0.5 m increments using drilling and mechanical breaking of the rock stub. The in-situ stresses at the test site have been measured

132

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Fig. 3" Locations of 359 acoustic emission events after excavation of a tunnel increment. The arrows indicate the direction of the m a x i m u m principal stress. [17]

using overcoring and hydraulic fracturing methods. The test tunnel was in direction of the intermediate principal stress or2 in order to maximize the stress concentration around the tunnel (for direction of principal stresses see insert in Fig. 2). One topic of the experiment was to study the rock behavior during excavation utilizing acoustic emission measurements. In addition to mechanical instrumentation around the test tunnel, a network of 16 triaxial accelerometers was installed. The acoustic emission transducers had a frequency response in the range from 50 Hz up to 10 kHz and were located in boreholes (filled circles in Fig. 2) in such a way as to give a focal sphere coverage of the tunnel. During excavation of the tunnel between October 1991 and July 1992, 25,000 events were detected and some 3500 sources were located. Fig. 3 shows only a part of those, namely 359 events, and the contour of the tunnel in cross-section (upper figure) and in longitudinal view (lower figure). The arrows in the upper figure indicate the direction of the maximum principal stress. The figure shows that most events occur in the roof and floor of the tunnel. In these areas of the tunnel the maximum tangential stresses occur. The zones of maximum acoustic emission event density correspond with breakout

133 notches which formed in the roof and in the floor of the tunnel, after excavation began, and which deepened by spalling during ongoing excavation. The notch formed orthogonal to the direction of maximum principal stress al. The spalling planes were parallel to al and a2 and normal to the direction of the smallest principal stress a~. Post test analysis using the moment tensor method which was applied to 37 strong events located at the roof, pointed out that most of the events show a significant non-shear component (see also Ming Cai et al. [33]). ACOUSTIC EMISSION MEASUREMENTS

IN SALT ROCK

Like granite, rock salt is a favorable rock for underground disposal and storage of radioactive waste as it is capable of creep deformation without occurrence of fracture in a wide range of the conditions of state. Nevertheless, in presence of high deviatoric stress microcracks are generated. Microcracking touches the integrity of the rock so that permeability might increase. Like rock salt, anhydrite is a major constituent of salt deposits. It exhibits elastic-brittle material behavior and has a much higher strength than rock salt. Observations underground indicate that anhydrite often is a joint rock. From acoustic emission measurements Spies and Eisenbl~ttter [34] found evidence that reactivation of such joint planes is the mechanism of crack generation in anhydrite.

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emission network was installed (levels L1 to L3). [35] A network of 24 acoustic emission transducers was installed in a salt mine in northwestern Germany. Fig. 4 shows a vertical section as a sketch of the geological conditions and the geometry of the rooms. The detailed geological structure is more complex. The rooms have been mined in rock salt of the Zechstein Leine series (Na3) separated from rock salt of the Stassfurt series (Na2) by anhydrite (A3), salt clay (T3) and the potash seam (K2). The rooms at the upper three levels L1, L2 and L3 were monitored by the network. The covered volume amounts to about 100m • 100 m • 150m extending over

134 three mining levels in a depth of 400 m below ground 9 In this area rooms have been mined 60 to 70 years ago. The acoustic emission transducers are installed in 3 m to 20m long boreholes. The central unit of the acoustic emission system consists of a transient recorder monitoring the data stream of the 24 acoustic emission sensors in a frequency range from 1 to 100 kHz, a personal computer, and a modem for telecommunications. In case of an acoustic emission event the computer transfers the signals from the recorder, calculates event parameters and locates the event automatically using P- and S-waves onsets 9 The maximum amplitudes of the signals and the location of the events are used to calculate a measure of strength of the source analogue to the magnitude in seismology. As an example of acoustic emission activity near the boundary of rock salt and anhydrite, Fig. 5 shows the locations of events marked by dots in a ground plan at the upper level. The locations are of the depth interval ranging from 6 m below to 6 m above the upper level 9 Fig. 5 shows the contours of the cavities at the upper level as dashed lines, those of the intermediate level below as continuous lines 9 In addition, Fig. 5 shows also the geological boundaries obtained by intensive geological and geophysical mapping 9 Anhydrite (A3) and salt clay (K2) are not continuous layers but are broken into blocks by salt tectonics. The locations have been determined in a time interval of three years. Only stronger acoustic emission events are displayed to reduce the large number of events. From test measurements it was concluded that the location error can reach a value of 3 m far away from the network, but in most cases the error is smaller, typically I m. Most of the events occur at the contours of the cavities but distinct activity can also be observed rarer from the rooms near the boundary of rock salt and anhydrite. The activity in the rock salt in front of the boundary is spatially dispersed 9 The acoustic emission events along the contours characterize the process of dilatancy or microcracking in the rock salt which is spatially distributed over larger volumes. ~..I

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and locations of acoustic emission events recorded in a period of three years. [35]

135

In the lower part of Fig~ 5 a linear feature can be noticed along the boundary of rock salt and anhydrite. This is an accumulation of acoustic emission events that were recorded in a short time interval of some days. Another prominent example of this type of activity is shown in Fig. 6. It is a ground plan of the third level showing the contours of Room 23 and a pillar in a geographic coordinate system (north direction upwards). Locations of the events from a depth interval of 30 m from the floor to a level above the roof of Room 23 are shown. The locations of the acoustic emission events in Fig. 6 were determined during a time interval of half a year. The ring shaped cluster (marked by I) with diameter of about 8 m occurred about 20m away from Room 23 in horizontal direction. The activity began suddenly with a high event rate which decreased continuously during the following two days. A second cluster (marked by II) also exhibits the shape of a ring but the event rates varying only moderately in the whole time interval.

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136

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contours of Room 2~ of level L~, revised geology and locations of acoustic emission events (time interval half a year). [35] view indicates that the microcracks outline the discontinuous growth of a macrofracture. This fracture is merely 3 cm from the anhydrite-salt boundary. ACOUSTIC EMISSION MEASUREMENTS TURING TESTS IN ROCKS

DURING

HYDRAULIC

FRAC-

During a hydraulic fracturing experiment the hydraulic pressure in a sealed volume of a borehole is increased up to the initiation of a fracture which propagates into the rock. At the end of the 1940s the hydraulic fracturing technique was applied first to raise the oil or gas output by increasing the permeability of rock. Based on the work of Hubbert and Willis (1957)[36] and of Kehle (1964)[37] Fairhurst (1964)[38] suggested to use hydraulic fracturing for stress measurement in rocks. In all applications, the measurements of dimension, shape and orientation of the fractures are of upmost importance when determining the in-situ stress state. One possibility is the overcoring of the fracture borehole. But this method is limited in borehole diameter and very expensive, in particular for hydraulic fracturing tests in greater depths. A suitable method to measure the crack orientation and extension is the three-dimensional location of AE events. A lot of papers show the application of AE during hydraulic fracturing tests on rock samples as well as in the field for example Lockner and Byerlee (1977) [39] and Eisenbliitter (1988)[40], respectively.

137

Hydraulic Fracturing Tests in Siliceous S a n d s t o n e Ohtsu [30] carried out acoustic emission measurements during in-situ hydraulic fracturing tests at an underground working tunnel in Imaichi/Japan. The test site was composed of siliceous sandstone. In order to monitor AE events a total number of 34 AE sensors were installed in boreholes in a distance of one meter from the injection well. The sensors were 17 accelerometers with resonance frequency of 25 kHz and piezoelectric transducers with resonance frequency of 65 kHz. Fig. 8 shows the configuration of only eight AE sensors of accelerometer type which were utilized for source location and source type determination. The amplitude calibrated sensor recorded the first motion very clearly. Other sensors more /"

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transducers (indicated by filled circles) were located in four boreholes around the central injection well for the injection test in 5.68 m. [30] distantly placed from the injection well were very unsensitive and therefore their signals were fairly noisy. During four hydraulic fracturing tests at 3.88 m, 5.68 m, 6.03 m and 8.53 m borehole depths approximately 200 AE events were located. Fig. 9 shows that clustered events appear periodically along the injection well which were attributed to the fracture planes. The events from the injection test in 5.68 m depth overlap those of the injection test at 6.03 m. The solid lines indicate the fracture planes found by AE locations. The fracture planes are oriented transverse to the injection well and inclined approximately by angles of 40 ~ to 45 ~ with respect to the horizontal direction. The deepest cluster is inclined by an angle of 25 ~. According to visual inspection of the injection well prior to hydraulic fracturing, the orientation of existing natural joints varied from 40 ~ to 60 ~.

138 Elevation view

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Fig. 9" Located A E events in the elevation view for hydraulic fracturing tests in 3.88 m, 5.68 m, 6.03m and 8.53 m (location of the injection is marked by means of horizontal arrows)./30/ Ten acoustic emission events which were recorded during the fracturing test in 5.68 m borehole depth, were analyzed by using the moment tensor method. The moment tensor analysis pointed out that the orientation of microscopic crack planes was in good agreement with the direction of the macroscopic fracture plane. AE events of shear type occurred more in the center of the macroscopic fracture planes whereas those of tensile type are located farther from the injection well near the crack tip. From these AE measurements it was concluded that at first tensile cracks were generated at the week seam of joints, and then shear cracks occurred on the open joints. The orientation of tensile cracks were perpendicular to the macroscopic fracture plane and the events of shear type were parallel to the macroscopic fracture plane.

139

Frac borehote Fig. 10: Test site of the hydraulic fracturing series at the ~20-m level in the vicinty of huge

chambers. [32] Hydraulic Fracturing Tests in Salt Rock In the salt mine Bernburg in Germany hydraulic fracturing tests have been performed aimed at investigating the stress state in rock. The test sites of the hydraulic fracturing tests are in the Leine rock salt Na3. Fig. 10 shows the situation of the test site in a perspective view. In the vicinity of the fracturing site an ensemble of huge chambers of 120 m length, 25 m width and 30 m height can be seen, which were excavated thirty five years ago. In this region a number of further measurements, e.g. extensometer and convergence measurements were carried out. The horizontal injection well had been made from the wall of the gallery, which cuts a barrier pillar. The borehole transducers were located in four further boreholes of 10m length (98 mm diameter) around the central injection well. In these AE sensors could be placed in various depths. The eight transducers were placed in such a way that in a depth of 10m a nearly quadratic array of about 6.5 m diagonal length was generated. The maximum transducer sensitivity was in direction of the injection well. The piezoelectric transducers had a resonance frequency of about 50 kHz. The signals were digitized with a sampling frequency of 500 kHz and 12 bit amplitude resolution. The positions of the located events are presented in Fig. 11 in projections to the three coordinate planes (x-y plane: top view; x-z plane and y-z plane: lateral views). The figure shows the results of the fracturing test series. During six tests in borehole depths from l m to 9 m 814 AE events could be located with a residual error below 10cm. This figure also shows the coordinate axes with origin at the fracture borehole. It can be seen that clusters appear periodically along the injection well which are to be attributed to the fracture planes. The orientations of the fracture planes as indicated by the above AE measurements were compared with independent stress calculations. These FEM calculations based on long-term surface subsidence measurements and

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141 measurements of underground convergence. Fig. 12 shows the results of these stress calculations in a vertical cross section through the test site (upper figure). The horizontal line represents the injection well. A part of the contour lines of the huge chamber can be seen. The crosses represent the orientations of the principal stresses around the test site. It can be seen, that in the vicinity of the free surfaces of the rooms (wall, floor and roof) only the stress component parallel to the surface exists. In larger distances from the surfaces the second stress component appears. Fig. 12 shows at the bottom the orientation of the principal stresses along the injection well and the located events (dots) in the projection to the y-z plane as seen in Fig. 11. The orientation of the fracture planes as measured by acoustic emission agrees remarkably well with the orientation of the calculated principal stresses. It can be seen that the direction of the fracture planes appears to coincide with the the maximum principal stress. This fact is observed for all fractures. These results confirm Hubberts and Willis' assumption that the fractures are normal oriented to the minimum principal stress.

CONCLUDING

REMARKS

In acoustic emission there exists up to now no commonly accepted measure of event magnitude like the (Richter) magnitude in seismology. In the case of high-frequency microseismic measurements and acoustic emission measurements in rock as well, there are often listed nominal magnitudes. Such nominal magnitudes can also be found in Table 2. In order to get such magnitudes, usually seismic energy or seismic moment is measured and the seismic magnitude is calculated from seismological empirical formulas like the famous Gutenberg-Richter relation [41] between seismic energy and magnitude. Also admitting that only a correlation of orders of magnitudes is needed, this approach seems to be very uncertain for two reasons: 9 The formulas are empirically established for earthquakes only. Extrapolation over so many orders of magnitude seems doubtful. 9 The formulas are applicable for specific types of sources, namely double-couple events. In microseismics and acoustic emission as well, this seems not to be the prevalent source type. On the contrary, very often sources of mixed mode or of pure tensile or CLVD type are found by momentum tensor analysis. In conclusion, more quantitative acoustic emission measurements on well-defined source types are needed for further progress in this field. The work of T. Kishi on fracture mechanical specimens of different materials could be exemplary for such measurements on rock specimens and in an enlarged scale.

142 REFERENCES

10.

11.

12. 13. 14. 15. 16. 17. 18. 19. 20. 21. 22.

Obert, L. (1977). In: Proc. 1st Conf. on Acoustic Emission//Microseismic Activity in Geologic Structures and Materials, pp. 11-12, Hardy, R. H., Leigthon, F. W. (Eds). Trans Tech Publications, Clausthal. Hardy, H. R. (1977). In: Proc. 1st Conf. on Acoustic Emission//Microseismic Activity in Geologic Structures and Materials, pp. 13-31, Hardy, R. H., Leigthon, F. W. (Eds). Trans Tech Publications, Clausthal. Obert, L., Duvall,W. I. (1957). U.S. Bureau of Mines Bulletin 573. Cook, N. G. W. (1963). In: Proc. 5th Syrup. on Rock Mechanics, pp. 493-516, Fairhurst, C. (Ed). Pergamon Press New York. Blake, W. (1971). Report U.S. Bureau of Mines, TPR 39. Blake, W., Leighton, F. (1970). In: Proc. 11th Syrup. on Rock Mechanics, pp. 429-433., AIME, New York. Salmon, M. D. G., Wiebolds, G. A. (1974). In: Rock Mechanics 6, 141. Hardy, H. R. (1989). In: J. Acoustic Emission, 8, 4, 65. Hardy, n. R., Leighton, F. W. (Eds) (1977). Proc. 1st Conf. on Acoustic Emission//Microseismic Activity in Geologic Structures and Materials. Trans Tech Publications, Clausthal. Hardy, H. R., Leighton, F. W. (Eds) (1980). Proc. 2nd Conf. on Acoustic Emission//Microseismic Activity in Geologic Structures and Materials. Trans Tech Publications, Clausthal. Hardy, H. R., Leighton, F. W. (Eds) (1984). Proc. 3rd Conf. on Acoustic Emission//Microseismic Activity in Geologic Structures and Materials. Trans Tech Publications, Clausthal. Hardy, n. R. (Ed) (1989). Proc. 4th Conf. on Acoustic Emission//Microseismic Activity in Geologic Structures and Materials. Trans Tech Publications, Clausthal. Hardy, H. R. (Ed) (1995). Proc. 5th Conf. on Acoustic Emission//Microseismic Activity in Geologic Structures and Materials. Trans Tech Publications, Clausthal. Hardy, H. R. (Ed) (1998). Proc. 6th Conf. on Acoustic Emission//Microseismic Activity in Geologic Structures and Materials. Trans Tech Publications, Clausthal. McGay, N. C., Wainwright, E. H. (Eds) (1984). Proc. lnd. Symp. Rockbursts and Seismicity in Mines, So. Afri. Inst. Min. and Metallurgy, South Africa. Fairhurst, C. (Ed) (1990). Proc. 2st. Symp. Rockbursts and Seismicity in Mines, Balkema, Rotterdam. Young, P. R. (Ed) (1993). Proc. 3rd. Symp. Rockbursts and Seismicity in Mines, Balkema, Rotterdam. Gibowicz, S. J., Lasocki, S. (Eds) (1997). Proc. 4th. Symp. Rockbursts and Seismicity in Mines, Balkema, Rotterdam. McGarr, A. (1990). In: Rockbursts and Seismicity in Mines, pp. 245-248, Fairhurst, C. (Ed). Balkema, Rotterdam. Ahorner, L., Sobisch, H.-G. (1988). Kali und Steinsalz (in German) 2, 38. Talebi, S., Mottahed, P., Pritchard, C. J. (1997). In: Rockbursts and Seismicity in Mines, pp. 117-120, Gibowicz, S. J., Lasocki, S. (Eds). Balkema, Rotterdam. Mutke, G., Stec, K. (1997). In: Rockbursts and Seismicity in Mines, pp. 213-217, Gibowicz, S. J., Lasocki, S. (Eds). Balkema, Rotterdam.

143 23. Hente, B., Quijano, A., Diirr, K. (1989). In: Proc. ~th Conf. on Acoustic Emission/Microseismic Activity in Geologic Structures and Materials, pp. 171-179, Hardy, R. H., Leigthon, F. W. (Eds). Trans Tech Publications, Clausthal. 24. Trifu, C.-I., Shumila, V., Urbancic, T. I. (1997). In: Rockbursts and Seismicity in Mines, pp. 295-298, Gibowicz, S. J., Lasocki, S. (Eds). Balkema, Rotterdam. 25. Scott, D. F., Williams, T. J., Friedel, M. J. (1997). In: Rockbursts and Seismicity in Mines, pp. 311-315, Gibowicz, S. J., Lasocki, S. (Eds). Balkema, Rotterdam. 26. Will, M. (1978). In: Proc. 2nd Conf. on Acoustic Emission/Microseismic Activity in Geologic Structures and Materials, pp. 191-209, Hardy, R. H., Leigthon, F. W. (Eds). Trails Tech Publications, Clausthal. 27. Martin, C. D., Young, R. P. (1993). In: Rockbursts and Seismicity in Mines, pp. 367371, Young R. P. (Ed). Balkema, Rotterdam. 28. Yaramanci, U. (1992). GSF Report (in German) 32. 29. Niitsuma, H., Nagano, K., Hisamatsu, K. (1993). J. Acoustic Emission 11, 4, S1. 30. Ohtsu, M. (1991). J. Geo. Res. 96, B4, 6211. 31. Eisenbl~itter, J., Manthei, G., Meister, D. (1998). In: Proc. 6th Conf. on Acoustic Emission/Microseismic Activity in Geologic Structures and Materials, pp. 227-243, Hardy, R. H. (Ed). Trans Tech Publications, Clausthal. 32. Manthei, G., Eisenbl~itter, J., Salzer, K. (1998). In: Proc. 6th Conf. on Acoustic Emission/Microseismic Activity in Geologic Structures and Materials, pp. 245-267, Hardy, R. H. (Ed). Trails Tech Publications, Clausthal. 33. Ming Cai, M., Kaiser, P. K., Martin, C. D. (1998). Pure Appl. Geophys., 153, 67. 34. Spies, T., Eisenbl~itter, J. (1998). In: Proc. 3rd Intern. Conf. on Mech. of Jointed and Faulted Rock, pp. 405-410, Rossmanith, H. (Ed). Balkema, Rotterdam. 35. Spies, T., Eisenbl~itter, J. (1999). In: Proc. of Intern. Congress on Rock Mech., pp. 1071-1074, Vouille, G., Berest, P. (Eds). Balkema, Rotterdam. 36. Hubbert, M. K., Willis, D. G. (1957). Trans. AIME 210, 153. 37. Kehle, R. O. (1964). J. Geophys. Res. 69, 259. 38. Fairhurst, C. (1964). Rock Mechn. and Eng. Geol. 2, 129. 39. Lockner, D., Byerlee, J. D. (1977). J. Geohys. Res. 82, 2018. 40. Eisenbl~itter, J. (1988). In: Acoustic Emission, pp. 291-303. Deutsche Gesellschaft fttr Metallkunde e.V., Oberursel. 41. Gutenberg, B., Richter, C. F. (1956). Bull. Seis. Soc. Am. 46, 105.

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145

POST-FAILURE MICROMECHANISMS IN SHEAR BANDING OF ROCK JOSEPH F. LABUZ & FERNANDA C. S. CARVALHO Department of Civil Engineering, University of Minnesota Minneapolis, MN 55455 USA

ABSTRACT Laboratory and field observations indicate that failure of intact rock is associated with a rupture surface or shear band, where deformation is concentrated in a narrow zone; displacements occur with decreasing stress within the shear band, while outside the band the material remains more or less intact. When localization of deformation occurs within a rock structure, the global loaddisplacement behavior appears as a softening response. Therefore, understanding the initiation and propagation of the shear band is of fundamental concern, as this mechanism is associated with phenomena such as rock bursting and progressive failure. The study of localized failure under controlled conditions can accomplished within a laboratory setting with the University of Minnesota Plane-Strain Compression Apparatus (U.S. Patent 5,063,785). The device provides an opportunity to compare the displacements from microseismic events with the global measurements. An acoustic emission system was used to monitor the softening response of a sandstone in plane-strain compression. The recorded waveforms were analyzed for event location and source characterization. The complicated deconvolution process was eliminated by using a simplified calibration procedure. The moment tensors of the events associated with softening were evaluated, and the source mechanisms were determined. The sources of the post-peak events were characterized as being caused predominantly by slip in the direction of the global shear band. The displacement of each acoustic emission was estimated from the moment tensor by assuming a constant strain drop of 10.4 over the slip area. Within a certain post-peak regime where 110 events were captured, the AE displacement was O.Ollmm, while globally 0. 033ram was measured. KEYWORDS: acoustic emission, displacement discontinuity model, plane strain testing, post-failure response, sensor calibration, source characterization INTRODUCTION A noticeable feature of slope and underground failures in rock is the appearance of slip surfaces or shear bands, the characteristics of which are associated with deformation being concentrated in narrow zones and the surrounding material remaining intact [ 1,2]. The concept of a distinct failure plane that forms a wedge of material allows a direct examination of force equilibrium of the system, and is central to most stability calculations. The problem to be considered here is the initiation and propagation of the shear band itself. Much less attention has been given to shear-band development because of the difficulty of measuring the response in a laboratory

146 environment. In particular, the softening observed in going from the peak to residual strengths imparts a behavior often associated with the progressive nature of failure [3]. Furthermore, an important concem in rock mechanics is fault or joint slip manifested as rockbursts. A number of parameters have been developed to characterize this dynamic process, such as the fundamental quantity called the moment tensor [4]. On the laboratory scale, acoustic emission (AE) is the term used to describe stress waves emitted by the rapid release of energy from localized sources within a solid material. Such a localized source acts as a center of radiation for elastic waves that propagate throughout a specimen to be detected by a transducer and recorded as an acoustic emission signal. Acoustic emission has the potential to be a technique for the study of softening response, as the AE signal contains information about the AE source, which includes magnitude, orientation, and mechanism; as well as the medium through which the wave propagates, and the recording system, including the transducer, couplant, and electronics. In order to obtain information about the source, quantitative methods to separate these three effects from the AE signal have been developed [5-7]. Assuming that the governing wave equations of the propagation medium are linear, and the coupling of the transducer and the structure are described by linear boundary conditions, then the measured AE signal is the convolution of the source function, structure function (dynamic Green's function), and recording system function. Quantitative analysis of acoustic emissions is an elastodynamics problem that allows one to extract, from the recorded waveforms, source parameters such as crack orientation and type of mechanism that generated the source. For tractability of solutions, this process is usually subdivided into smaller problems. In general, there are three important aspects to be studied: source representation, wave propagation and transducer response. In the first problem, which is concerned with the source model, the source element is isolated such that the representation is only a function of the discontinuity that generated it and the properties of the medium in the immediate vicinity. It is necessary, however, to make assumptions about the type of source discontinuity that will be considered and its time dependency. When considering the wave propagation from an acoustic source, the material is assumed to be linearly elastic, homogeneous and isotropic. This allows the radiation problem to be represented by an existing analytic Green's function that corresponds to a buried pulse in an infinite material. Although the elastic properties of the material do change as damage accumulates, this effect is not considered for characterization purposes. Since the wavelengths of the AE signals are usually much larger than the size of the sources, radiation patterns should not be significantly affected by these inhomogeneities [8]. Source parameters are recovered by relating them to the components of a moment tensor that characterizes each event. This is done by minimizing the error between measured displacements at each transducer and the displacements generated by a seismic moment at the source. In this sense, transducer calibration becomes an essential part of the problem. Once calculated, the seismic moment tensor is then decomposed into different parts, each one representing a particular type of discontinuity such that their relative contributions can be compared. Michaels et al. [9], Scruby et al. [6], and Shah and Labuz [7] assumed the AE source to be characterized as force dipoles, which can be combined in form the moment tensor. Shah and Labuz [7] evaluated the seismic moment tensor by performing the full deconvolution based on the assumption that the piezoelectric transducer is linear and responds only to the normal displacement (or velocity) of the surface on which the transducer is mounted. Scruby et al. [6] and Ohtsu [10] assumed that the AE signal is proportional to the normal displacement. Lockner et al. [11]

147 conducted triaxial tests on granite with monitoring of acoustic emission. The acoustic energy release rate was compared with the energy release rate calculated from global measurements by assuming that the radiated acoustic emission is proportional to an equivalent event amplitude. A reasonable agreement between these two energies was shown. In this paper, the softening response of a sandstone was observed using a special plane-strain apparatus [12], where kinematic constraints are removed and accurate measurements are realized. The University of Minnesota plane-strain apparatus combines the features of a direct shear test, such that the stress and displacement characteristics of the shear band can be measured directly, and a constitutive plane-strain compression test, such that the twodimensional material behavior can be evaluated. As shown in Fig. 1, a stiff device has been devised, with the underlying concept being the unrestricted formation of the shear band, as opposed to conventional triaxial experiments where sliding along the failure plane is inhibited by end-platen restraint. The global behavior was captured using a closed-loop, servo-hydraulic testing system. The softening response was monitored by the AE technique. The moment tensor of each event was evaluated based on the sensitivities of the sensors, and the source mechanisms of normal and tangential displacements were determined. The slip from each AE was estimated and the sum of the slip displacements was compared with the global measurement.

Axial Force (Upper Load Cell)

Top Loading Plate Pressure Vessel ~ ,

,,<

,J J

Specimen

J

Lower Load Cell

J

S F

Linear Bearing Biaxial Frame

Fig. 1. Sketch of the plane-strain apparatus EXPERIMENTAL SETUP A plane-strain compression apparatus (Fig. 1) was developed at the University of Minnesota [12]. The apparatus is unique because it allows the failure plane to develop and propagate in an unrestricted manner by attaching the upper platen on a low friction linear beating referred to as the sled. Plane-strain deformation is enforced by a stiff biaxial frame, whereby the specimen is wedged against a thick-walled steel ring. For the maximum allowable confining pressure of 20MPa and a~al load of 500kN, the lateral strain is on the order of one percent of the axial strain for the rock tested (E-2GPa). The lateral strain can be monitored by a set of strain gages, which allows for an estimate of the average intermediate principal stress. Prismatic specimens are used, with the size range from 75-100mm in height, 30-40mm in thickness, and lOOmm in width. The axial load is measured by load

148 cells located at the bottom of the specimen and outside the pressure chamber. The axial displacement, the lateral displacement of both surfaces of the specimen exposed to confining pressure, and the lateral displacement of the top platen in contact with the sled are measured by LVDTs. The two surfaces of the specimen exposed to confining pressure are sealed by a polyurethane coating; metal targets glued to the specimen provide firm contact points for the lateral LVDTs. The four surfaces in contact with polished-steel platens are covered with a stearic acid lubricant to reduce the friction between the platens and the specimen. An acoustic emission system was designed around CAMAC (Computer Automated Measurement And Control) based, high speed, data acquisition equipment manufactured by LeCroy Corporation (Chestnut Ridge, New York). The instrumentation that is housed within the crate consists of four, two channel modular transient recorders (LeCroy model 6840) with a sampling rate of 20 million points per second (50 nanoseconds between points) and 8-bit resolution. The LeCroy 6010 controller interfaces with a 486 microcomputer via a GPIB cable and National Instruments AT-GPIB card. The LeCroy 6010 controller has a Motorola 68020, I OMHz microprocessor. A segmentation code, downloaded into 6010, permits data acquisition until 128kbytes of digitizer memory are filled, before the data are transferred to the host computer. This means 64 events with 2kbytes per event (about lOOps) can be captured as fast as they arrive. The transfer is accomplished using direct memory access, for a rate of 0. 6Mbytes/s. The total transfer time for 64 events including the storage time on the computer is found to be four seconds. Before the experiments, the piezoelectric (Physical Acoustics model $9225) transducers and preamplifiers were calibrated by breaking a pencil lead (0.5mm diameter) at known positions on the specimen to produce a point-step force. The load for breaking the pencil lead was measured with a load cell (sensitivity of 2.2N/mV), and the normal displacement was calculated from the analytic solution for a point-step force applied to a half space. Only the P-wave component was considered, and it was ensured that reflected waves were not distorting the signal. The objective of calibration is to relate the transducer response to mechanical disturbances taking place at their location. The true response of a sensor, however, is a function of the disturbance generated by the source event modified by the presence of the transducer. Since the undisturbed field is of interest, the loading effect becomes part of the representation. Several s i m p ~ g assumptions must be made in order to obtain a practical solution. In general, it is assumed that the wave modes are uncoupled and can be analyzed separately. Transducers are assumed to be vertically sensitive [13]. Also, it is desired to have transducers that are very small compared to the characteristic dimensions of the tests such that their size can be disregarded in the analysis. If the AE receiving system, including the piezoelectric transducers, is assumed to be linear responding only to the displacement (or velocity) normal to the plane on which the transducer is placed, then mathematically, the output voltage is given by V(O - Tl * Un + T2 * vn = T , v,

(1)

where u~ and v, are normal displacement and velocity at the transducer, V is the measured voltage, T1, I"2 and T are receiving system functions and 9represents a convolution. The final dependence of the measured voltage on the displacement is unnecessary, because it is indirectly included by the velocity dependence. Thus, the response of the receiving system is characterized by a single transducer transfer function T(t), which represents the response of the receiving system for a unit velocity pulse at t-O. Michaels et al. [ 10] have shown that deconvolution techniques can be used to recover source functions for arbitrary sources based on the transfer functions estimated from a reference source, such as a glass capillary break. Thus, the transfer functions obtained for piezoelectric transducers are completely independent of the source characteristics and are only a function of the recording system and coupling effect.

149 The main purpose of transducer calibration is to establish a direct correlation between the mechanical disturbance taking place at its location and the corresponding voltage output. If AE source characterization is to be performed, it is required to relate the transducer output to displacements at the surface. For each event recorded during a test, a deconvolution process is necessary to estimate displacements at each transducer location, which can become time consuming. Some authors [6,10] have avoided this process by directly relating transducer output to vertical displacements at the surface through a scalar sensitivity parameter. This is usually done by assuming that the first peak of the displacement calculated from the analytic solution, which is due only to the P-wave, is directly proportional to the first peak of the recorded signal. This relation can be written as (2)

ue - SA • V P

where up is the amplitude of the first displacement peak, Vp is the amplitude of the first voltage peak, and SA is the amplitude sensitivity parameter, given in meters/mV. The main problem associated with this assumption is that, in the case of piezoelectric transducers, their response does not seem to be related exclusively to displacement or velocity, but possibly a combination of both. In addition, the rise times of the first peak of the displacement curve and of the recorded signal do not always correspond, causing the sensitivity parameter to vary considerably for different calibration distances. Figure 2 shows the sensitivities calculated for one transducer. In the graphs, the amplitude sensitivity SA increased approximately linearly with the distance between source and receiver for distances ranging from approximately 20-85mm. In another experiment, two calibration curves were obtained for the same transducer from separate tests; the only variable in comparing the two sets of data was the coupling condition between the transducer and the specimen surface. By changing the coupling, the magnitude of the sensitivity values changed considerably. The variation of the sensitivity parameter with distance has also changed with coupling since the two curves have different slopes. These results indicate that the amplitude sensitivity, for a specific coupling condition, is a function of both the rise time of the vertical displacement (implicitly taken into account by the distance) and its magnitude, which is also a function of the break force. 1.5E-12

!

>

Calib2 1.2E-12

.....

~

9.0E-13

- ..............

=

6.0E-13

-

3.0E-13

- ...............

I [ ............ :...................

T3

t

........

<

i .............. i ,,

...........

m "CJ

~

---i-, , , ,

........

A" - -

i ...............

i. . . . . . . . . . . . . . .

'

" ',

~

-i . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

i- . . . . . . . . . . . . . " I

i ..............

::i

::

i..............

i ..............................................

1 1

i

O.0E+00 0.00

0.02

0.04 Distance

0.06

0.08

0.10

(m)

Fig. 2. Amplitude sensitivities from glass capillary breaks The linear variation of the amplitude sensitivities with distance seems to be consistent with the variation of rise times. Figure 3 shows the variations of the rise times of calculated displacements and calibration signals with distance for different transducers. The rise time of the displacements estimated from the analytic solution have a consistent linear variation with distance, and are

150 independent of the break force. The rise times of the recorded signals, however, seem to be approximately constant with distance for all transducers tested. In view of the different time dependencies of the analytic and experimental peaks with distances, another sensitivity parameter can be introduced as L S~ = ~ L

(3)

where T~ and Td are the rise times of the recorded signal and calculated displacements respectively and Sr is the rise time sensitivity. Results obtained with four different piezoelectric transducers showed a consistent dependency of rise time sensitivities on the normalized frequency of the signals for all transducers, even though amplitude sensitivities could be different by a factor of almost ten. It is also possible to use a unique relationship between rise time sensitivity and normalized frequency, which is valid for all material types. This suggests that the time dependency of the signals, contrary to amplitude responses, are independent of the transducer type or coupling with the specimen surface. Rise time sensitivities are also independent of displacement magnitudes. Amplitude sensitivity parameters for all transducers tested exhibited an approximately linear variation with the source-receiver distance. Dependency of the amplitude sensitivities on displacement magnitude was considered by scaling the sensitivities with respect to the break force. Scaled sensitivities were shown to have a linear variation with normalized frequency of the signals.

3'0 I ] * calib5 [ 2.51 .... ~ 9 calib4 ] ! "~'~92"OJ 1.5

]

__~.__ i ~-

calib2 l _ i ~ i i i i i i i i i e m e n t

i ..K4(! i calibration -~ 1.0 ............. i ---yK~"..... i ............. i ........ signal -

,~~,t~

.-it

9

~'," 9 A": 9 "A:'~ .i 0.5 ..................................... x-,~.........................

0.0

0

0,03

I

0.06 0.09 Distance (rn)

0.12

0.15

Fig. 3. First peak of the analytical displacement and the calibration signal SOURCE REPRESENTATION AE is associated with a sudden displacement in the material. Assuming a point-source model, the localized displacement discontinuity (a microcrack) can be given by the tensor ~: -

+

(4)

where b is the displacement vector, n is the microcrack normal, and AA is the microcrack area. The methods used to characterize AE come from concepts developed by seismologists to study earthquakes, based on the representation of displacement discontinuities within the material as equivalent sets of force dipoles or moments [14]. The moment tensor M is related to the displacement discontinuity tensor by

151

(s)

A/lij -- Cijkl ~.lk l = CijklbkFll ~

where C is the material's stiffness tensor. Under its eigenvector coordinate system, the moment tensor for an isotropic material can be written as M~ = A ~ + 2ktN~

(6)

where )1, and It are the Lame constants, ~ and Mk are the eigenvalues of the tensors ~ and M0, and ~/;;=~/l+~t2+gt~. For isotropic solids, the eigenvector coordinate systems of M and N coincide. The solution of the eigenvalue problem for ~ yields

f

t

/if1 = [bIAAcos 2 a

lb[z~/= ~ 1 - ~ 3

~2 = 0 ~3 = -Ibl AA sin2 a

(7)

o=+tan

where the crack mode angle (x is the angle between the normal to the displacement discontinuity n and the first eigenvector of M. The angle 2a is measured from the normal n and the displacement vector b. Obviously, if a=0 ~ then it is a pure tensile mechanism; if a=45 o, it is pure shear. Note that the intermediate value of ~is zero, so the moment tensor must have the form =

2,

(Mr +M3 )

(8)

The displacement vector u at any point X and time t due to a suddenly produced displacement discontinuity in an infinite domain is conveniently given in terms of the seismic moment [4]: ui(X,t)

(9)

= M j k * G~j,k

where G~,~is the derivative of a Green's function that describes the elastodynamic problem, which for AE corresponds to a point-step force at point Xo and time to in an infinite domain. Since only the first peak of the displacement is considered in this analysis, the analytic solution to u for a radial displacement at a point due to the P-wave is used [4]. Knowing the displacement at each transducer, the moment tensor of the source, which is assumed not to depend on time, can be obtained by minimizing the sum of the error E in the displacement: E = Z

k=l

u ~ - u.

M

Pq' X

+r

1.11 +

~"

III+

@- + 1 ) 2

(10)

where uk is the normal displacement measured at the k-th transducer, u,=ujni is the displacement caused by the moment tensor of the source, n is the normal to the plane of the transducer, I, II, llI are the invariants of the moment tensor, and ~, is the Lagrange multiplier. Note that the moment tensor components are calculated subjected to the restriction that the second eigenvalue of ~ must be zero to be compatible with the model of displacement discontinuities as the source mechanism. The components of the moment tensor are calculated so as to minimize the error between the measured and calculated displacements, subjected to the imposed restriction. The problem is solved by using the Levenberg-Marquardt method to minimize the error.

152 EXPERIMENTAL RESULTS A soft rock, sandstone with an unconfined compressive strength of I OMPa and a Young's modulus of 2GPa, was tested under biaxial conditions using the University of Minnesota PlaneStrain Apparatus. Fig. 4 is the typical behavior of the axial load, lateral displacements, and linear bearing displacement as a function of average axial displacement for the rock at a confining pressure (o-.3=15MPa). The difference between the axial load measured by the upper load cell and lower load cell was very small, which indicated that the stearic-acid lubricant successfully reduced wall friction. The displacement measured by the two lateral LVDTs changed by the same amount in the elastic region, which meant that the deformation of the specimen was uniform prior to failure. The load-displacement-AE behavior of the material is shown in Fig. 5. The markers along the top curve represent the AE events recorded by eight piezoelectric transducers and the bottom curve shows the corresponding cumulative number of events. A total of 1158 events were recorded during the test, the first 459 corresponding to the pre-peak portion of the curve. Most events from the post-peak portion of the test were closely aligned with the shear band, which was inclined at approximately 52 ~ from the horizontal (Fig. 6). As shown in Fig. 4, the linear beating did not move until point B, which was after peak load. This can be corroborated by the locations of acoustic emission; a clustering of microseismic events, which indicates localization, was detected at peak strength, but the locations did not extend across the specimen until point B. Thus, the shear band was not fully formed at peak stress, and growth of the shear band occurred between peak shear strength and the shear stress at point B. Once the shear band traversed across the specimen, the linear beating was free to translate. This meant that the displacement of the linear beating was equal to the horizontal component of the upper block of the specimen sliding along the shear band. Therefore, measurements of axial and lateral displacements allow the determination of slip, which can be compared to the estimate from the source AE characterization. 200

0.9

1. . . .

160

load

~

(

~

0.7

'/

i

120

0.5 ~

~ 80

o.3 .~

Z o .,-,

40

0.1 ~

_j ~

li.... bearing

/ ,

0

0.4

,

,

,

0.8 1.2 1.6 Average axial displaeernent (ram)

,

2

-0.1 2.4

Fig. 4. Mechanical response of the rock at 15MPa confining pressure

~4 O

153 1500

200 160 4 .......................

.

.

.

.

.

.

.

.

.

.

.

1200

.

o

f ~"~.~120 ]]i

.

900

...............

--

"~ so

600

...................

400 ~~

.~

300

. . . . . . . . . . . . . . . . . .

....

0.5

0.0

1.0

---, 2.0

1.5

0 2.5

Axial displacement (mrn)

Fig. 5. Load-displacement-AE response 100

100

~

90 80

:2... :::"

70

.

".'~

r

80

" ~'

9

70

60

60

50

50

40

40

30

30

.

20

20

(a)

10

0

10

20

(b)

30

40

0

z (mm)

10

20

30

40

z (mm)

Fig. 6. AE locations (a) before and (b) after peak load A unique feature of the plane-strain apparatus is the ability to determine the shear stress-slip displacement (z-5) response of the shear band (Fig. 7). Once the shear band forms (Fig. 8) across the specimen at the angle 0 measured from the minimum stress direction (~3), the shear stress r is

v --

O--1

-

2

0- 3

sin(20)

(11)

where is ~1 the vertical stress and ~3 is the horizontal stress (the confining pressure). The slip displacement 5 is calculated from 1

8 - [(Au) 2 + (Av) 2]2 cos(f1-0)

(12)

154 where Au is the horizontal displacement of the upper block sliding along the band, which is the displacement of the linear bearing; Av is the vertical component of sliding less the elastic deformation, which was determined from the axial displacement and the initial loading modulus. The angle fl is the orientation of the resultant displacement Ar (Fig. 8). The plane-strain apparatus provides an opportunity to compare slip calculated from the AE model with slip measured from the global measurements. After sled movement, the first 110 events were analyzed for source characterization. These events corresponded to a shear-stress drop from 23. 0522.53h,ff'a on the shear band (Fig. 7). From the simplified calibration of the transducers and the recorded acoustic emission, the normal displacement at the transducer position was calculated. Knowing the location and the displacement at each transducer, the moment tensor, with the corresponding displacement discontinuity tensor, of each event was determined. 23.0

22.5

22.0

r~

21.5

21.0

20.5 0

0.03

0.06

0.09

0.12

0.15

0.18

0.21

Slip D i s p l a c e m e n t ( m m )

Fig. 7. Softening response of the shear band

Shear band--~ ~

~ ~l

Shear band 0

Ar\'

Av

Fig. 8. Specimen with shear band and the kinematics

0.24

155 ANALYSIS Due to the symmetric characteristics of the moment tensor, the source model is not capable of uniquely determining the source parameters. In other words, there are always (except for the particular case of ct=0) two possible orientations for the source events since vectors b and n can be interchanged while producing the same seismic moment. Therefore, an additional source of information is needed to differentiate between the two vectors. When global loading and deformation mechanisms for a laboratory specimen are considered, the analysis is performed at a much different scale than that of acoustic sources. In a macroscopic scale, calculations of stress fields and deformation patterns usually do not take into account the internal structure of the material. Since acoustic sources are often of the order of the grain size of the material, their orientations can be influenced by microscopic inhomogeneities. Nevertheless, it is expected that kinematic motions from both scales be compatible. In softening from the start of slip to residual stress, the curve of vertical displacement versus horizontal displacement was convex up (Fig. 9), which represents compaction. Eventually, the constant level of shear stress was reached, where the upper block was sliding on the shear band with some frictional resistance but no volume change. 0.2

0.16

i

0.12

0.08 > 0.04

0

i 0

0.01

, 0.02

0.03

0.04

0.05

0.06

0.07

0.08

Horizontal displacement (ram)

Fig. 9. Compaction on the shear band In general, acoustic emission sources are associated with displacement discontinuities that have both normal and tangential components. Normal components of displacement could be associated with either closing (compaction of the shear band) or opening. The direction of tangential displacements, however, could be expected to follow the global displacements of the specimen. In summary, for every characterized event, both possibilities for b and n were checked against global kinematics and the vectors were chosen such that the tangential displacements were compatible. For the entire experiment, source characterization was performed for 290 events. The results obtained in terms ofmicrocrack and displacement vector orientations are shown in Fig. 10. For most events, the orientations of both vectors were relatively close to the shear band. The average microcrack and displacement vectors estimated from all characterized events were both within 30 ~

156

Fig. 10. Statistics of AE sources These orientations were on average very close indicating that most events were associated with shear mechanisms. In fact, all events were characterized as predominantly shear (crack mode angle c~ between 30-45~ The source mechanisms of mode I opening/closing and mode II sliding can be evaluated from equation (7) in terms of volume of the crack, bAA (Fig. 11). As expected for shear banding type of failure, the shear (volume) component was dominant, about ten times larger than the normal (volume) component, which indicated closing. The compaction during sliding was corroborated by the global measurements (Fig. 9) and it can be attributed to the significant confining pressure. To calculate an actual displacement, the crack area must be known. It is assumed in seismology that a constant strain drop of 10 over the slip area is representative of shear faulting [ 15] .

-4

.

[ b -- 10 -4 A A ~

(13)

The slip displacement of each event was calculated using this relation. Furthermore, the displacement of each event was assumed to contribute to the slip along the shear band; this is reasonable, as the AE events were produced by sliding and not by creation of new microcracks. Recognizing that the displacement vector is three-dimensional, the projection of b on the shear band must be computed. The orientation of the shear band on the y-z plane (Fig. 8) can be written as SB=Oi - 0.76j + 0.65k

(14)

where i,j,k are unit vectors. The directions of b and n were assigned from the global kinematics such that b was closely aligned with SB; then the component of b was calculated as bs8-- [b[cos~

(15)

where ~ is the angle between b and SB. The sum of the displacement from the AE can be compared with the shear band displacement. The cumulative AE slip was O.Ollmm, in close agreement with the global measurement of 0. 033ram.

157

0.03 '

0.02

Shear

m o)

0.01

<

iiiiiiii

0.00 ~3 -0.01

s

Normalg00:'~2D0

110

Event number

Fig. 11. Source volumes; negative values indicate closing

CONCLUSIONS A biaxial compression test on a sandstone specimen was performed with monitoring of acoustic emission. The plane-strain test showed that the shear band was not formed until after peak load. Compaction of the shear band was observed from the vertical and lateral measurements of displacement. The first 110 events recorded after the shear band was fully formed were characterized by a displacement discontinuity model. The complicated deconvolution process was eliminated by using a simplified calibration procedure. The sources of the post-peak events were characterized as being caused predominantly by slip in the direction of the global shear band although closing mechanisms were identified. Using the assumption of a constant strain drop of 104 over the slip area, the displacement from the 110 events was estimated to be 0. 011mm. The slip displacement along the shear band from global measurements was O.033mm. Thus, the quantitative acoustic emission method was capable of monitoring displacement during post-failure response. ACKNOWLEDGMENTS--This research was partially supported by the National Science Foundation (CMS-9604684) and the Minnesota Supercomputing Institute of the University of Minnesota. REFERENCES Hoek, E. & Bray, J. (1981). Rock Slope Engineering. Institution of Mining and Metallurgy, London. Chowdhury, R.N. (1978). Propagation of failure surfaces in natural slopes. J. Geophys. Res., 83(B12), 5983-5988. Palmer, C. A. and Rice, J. R. (1973). The growth of slip surfaces in the progressive failure of over-consolidated clay. Proc. Roy. Soc. Lond. A, 332, 527-549. Aki, K. & Richards, P.G. (1980). Quantitative Seismology: Theory and Methods. W. H. Freeman and Company, San Francisco. Palmer, C. A. and Rice, J. R. (1973). The growth of slip surfaces in the progressive failure of over-consolidated clay. Proc. Roy. Soc. Lond. A, 332, 527-549. Scruby, C.B., Baldwin, G.R. & Stacey, K.A. (1985). Characterization of fatigue crack extension by quantitative acoustic emission. Int. J. Frac., 28, 201-222.

158

10. 11 12. 13 14. 15.

Shah, K.R. & Labuz, J.F. (1995). Damage mechanisms in stressed rock from acoustic emission. J. Geophys. Res., 100(B8), 15,527-15,539. Rice, J.R. (1980). The mechanics of earthquake rupture. Physics of the Earth's Interior. Eds. Dziewonski, A.M. & Boschi, E., Italian Physical Society, Noah-Holland, Amsterdam, 555649. Michaels, J.E, Michaels, T.E & Sachse, W. (1981). Application of deconvolution to acoustic emission signal analysis. Material Evaluation, 3, 1032-1036. Ohtsu, M. (1989). Source kinematics of acoustic emission based on a moment tensor. NDTInternational, 22(1), 14-20. Lockner, D.A., Byerlee, J.D., Kuksenko, V., Ponomarev, A. & Sidorin, A. (1991). Quasistatic fault growth and shear fracture energy in granite. Nature, 350(7), 39-42. Labuz, J. F., Dai, S.T., and Papamichos, E. (1996). Plane-strain compression of rocklike materials. Int. J. RockMech. Min. Sci. & Geomech. Abstr., 33(6), 573-584. Simmons, J.A., Turner, C.D. & Wadley, H.N.G. (1987). Vector calibration of ultrasonic and acoustic emission transducers. J. Acoust. Soc. Am., 82, 1122-1130. Burridge, R. & Knopoff, L. (1964). Body force equivalents for seismic dislocations. Bull. Seis. Soc. Am., 54, 1875-1888. Kanamori, H. & Anderson, D.L. (1975). Theoretical basis of some empirical relations in seismology. Bull. Seis. Soc. Am., 65(5), 1073-1095.

159

ADVANCED ACOUSTIC EMISSION FOR ON-STREAM INSPECTION Mark F. Carlos, Physical Acoustics Corporation, Princeton, New Jersey Sotirios J. Vahaviolos, Physical Acoustics Corporation, Princeton, New Jersey W. David Wang, Equilon, Houston, Texas

ABSTRACT The "Advanced Acoustic Emission for On-Stream Inspection" project is a PERF (Petroleum Environmental Research Forum) sponsored initiative. The objective of the project is to develop field-usable, cost-effective, waveform based, Acoustic Emission (AE) technology for on-stream inspection of Petroleum industry vessels and pipelines, in lieu of internal inspection. To achieve this objective, the development effort is focusing on 4 key areas, including: 9 Development of more accurate AE source location techniques, 9 Determination and use of reliable source discrimination techniques, 9 Implementation of quantitative AE-fracture mechanics correlation's for fitness for service assessment, and 9 Integration of these developments into a high speed and parallel processing, cost effective AE system with experienced based, user-friendly operator software, as well as providing acoustic emission testing guidelines, procedures and inspector qualification requirements. Much progress has been made on this project to date. Many unique and exciting capabilities have been developed and added to the pre-test AE test planning and post-test AE data analysis process. Included in the pre-test planning is the capability to predict the AE response in terms of the source moment tensor using Green's functions and integration of the sensor response with these predictions. Velocity prediction calculations are aided by more advanced dispersion curves that take into account vessel contents, frequency and wave propagation attenuation. This paper will focus on project description and status along with presentation of sample but representative results and exciting capabilities coming out of this advanced AE development work. KEYWORDS Acoustic Emission (AE), Non Destructive Testing (NDT), Fitness for Service, Dispersion Curves, AE Source Discrimination, Source Location, Waveform Based Acoustic Emission, Neural Network, Pattern Recognition, Time-Frequency Analysis.

INTRODUCTION The overall objective of this effort is to deliver reliable AE methods for global, on-stream

160 inspection of pressure vessels in lieu of internal inspection. Nine companies, including 7 oil companies and 2 AE field test companies have joined together in this PERF sponsored initiative, to advance the "state of the art" of acoustic emission inspection and analysis. The development work includes investigating and implementing new and improved AE waveform based techniques for source location, source determination, noise identification and discrimination, correlation of defect sources to severity and ultimately, fitness-for-service assessment. Much theoretical, laboratory and field test work is being conducted to improve the knowledge and tools necessary to achieve the objectives of this project. Theoretical work being carried out to support the project development goals includes the following: 9 Developing advanced 3 dimensional dispersion curves (wavemode analysis) that take into account, not only the materials associated with the vessel construction, but also vessel fluid loading [1]. 9 Velocity determination (necessary for accurate source location) calculations based on selected frequency ranges of the dispersion curves, 9 Source waveform prediction [2,3] at the output of the sensor, taking into account; source distance, source height, source moment tensor (which corresponds to source type), wave modes and frequency analysis, 9 Finite element analysis of pressure vessels taking into account, the vessel construction (including size, mounting, nozzles and welds), its material properties (including strength parameters, physical constants, fracture toughness and material data for crack growth calculations), in order to carry out stress predictions and ultimately fitness for service determination [4], 9 Level II, Fitness for Service (FFS) analysis calculations within API-579 guidelines as a pre-inspection tool to aid in determining the how much pressure is needed to detect defects of interest before the AE test and as a post test analysis tool to evaluate the vessel's fitness for service as well as safety of the welded structure [4,5]. 9 Calculations to assess the sizing of the crack from the AE data. 9 Development of advanced location determination calculations. 9 Development of advanced event detection and data qualification techniques [6]. 9 Development of Time-Frequency Analysis techniques to aid in source discrimination techniques [7]. Laboratory work being carried out to support the project development goals includes the following: 9 Laboratory characterization of petroleum industry steels, to develop an understanding of AE activity due to fatigue and environmentally induced cracking, specifically to determine, the load level at which detectable AE signals appear with a given level of damage as well as determine the relationship of the AE signal to crack size and crack type determination [8]. 9 Location characterization tests are being carried out to analyze and improve location algorithms. 9 Feature extraction, analysis and pattern recognition of the predicted AE waveforms, taking into account multiple test variables (including source distance, source type, source height) in an effort to determine defect patterns for incorporation of noise filters and defect classifiers into the final software.

161 Field Test work being carried out to support the project development goals includes the following: 9 Conducting multiple AE tests on petroleum vessels in order to collect AE data for analysis by the advanced AE software to analyze background noises, actual AE signal propagation characteristics, attenuation effects and simulated defect sources. 9 If possible, conducting AE tests "to failure" on old petroleum vessels with known flaws in order to collect "real" crack data for analysis. 9 Performing field test work to improve test procedures and test software. As can be seen from the theoretical, laboratory and field test work, this project represents one of the largest and most comprehensive AE efforts to date, bringing together these aspects into the AE system and software. Although the project is not yet completed, there are substantial accomplishments and developments to be reported. These are described further below in the following sections.

Accurate Source Location

Accurate source location requires the application of a series of very important processes, including; accurate detection and processing of AE signals, extraction of critical timing features and assignment of their "correct" propagation velocity, event detection and grouping of AE signals to form an accurate "event record", and application of the appropriate location algorithm. This project is focusing on each of these components in terms of pre-test analysis and setup to assure the best up-front test conditions, as well as carrying out the in-test (and post-test) location determination processes and verifications. In order to carry out pre-test analysis and assure the best AE setup, conducive for accurate source location, a software utility program called PLOTRLQ has been developed based on the theoretical efforts described above with dispersion relations and source waveform prediction. The purpose of PLOTRLQ is to provide the user with insight into the propagation o f AE signals within the structure being analyzed (e.g. vessel or piping) and aid in the selection of AE sensors, frequency filtering and wave velocities, for optimum detection and determination of accurate location of AE sources. Many variables are acting on ALE signals that affect its propagation from the source. A theoretical approach is needed (and provided with PLOTRLQ) to aid in understanding these effects and guiding the practitioner into the proper AE system configuration for optimum AE performance. There are several important tools available within PLOTRLQ to assist in analyzing and visualizing the propagation of AE signals within the structure being analyzed. First, there is a very versatile Dispersion Curve generation capability that allows for a quasi 3-dimensional presentation of data. Second, there is an analysis of "best velocity", which provides the user with a theoretical velocity value to use based on a selected frequency range of analysis. This will help in determining the appropriate velocity to use for accurate source location calculations in the presence of multiple wave modes in a structure, each propagating at a different frequency. Third, there is a source waveform prediction capability within PLOTRLQ which shows the user the effect of defect type, source height in the structure, source distance and sensor selection. This is useful in determining the best AE sensor to select for the detection of a specific type of defect.

162 Upon opening up this program, a single user entry menu is presented (see Fig. 1) which allows the user to enter all the parameters needed for the analysis.

Fig. 1. Calculations Settings Menu The upper portion of the menu allows the user to select the vessel material being analyzed and its fluid loading. The information needed is automatically entered from the material and fluid databases, by simply selecting the correct material or fluid name or description. The only other information needed in order to produce a dispersion curve is the "Plate Thickness" and the "'Source Height in plate" (also seen in the menu). Once these pieces of information are entered, a 3 dimensional dispersion curve can be calculated and displayed. An example for a 50.8 mm (2 inch) thick plate is shown in Fig. 2. This dispersion curve is actually displayed in color. The axis labels are indicated at the top of the screen just below the toolbar. It indicates that the X-axis is plotted as a function of frequency in kHz, the Y-axis is plotted as a function of group velocity Vg and the Z-axis (or color axis) is plotted as a function of "Sigma" or attenuation coefficient due to wave energy loss due to the fluid. This third dimension, not usually seen with standard dispersion curve and wavemode analysis, allows the user to understand the relative attenuation of various portions of each wavemode due to the fluid coupling. Other information can be plotted on this third axis, including the effective modal factor, "Qeff" which provides information on the relative strength of each portion of the wavemode at different frequencies and velocities.

163

Fig. 2. Dispersion Curve for 50.8 mm steel and water fluid loading and third dimension In addition to the displayed dispersion curve, the user has much flexibility in choosing axes, axes limits and display control. This all aids in pre-test analysis and determining the frequencies, velocities and relative strengths which wavemodes travel through the materials. The next important question to ask when the complex dispersion curve is generated is, "'if the frequency is band-limited, what is the nature of the propagating velocity?" or "'is there a specific group velocity that can be assigned which describes the travelling waveform?" This can be answered by performing a "velocity projection". In this case the user enters the desired frequency band of analysis and a velocity projection graph is generated. This velocity projection is very useful in determining which velocity to choose for inserting into the source location user settrp. Source Waveform prediction, display and processing is the last major capability that is available in the PLOTRLQ utility software. This allows the user to view predicted waveforms either at the sensor face or through the sensor and into the input of the AE system. The user has the ability of varying several key aspects of this analysis and display capability, as can be seen on the bottom portion of the Fig. 1 menu. The user can select various waveform calculation types (e.g. with or without fluid loading), AE Source type (or by direct entry of the source moment tensors), waveform sampling rate, waveform start and stop points, receiver to source distance, source height in the plate and specific sensor type. A typical output waveform (at the output of the AE sensor) is shown in Fig. 3.

164

Fig. 3. Predicted Sensor Output Waveform With the predicted waveform capability, the user has the ability of generating many waveforms with different and changing parameters and viewing the corresponding waveform shape changes due to these variables. For example, the user can choose a fixed defect type and simply vary distance from source to receiver to view the waveform changes due to distance and dispersion. Or the user can vary different defects while holding the other variables constant to see waveshape changes between different defect types. Many types of analysis capabilities are offered by viewing the predicted AE source waveforms. PLOTRLQ also allows the user to perform feature extraction and waveform saving for further analysis. As can be seen by PLOTRLQ's capabilities, many aspects regarding setting up of the AE system and signal processing prior to a test, can be carried out by program. With PLOTRLQ, the user can determine the most appropriate AE sensor, frequency response, which critical timing parameters to extract and the dominant velocity that might be appropriate to select for the location algorithm. Three other aspects of "accurate source location" are also being investigated and improved in this project. These include, "Extraction of critical timing features", "Event detection and grouping algorithms" and "source location algorithms". Various timing features are being investigated in this work. A "Timing feature" (also know as "time of arrival") is the determination of the accurate time of arrival of that waveform (AE signal) from the source. When a timing feature is determined, it is also important to know its related velocity since it may be related to one or more wavemodes or frequencies. The goal of this investigation is to allow the extraction of one or more accurate timing features from each AE sensor for a given event. Various timing features including, first arrival, peak analysis,

165 band-limited analysis are being investigated and implemented into the location analysis software. Event detection and grouping of AE data (hits) to form an event is also a very important consideration in this work. In actual testing on a vessel, there are many noise sources which may be emitting at the same time as a defect event and there is also the possibility of receiving overlapping events. It is important to carefully apply event detection and grouping algorithms to make sure that each received AE "hit" is part of a given event. This is being accomplished by studying various aspects of AE sensor setup, their distance and timing relationships, attenuation and propagation characteristics and implementing one or more event qualification checks to eliminate errant AE sensor arrivals, in order that the data presented to the location algorithm is correct. The last important aspect of accurate source location involves the selection and implementation of the source location algorithm. Various source location algorithms are being investigated in this project with keen interest in over-determination location methods, whereby more that the minimum number of AE arrivals are analyzed. This is important since it allows an averaging and minimizing of any error due to one or more erroneous data points.

Reliable Source Discrimination

The purpose of source discrimination is twofold. If successful, it provides for the identification of defects as opposed to noise sources and secondly, it allows the identification of different types of defect sources. Removing noise sources and identifying defect sources provides a signal "filtering" function that can eliminate processing and analysis of unimportant signals. This can result is a "faster" processing system and one which provides more accurate results to the FFS algorithms. Two approaches are currently being considered, both of which are theoretical, at this point. The first, a straightforward pattern recognition method, analyzes extracted feature data from a very large set of predicted source waveforms with known parameters, from PLOTRLQ. This effort, provides a very comprehensive set of AE feature data with known sources, known source distances, known source heights, that can be entered into automated pattern recognition software, to determine if the various sources can be separated and identified from one another, as a result of AE feature analysis. If successful, a neural network front-end filter will be able to be implemented that can immediately filter our (eliminate) AE data due to noise sources and classify various defect sources to permit an accurate data analysis. The second area being investigated, uses time-frequency analysis (like Short-Time FFT's) to separate or discriminate between different sources. Time-Frequency information is available in the advanced dispersion curve analysis inherent in the PLOTRLQ software and this allows one to determine a time-frequency analysis of each source type, to determine if there is adequate time-frequency information separation to distinguish between the various sources and noise expected. If successful, these techniques will be implemented into the system processing software to identify different AE sources and process them appropriately.

166 Acoustic Emission Based Fitness-For-Service Analysis

With the above AE work being carried out to detect and process AE signals, to classify them and determine their location, the next step in the analysis process is to determine the severity of these received AE sources and determine how they affect the overall quality and structural integrity of the vessel. This is the overall purpose of the "Fitness-for-Service" part of the project work. It effectively closes the loop and provides a quantitative AE inspection result. Utilizing the theoretical and laboratory work referenced above in the introduction, a software program called AE-FFS is being developed and refined. The program intent is to provide both "pre-tesf" analysis and setup support and "post-test" FFS analysis results. In pre-test assessment, the operator is able to establish the "minimum detectable defect size", based on the placement and separation of AE sensors on the vessel and the loading (pressurization) schedule planned. This allows some flexibility in sensor and test setup. The AE-FFS post-test analysis, analyzes each detected defect based on its AE source location, AE signal levels and applies an API-579 level II analysis to evaluate the vessel's "Fitness for Service". If the vessel is fit for service, its safety margin is calculated and displayed using the Failure Assessment Diagram (FAD) technique. For FFS analysis, various vessel parameters must be entered and presented to the program. Included is the vessel geometrical parameters (shape, size, thickness, weld locations, nozzle locations, AE sensor placement and defect type), material parameters, loading and welding type. With this information entered, the AE system can pass the results and location of each detected defect to the FFS algorithm and fitness for service can be determined.

Fig. 4. FFS Analysis Result using FAD techniques

167 Figure 4 shows the results of a typical post test FFS analysis result using a failure assessment diagram. In this case, the result is that the vessel is safe and that there is adequate safety margin.

AE Instrumentation and Guidelines

The efforts detailed above, are improving on the science of the acoustic emission inspection process and making the AE results more accurate and quantitative. In addition however, another key goal of the project has been to improve further on Acoustic Emission systems hardware and software, in order to make AE easier and less costly to apply. Also, there was a goal to provide a new class of multi-channel, high performance, digital, waveform collection and processing AE systems. This is needed in order to perform the detailed analyses described in the previous sections. This goal has been met with the development of the DiSP, AE system. Figure 5 shows a fully configured "all in one" field test DiSP, system with 56 AE channels [9]. The DiSP-56, "All-in-one" chassis, is integrated inside a rugged transit case that also contains a PC computer, flat screen LCD display, printer, keyboard, mouse and all wiring pre-connected to the system so that all one has to do to operate the system is to connect the AC line cord and sensors, power-up, and begin testing. This makes it very conducive for AE field testing. The DiSP uses multiple PCI/DSP-4's, "AE System on a PCI card", to provide up to 56 AE, high performance channels inside one system enclosure. The features of the system that make it conducive for this project includes, its fully digital design, with 16 bit, 10 Mega samples/second A/D waveform acquisition rate, its on-board digital signal processor for processing data at very high speeds, 4 high pass and 4 low pass "real time" filters for selecting the most desirable bandwidth for AE signal processing, its waveform module with separate DSP processor for high speed waveform, all built upon PCI bus, for high speed data transfer to the analysis computer. This multiple processing system will provide the necessary performance and growth which is needed for project success.

The software for the system is being integrated into a single pre-test and post-test user friendly acquisition and analysis package, incorporating all the features of the software that has been described in the previous sections. The system and software will expect the user to be Level II certified in AE testing. In addition to the system and software, a comprehensive set of AE test guidelines are being developed. These guidelines will provide all the necessary background in order to help the user understand all aspects of the test and to guide the user through all parts of the test, including instrumentation description and setup,

168 software description, setup and operation, pre-test planning and analysis, physical setup of the test, data acquisition procedure and guidance, dismantling, data analysis and reporting requirements. In summary, much progress has been made on this project to date in each of the four area's including; development of more accurate source location techniques, determination and use of reliable source discrimination techniques, implementation of quantitative AE-fracture mechanics and fitness for service, and integration of these developments into a high speed, cost effective AE system with experienced based, user friendly operator software and full testing guidelines and procedures. The effort involves, theoretical and laboratory work, field testing as well as system hardware and software development work. The project team is comprised of vessel owners and AE field test companies who are seeking a better, more efficient and accurate NDT test method, AE vendors who are advancing the state-of-the-art of acoustic emission, and university professors who are turning theoretical knowledge into practical useful technology. Although not complete, this project is well on its way to a very successful completion, offering a new "Advanced acoustic emission for on-stream inspection" technology, leading the industry on its way, "beyond the millennium".

REFERENCES

.

.

.

Weaver, R., (1998). Theoretical Modeling of Acoustic Emission Wave Propagation, Phase I: Analytic Formalism. PERF 95-11 report. Weaver, R., Pao, Y-H, (1982). Axisymmetric Elastic Waves Excited by a Point Source in a Plate. Journal of Applied Mechanics. Weaver, R., (1998). Theoretical Modeling of Acoustic Emission Wave Propagation, Phase II: Numerical Evaluations. PERF 95-11 report. Tsai, Chon L., Zhao, Yufei, (1999). Fitness-for-Service Assessment for Operating Pressure Vessels and Pipelines In Refinery and Chemical Service by Acoustic Emission Tests. PERF 95-11 report. Wang, W.D., (1999). Fitness-for-Service~Acoustic Emission correlation program for Pre-Test and Post-TestAnalysis. PERF 95-11 report. Carlos, M.F., (1999). Event Detection and Grouping for Accurate Source Location. PERF 95-11 report. Weaver, R., (1999). Theoretical Modeling of Acoustic Emission Wave Propagation. Phase III: Preliminary Study of the Inverse Problem. PERF 95-11 report. Rokhlin, S.I., (1999). Assessment of Acoustic Emission from Cracking in A-516 Grade 70 Steel. PERF 95-11 report. Carlos, M.F., (1999).Advanced Acoustic Emission For On-Stream Inspection, Progress Report. PERF 95-11 report.

169

LISTEN TO YOUR STORAGE TANKS TO IMPROVE SAFETY AND REDUCE COST PHILLIP T. COLE Physical Acoustics Limited, Over, Cambridge, CB4 5QE, UK. [email protected] PETER J. VAN DE LOO Shell Global Solutions, Amsterdam, The Netherlands. [email protected]

Keywords:

In-service, tank floor inspection, predictive maintenance, acoustic emission.

ABSTRACT The floor of an above ground storage tank (AST) is impossible to inspect using conventional methods with the tank still in-service. However, the cost of removing it from service is exceedingly high when cleaning and decontamination costs are taken into account. Spending this money, only to find the floor does not require any repairs, is very wasteful of valuable maintenance resources, but the consequences of a major floor failure are even worse. Since 1990 an alternative in-service monitoring method, that gives information about floor condition with the product still in the tank, has been developed in co-operation with the oil industry. TANKPAC TM uses sensitive sensors on the outside of the tank to listen to the sound resulting from corrosion of the floor. A database, developed by opening and inspecting tanks after testing, is used to interpret the data, following separation from the environmental noise by using a range of signal analysis and processing techniques. The method provides a very costeffective maintenance planning tool for tank farm managers, proven by its use on >2000 tanks, and its acceptance by major oil companies.

BACKGROUND When acoustic methods were first used to investigate tank floors, in the early 80's, the focus was on leak detection and location, and results varied enormously, with acoustics being considered, on balance, largely an unreliable method. There are several reasons why acoustic leak detection alone is of limited use for tank floors. The main being that the sludge and debris on the floor of a typical crude oil tank is often all that seals the floor, so detecting "no leak present" tells you nothing about the floor condition. In the words of one tank farm manager: "I don't need a complex test to tell me I'm knee-deep in oil". What he needed was "a method to tell the condition of the floor so that inspection and maintenance can be planned before leaks occur". By the time the floor of a crude tank gets in very bad condition it may be too late to remove the tank from service without major leakage problems. There are many cases of floors leaking in dramatic fashion when attempts are made to re-suspend the sludge prior to shutdown. Looking at the example in Fig. 1, showing one of 40+ holes in the floor of a crude oil tank following shutdown, this is not surprising. On the other hand, to spend -~ $250,000 cleaning a crude tank for inspection, to find no repairs are needed, is a significant waste of valuable maintenance resources that could be better spent elsewhere. Crude tanks are not the only case of importance, most product tanks cause significant environmental damage when they leak, and also cost significant sums to shut down, clean, and inspect, a method that can tell the condition on-line is invaluable.

170

Fig. 1. Damage in a crude oil tank found following cleaning. Sludge and debris seal the floor preventing leakage in service. MAINTENANCE PRACTICES Rotating machinery maintenance used to be time-based, or "preventive", with machines being stripped down at set intervals, inspected and re-assembled to be put back in service, sometimes in worse condition than when they were taken out, or "breakdown" maintenance, simply waiting for a failure to occur. Many years ago this changed largely to condition-based, or "predictive" maintenance, by measuring parameters such as the vibration signatures, temperature, and performance, problems were diagnosed early, allowing appropriate action to be planned. This minimised overall maintenance costs, and optimised production efficiency. Traditional Tank Maintenance Practice.

Most tank maintenance is still either time based, taking tanks out at set intervals, or "leakbased", waiting for a major problem to become apparent by the product outside the tank. "Leak-based" maintenance is not longer acceptable practice in most countries for both environmental and economic reasons. "Time-based" maintenance practice varies enormously from country to country, and company to company, however, even a strict "intelligent" timebased regime based on past history does not guarantee that no failures will occur in service, as some of the following case histories will describe. Reasons for failure of time based maintenance include the inadequacy of the inspection methods used, and change of conditions inside the tank, leading to increased corrosion rates. Possibly the biggest failure of time-based maintenance though, is that enormous resources are wasted opening and cleaning tanks for inspection, when there is nothing wrong, and no repairs are needed. Saudi Aramco [ 1] reported that >M$50 could be saved annually by avoiding the tank cleaning and inspections where no repairs were actually required.

171

Failure Of Traditional Tank Maintenance Practice- Some Case Histories. The first example in Fig. 2 shows the test result from a naptha tank that was found to be losing ~100 cubic metres per day according to overall inventory records. In this case there was no visible product, due to the sand base and low water table, and only a faint smell of naptha in the tank farm area. When the floor was cut it was found that several cubic metres of the base had been washed away by the leaking product, leading to a high risk that catastrophic floor failure could have occurred. The cause was microbiologically induced corrosion (MIC) which had made a 1cm hole in the floor. A possible problem with one of the eight tanks in this farm had been suspected by operations, this had led to Physical Acoustics being called, but it could also have been a gauging error.

Fig. 2. Acoustic Leak location on a naptha tank floor, 100 cubic metre per day leak through a 1cm hole, there was no evidence of product outside the tank, or of which tank was leaking.

The second case, shown in Fig. 3, is typical of many clean product tanks, it is a diesel oil tank and has an epoxy paint coated floor, the coating has failed locally, resulting in highly localised corrosion at a high rate. In this case one of the locations has pin-holed through into a hole resulting in product leakage. When the tank was opened shortly after, the hole was 1mm diameter. In this case no problems with the tank had been expected, the damage was identified during a routine TANKPAC TM test to check for floor corrosion.

Fig. 3. Leaking diesel oil tank, lmm pinhole in the floor, found during routine corrosion test.

172 The third case used to illustrate the failure of time based maintenance is a hot gas-oil tank, this had been inspected using traditional methods internally, twelve months prior to collapse, by using a combination of visual inspection, magnetic flux leakage, and ultrasonic thickness gauging. Unfortunately the highly localised line of underside corrosion was missed by the inspection pattern used, leading to rupture during filling. The floor split circumferentially for a considerable distance, and the rapid loss of product pulled a partial vacuum that led the tank to collapse, see Fig. 4.

Fig. 4. Gas-oil tank, collapsed as a result of localised annular ring corrosion, this had been internally inspected 12 months before collapse.

On a similar tank from a nearby refinery the same problem had been identified during an inservice TANKPAC T M test by Physical Acoustics, this test identified severe corrosion damage occurring, as a result of which some exploratory excavation was carried out. This confirmed more than 50% localised loss of metal under the annular ring around most of the circumference, where the temperature was optimum for corrosion to occur. The tank was immediately removed from service and a new annular ring installed. Figure 5 shows a representation of the floor with the emission that resulted from active corrosion plotted on the vertical axis.

Fig. 5. Acoustic location of active underside corrosion from the annular ring of a gas-oil tank.

173 There are hundreds more cases that demonstrate the limitations of conventional maintenance practices as applied to storage tanks. However there are many tens of thousands more cases where tanks have been opened, cleaned, and inspected, only to find the floor to be in good condition, and no repairs are required. This is not only an enormous waste of valuable maintenance resources, but also an environmental issue, due to the problems and costs associated with the disposal of the toxic waste.

IN-SERVICE TESTING FOR STORAGE TANKS Physical Acoustics has provided "on-line" monitoring services for spheres, bullets, and similar pressure systems for 20+ years, in the 80's a number of companies were promoting AE (acoustic emission) for tank floor leak detection. In 1989 UK customers were asking what could be done with tank floors, prior to, them leaking. We knew from work carried out in the early 80's that rusting of steel produces substantial acoustic emission, but was it detectable on a tank floor from the outside? Transmission through the floor plate was out of the question, so it had to be via the liquid. At the time people were trying to detect leaks using acoustic systems, so the obvious answer was to try it. A small number of tanks (-~50) owned by various customers were monitored and inspected afterwards during the first 2 years and a "user group" was formed to share the information. The basic procedure for testing and evaluation was established, and has slowly been refined over the years as a wider range of tank types have been tested, and more experience and feedback has been gained. New users usually evaluate the TANKPAC TM system by testing some tanks and inspecting afterwards, and ''user group" customers almost always feed back what they find when tanks are shut down, so the database and experience is continually expanding. The procedures are complex when compared with most AE tests, and not all tanks can be tested, or are suitable, so very strict quality records are kept and all test results and records are reviewed by a second engineer, and approved by a PAC Level III TANKPAC T M specialist. The investment in TANKPAC TM is substantial, but the third party publications attest to the benefits and reliability of the method.

Basis of the Method The basis of the method is that active corrosion produces acoustic emission, as the corrosion scale thickens the energy released from scale fracture and de-bonding or de-lamination increases. This was confirmed in the laboratory by independent research [2]. The energy released can be substantial, but usually the signals are small, and detecting them in the middle of a tank requires very high sensitivity, good test conditions, and excellent noise recognition and rejection. Test sensitivity is more than forty times that which is typically used for detecting cracks in pressure vessels. The analysis of test data is split into interpretation, where all noise signals are identified and removed, and evaluation, where the information from corrosion is graded based on the amount of emission, its characteristics, and location, together with tank information such as size, product, and sludge height. Noise sources include roof movement, condensation, and leakage of valves, particle impacts, nitrogen blankets, and level measurement systems.

174 Figure 6 shows emission detected from corrosion of mild steel during one hour monitoring of a sulphur tank [3], the horisontal axis is signal amplitude, the vertical is number of emissions, and the depth axis is time, in excess of one million emissions per hour were detected.

Fig. 6. Number of emissions versus amplitude from a corroding sulphur tank during one hour.

Extensive training of engineers is essential due to the complexity of the test, and before being allowed to test unsupervised they must meet minimum experience requirements and pass both written and practical examinations. "Noise" sources may also result from items corroding other than the floor, Fig. 7 shows the emission located from an oilfield production tank that has many internal zinc sacrificial anodes. When the epoxy paint protection on the floor fails, the zinc anode in that area corrodes instead of the floor, otherwise the floor would rapidly pinhole through. This method of protection only works because of the high water content. Once the anodes have dissolved the floor will deteriorate rapidly. The "active" zinc anodes can clearly be seen in this example. 54r s t o r ~ l e tne~

.~.~" : "--i -$#7.3. ,,:

.

~/-~

~:

.

,.

-

"

m

_

.

.

.,

.

...

-.",

. .

.

Fig. 7. Emission located due to corrosion of the active internal sacrificial anodes in an oil-field production tank. This demonstrates clearly the ability to locate corrosion in a storage tank.

175

Examples of Test Results and Outcome This 67m diameter crude oil tank, Fig. 8, showed extensive damage indications near the annular in two main areas. Following shutdown a complete magnetic flux floor scan was carried out and the results confirmed the findings. The floor had a 3mm grp coating which was still intact and tightly bonded, so the floor was cut and underside corrosion conf'u'med in excess of 60% through-floor.

-se

-ze

-xe

i

le

2,,

-,e

Fig. 8. Emission located due to underside corrosion in a 67 metre crude tank with a 3mm grp lining. The corrosion was >60% through floor in the active areas.

In this next case, underside corrosion was detected from a 110 metre diameter grp lined crude oil tank, but unusually, activity was not from the annular, but from the middle of the floor. Following opening, the MFL survey, (magnetic flux leakage, shown in Fig. 9), confirmed the findings, the floor coating was intact, so the floor was cut, to reveal severe underside corrosion. An analysis of the sand base showed chlorides to be present, leading to the suspicion that a truck load of beach sand had been used during the building operation.

Fig. 9. MFL survey confirming TANKPAC TM AE results from a 110m grp lined crude tank, off-centre areas represent >60% underside metal loss.

Usually a grp coating in crude service gives good long-term protection, however, on one occasion, out of seven identical crude tanks at the same location, six graded "A", no indication

176 of any problem, and one graded "E", indicating severe corrosion damage, location analysis indicated the entire floor to be corroding. The tank was removed from service to find the grp coating was loose and floating around in the product, inadequate surface preparation or poor resin cure was suspected.

The Grading System The TANKPAC TM test gives information on the amount and location of different types of emission, together with grades that result from processing the data based on the database of experience with particular tanks and products. The data is normalised before grading, it must be remembered that the location plots show only a percentage of the data, that which reaches three sensors, and this will vary with tank condition and test situation. The grading system takes into account all the factors that may have an effect on the test result. There are two main parameters which make the composite grade, the "overall" grade, and the "potential leak" grade, a combination of these results in the "composite" grade and hence re-test interval or inspection recommendation, this is shown in table 1 below. Table 1. T ~ A C

TM

Grading system and associated recommendations.

"Overall" Grade "PLD" Grade A B C

A B C E I I II (n/a) (n/a) (n/a=doesn't occur) I I II (n/a) (n/a) I> ~4 years I I II II (n/a) II > ~2 years II II III III III Ill+IV > schedule inspection II III III IV IV (or 1 year and 6months) E III III IV IV IV Clearly leaking tanks may be unable to grade, (should be opened anyway). Active concentrated sources in "all data" will also increase severity of grading.

It should be clearly stated that "potential leak" or "PLD" data does not imply the tank is actually leaking, only that there is highly concentrated activity of a type that historically has been associated with severe localised corrosion. When a significant leak is present, it is clear, and this may mask corrosion activity, preventing grading of the tank, similarly, identification of minor leaks may be masked by highly active corrosion.

TANKPAC TM RELIABILITY AND STATISTICS The most comprehensive independent analysis of the method [4] was carried out by a user group chaired by Peter van De Loo, this analysed the test results and compared them with the subsequent inspection results and repairs actually carried out on the tanks. The comparison was carried out on the 157 tanks internally inspected from 600 tests conducted prior to June 1996. The results were presented at the European Conference for Non-Destructive Testing in 1998, and are now published on the NDT net website at:

http://www.ndt.net/article/ecndt98/chemical/O95/O95.htm

This comparison focussed on the "overall" tank grade and concluded that 100% of"A" grade tanks had no damage and required no repair, whereas 60% of"E" grade tanks had extensive

177

damage requiring major repairs or a new floor. Fig. 10 shows the correlation between overall TANKPAC TM grade and the subsequent repairs required. One point made was that if the "PLD" data is taken into consideration the "misses" (B grade with FU3-4) are reduced. False calls (D/E with FU1-2) result from failure to recognise extraneous noise, increased experience, improved test practice, and modem analytical methods have reduced these still further. Follow-up results versus AE-grades, normalised per AE-grade population of 157 tanks (Shell, Dow-Stade, DSM, PKE, Total and PAL database) 120 100

60 n,

40

@

2(1

N -~

0

~ A

iIL

I, I

B C D E AE-grade [] FU 1/2 Minimal damage: no repairs [] FU 3 Damage: some repairs [] FU 4 Signit'r damage: major repair/new floor

Fig. 10. Correlation between "Overall" TANKPAC TM grade and the extent of damage and repairs required following internal inspection. Data from tests carried out prior to June 1996.

As more experience is gained on different tanks and products, in different conditions, and inspection feedback increases from customers, the procedure is "fine-tuned", with the annual update now at revision 8, more than 2000 tanks have been tested on-line. One limitation is that the system depends upon the presence of corrosion scale, so any procedure that removes this chemically or mechanically will "reset" tank condition. The method also cannot assess the internal condition of tanks in which the product or conditions regularly change, as the corrosion may stop and start. It will however tell you if the corrosion is currently active or not, and dramatic results have been seen when corrosion is stopped by deliberate chemical neutralisation. The method may still be used to check for underside corrosion on tanks with product changes. A comprehensive method statement is available which clearly states the requirements for effective application and limitations on use. CONCLUDING REMARKS A method now exists which can assess tank floor condition in-service, it is not an inspection method, but a maintenance-planning tool that allows owners to optimise the interval between tank internal inspection and re-habilitation, making enormous financial savings as a result, by avoiding unnecessary tank opening, whilst still maintaining integrity. The method has been developed in co-operation with major oil and chemical companies and has now been used worldwide in over 40 countries, a total of 10 years experience exists in the field use of the technique. The ability to diagnose damage in tanks long before a failure occurs now allows operators to plan their maintenance far more effectively, avoiding failure incidents, which are both costly and damaging to the environment.

178 TANKPAC T M is the only on-line maintenance-planning tool available for above ground storage tanks, the cost of testing using the method is insignificant when compared with the cost of actually opening a tank for internal inspection. This brings large M$ savings to operators, and when compared to many time-based inspection regimes, improved performance, as it is effectively providing a "condition-based" maintenance planning approach.

Acknowledgements: Development of this procedure, and verification by internal tank inspection, has been a cooperative effort involving too many oil and chemical companies to mention. However, most took an active role in the UK user group that meets under EEMUA (Engineering equipment and material users association, newsletter [5] describes use of method), or in the Netherlands user group. Shell carried out a major co-ordinating role and statistical analysis of results and feedback. Physical Acoustics Corporation, Princeton, NJ, USA, wrote the software and supplied the specialist instrumentation and necessary modifications.

References:

.

.

.

.

Miller, S.D., O'Brien, J., Keck, D.L. (1998) Proceedings of 7th European Conference For Non-Destructive Testing, Copenhagen. Van De Loo, P.J., (1998) Proceedings of 23 ra European Working Group Conference on Acoustic Emission. Vienna. Gautrey, S.N., Cole, P.T., (1997) Proceedings of 22 nd European Working Group Conference on Acoustic Emission. Aberdeen. Van De Loo, P.J., Herrmann, B. (1998) Proceedings of 7th European Conference on Non-Destructive Testing. EEMUA (1998), Newsletter No.26.

179

A C O U S T I C E M I S S I O N IN C O M P O S I T E M A T E R I A L S AND S T R U C T U R E S

Pierre F L E I S C H M A N N Groupe d'Etudes de M6tallurgie Physique et de Physique des Mat6riaux, UMR 5510, INSA de Lyon, 69621 Villeurbanne, France Jean Claude LENAIN Euro Physical Acoustics SA, 27 rue Magellan, 94373 Sucy-en-Brie, France

P a r t 1: F R A C T U R E F O R E C A S T IN C O M P O S I T E M A T E R I A L S F R A C T U R E F O R E C A S T IN C O M P O S I T E M A T E R I A L S

I INTRODUCTION Acoustic Emission (AE) is due to local and transient deformation processes in materials. Induced by a mechanical solicitation, AE sources are unstable transitions between two stable energetic states. The energy is released by emission of stress waves whose components, in the high frequency range, is the acoustic emission, the component at frequency zero is the increases in strain or elongation of the sample. If we consider a model AE source in a pure elastic material (1)(2), it can be shown that a relation exist between the high frequencies components of the emitted wave (transients or acoustic emission) and the component at zero frequency (permanent displacement or stress release). In this calculation, the nature of the source (mode I, II or III), the size and the orientation and the dynamic characteristics of the source lead to the amount of energy obtained in the high frequency range (3). Experimentally, when all AE sources are similar, same nature, same kinetic, same size..., each AE event leads to the same amount of permanent displacement. In such case proportionality is observed between strain of the test sample and Acoustic Emission event count. Experimental evidence can be observed in some simple composites materials where all AE events are due to the same sources (burst type emission due to fibber breaks for instance). AE can then be used to measure some changes in rheological behaviour of these samples and leads to a good prediction of the fracture. Another way is to measure the amount of AE energy during a test where the load increases linearly with the time. When the mechanical solicitation increases, the energy release by each acoustic emission burst also increases. We will show that, with some assumption, the ultimate load of the sample can be forecast.

II Acoustic Emission in Carbon Epoxy samples In the general case, AE can be describe by the relation: ul(t ) = kl(e,t,~. ) u 0

(1)

180 where k is proportionality factor which depends from the kinetics of the source t, the type of source e and on geometrical properties X. u 1 is the displacement in high frequencies (AE) and u 0 is the permanent displacement (total displacement minus elastic displacement). In burst type AE, when all AE sources are of same nature with the same geometric and kinetic characteristics, a proportionality between plastic strain and AE event count has to be measured. This remark explain AE results in some simple materials. In a one directional carbon epoxy sample, all AE bursts having an amplitude over a given threshold are due to fibber breaks. As the fibbers are all of same diameter, with a vicinity region of same nature, we can assume that the term kl(e,t,L ) is the same for all events: so each AE burst contains the same strain increase in the sample. The conditions to observe proportionality between AE cumulative burst count and strain are then satisfied. Figure 1 presents the AE cumulative burst count and the strain measured by a strain gauge during a creep test (4). The sample is a one directional long carbon fibber - epoxy matrix composite with a fibber volume fraction of 0.6. Fibbers are T800 type ( diameter: 7/tm, Young modulus 290 GPa, Weibull parameter 3.9) in a 5245C matrix. The test sample has a rectangular cross section ( 8mm * 0.8mmm) and a useful length of 55mm. There are near 100000 fibbers in the sample. A creep test is applied on the sample, with constant load controlled by an hydraulic MTS tensile device. A very good proportionality between strain and event count can be observed, as predicted by relation 1.

0,8

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Figure 1 Strain and AE cumulative burst count in a creep test of a carbon epoxy composite sample (4) So, if we consider that acoustic emission cumulative event and strain are proportional, at constant applied stress, AE leads to an original way to study the rheological behaviour of this material. It can be shown that, during a first stage, a correlation factor b is constant. Over a given stress, this correlation factor increases as shown in Fig. 2. Its means that first, single isolated fibber break occurs, without any correlation with other breaks. With the increase of the

181 load, the density of cracks increases to reach a critical density, where micro cracks begin to merge leading to co-operative effects. Then, this correlation factors increases. b -

09, 5

T300/914 Unidixe ctiormel

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~

0,1 O 0,0

-

200

, 400

.

, 600

.

, 800

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Figure 2 Correlation parameter b vs. the applied stress in a T300/914 carbon epoxy sample (4) The change in the b factor occurs at 60% of the ultimate load. The measurement of this factor can leads to a good estimation of the fracture load.

III Acoustic emission energy in relation with load

om

100

o g~

50

r I

0

20

I

40

60

80

load

Figure 3 Energy per load unit versus load in a Numerical simulation, load and energy are in arbitrary units. The percolation model (statistical model where the fibber breaks are uniformly and randomly distributed in the sample) is a very good description as long as no co-operative effects are observed or measured (5)(6). To introduce such co-operative effects in this model, a simple

182 way is to consider the local stress, instead the global applied stress: due to local stress enhancement, the local stress can be much higher then the global stress. If we consider that AE events are due to fibber break, that in a given section of the sample, the applied load is share by all non broken fibbers, and that a fibber break occurs when the effective stress on the fibber is over a value given by a Weibull distribution, a simple numerical calculation can be developed. Figure 3 gives the acoustic emission energy versus load with the additional assumptions: load increases linearly versus time, AE is only due to fibber breaks, the AE energy given by each fibber break is proportional to the square of the local load. The percolation theory say that the energy released per time unit dE/dt, on average increases as a power law of the time to fracture or load to fracture (Pr-P) if the load P increases proportional to time: dE/dt = Eo(Pr-P) -~ (2) This law has been verified in fibber glass composite (7) as presented in Fig. 4. The numerical simulation given in Fig.3 also verifies this law.

(dEfdt)/(dEmax/dt) 10O11JI10-2-

-3 1010-3

!

I

10-2 (Pr-P)/Pr 10-1

10fO

Figure 4 Power law verification (fracture occurs at the load Pr) in fibber glass composites (7) IV Time or load to fracture prediction

Fig. 4 shows clearly that, in glass epoxy samples, the power law is experimentally verified when (Pr-P)/Pr is in the range 10 -3 to 10 -1. The power a is 0.22. Now, with the knowledge of the value et, the measurements can be presented in Scale (dE/dt) -1/~ versus (P), then the load at fracture can be predicted as presented in Fig. 5. (The data presented in Fig. 5 comes from the data presented in Fig. 3).

183 _

qll

= 0,8& 0,6@

0,4 -~ 9 0,2_

m m

0

10

20

30

40

50

60

t

70

load Figure 5 Fracture prediction: the distance to fracture is zero as fracture occurs The term (dE/dt) -1/'~ is called "distance to fracture". The fracture occurs when this term is zero. We see that a good prediction of the ultimate load can be done when the load reaches 80% of the fracture load. This method, corrected by the presence of log-periodic modulations, has been used extensively to control pressure tanks of Ariane 4 and 5 rockets made in kevlar or carbon composites (8)(9).

V Conclusion In both examples presented here, all the acoustic emissions events are due to the physical mechanism which controls the fracture of the sample. Two types of test are presented. A creep test leads to the measurement of a "correlation factors" that increases when co-operative effects are observed in the sample. The change of this correlation factor occurs near 60% of the load of fracture in carbon-epoxy samples. Another test presented here is based on the AE energy emitted by increasing at constant rate the load of the sample. A power law between AE energy per time unit and load or time to fracture is observed. When the power coefficient is known, the AE results can be used to predict the load or time of fracture. This second method has been successfully used to control the pressure tanks of the Ariane 4 and 5 rockets.

References 1 M. Enoki and T. Kishi, Int. J. of fracture, 1988, 38, N~ 295-310 2 M. Ohtsu, J. of geophysical research, 1991, 96(B4), 6211-6221 3 D. Rouby and P. Fleischmann, Phil. Mag.A, 1983, 47, N~ 671-687 4 N. Rochat, R. Fougeres and P. Fleischmann, J. of Acoustic Emission, 1990, 9, N~ 91-96 5 Statistical Models for the Fracture of Disordered Media, edited by H.J. Hermann and S. Roux (Elsevier, Amsterdam) 6 D. Sornette, C. Vanneste and L. Knopoff, Phys. Rev A, 1992, 45, N~ 8351-8357 7 A. Garcimartin, A. Guarino, L. Bellon and S. Ciliberto, Phys. Rev. Lett., 1997, 79, N~ 3202-3205 8 J.C. Anifrani, C. Le Floch, D. Sornette and B. Souillard, J. Phys. I, 1995, 631-638 9 D. Sornette, SPIE Conference, 1999, Vo13586, 178-188

184 Part 2: ACOUSTIC EMISSION TESTING OF ADVANCED COMPOSITE STRUCTURES

Composite materials and specially Fiber Reinforced Plastics (FRP) are more and more used in many type of industries. Classical non-destructive testing methods are often difficult to apply in practice to composite structure because of their anisotropic properties and heterogeneous structure. In early 80's the aerospace and aircraft industries started some programs to investigate the use of Carbon Fibers Reinforced Plastics (CFRP).Designs also involved bonding between parts including metal and composites and so the structures becomes larger and more and more complex. Acoustic Emission (AE) was used to help these programs by giving a real time understanding of the mechanical behaviour of the structure under the applied controlled loadings. This part describes some practical applications.

MULTIPLE LAUNCH CAPABILITY FOR SATELLITES ON ARIANE PROGRAMS These structures: SYLDA for ARIANE III manufactured by Aerospatiale (France), SPELDA for ARIANE IV manufactured by British Aerospace (UK), SPELTRA for ARIANE V manufactured by DORNIER (Germany), have a sandwich construction made with CFRP thin skin, aluminium honeycomb and some circumferential rings in aluminium. AE was used during the qualification tests (loadings in different axes in order to simulate launch and flight conditions) and also for production quality control.

ARIANE V PROGRAM In the ARIANE 5 program, AE has been used for the qualification of several metallic and composite structures (figure 6) such as: CFRP Pressure vessels: This procedure has been applied on more than 30 hydrogen pressure vessels and criteria were confirmed by a burst test of one vessel. These composites high-pressure vessels are developed as pressure storage elements for the control mechanisms of central stage engine (GAM) and the booster stage nozzles (GAT). The tanks are designed with an inner steel liner and a wound carbon fiber composite. As a production test, for quality and safety purposes, AE is used to monitor the vessel behaviour during a prestreching and then, during the proof test. The change of the slope of the history curve and the Felicity ratio are used as criteria for the acceptance of the vessel.

185 ARIANE 5

'

SPELTRA

"!

Motor Case (Aluminium)

Boosters GAT & GAM

(Composite Pressure Vessels)

Figure 6

Structures of Ariane V tested by Acoustic Emission

186

CERTIFICATION OF CFRP WING STRUCTURE OF AIRCRAFT Test on several samples structural elements has been done in order to develop a safeguard criteria for a wing's element of AIRBUS A340. Test on full size structure, with acoustic emission real time analysis in order to avoid any catastrophic failure before the complete certification of the flap, has been conducted with full success up to the ultimate load.

ICOMPOSITE HIGH SPEED TRAIN VEHICULE AE monitoring of qualification tests on new composite high-speed train vehicle has been performed. Several loading cases have been applied in order to simulate the maximum stresses to the structure under the operating conditions. AE showed several high intense located sources, a severity analysis indicated a non-critical behaviour and so the program has been completed without any failure. A further investigation at the locations of the AE sources showed debonding damage.

ICONCLUSIONS In all above described applications, AE results provide the following benefits: 9 Real time warning if significant AE activity occurs in order to avoid any unexpected failure by stopping the test, 9 Global examination of the structure with location of AE active zones where it is necessary to perform some other non destructive tests to quantify the damage, 9 Validation of the safety factor from the analysis of the history of the AE activity and intensity versus applied load, 9 Confirmation there is no significant damage present at the end of one loading case so the structure will sustain the next application of loads

187

ACOUSTIC EMISSION EVALUATION IN CONCRETE SHIGENORI YUYAMA, Nippon Physical Acoustics Ltd., 8F Okamoto LK Bldg., 2-17-10, Higashi, Shibuya-ku, Tokyo 150-0011

Japan.

MASAYASU OHTSU, Department of Civil Engineering and Architecture, Faculty of Engineering, Kumamoto University, 2-39-1, Kurokami, Kumamoto 860-8555 Japan. ABSTRACT A series of studies has been made of acoustic emission (AE) behavior in concrete to demonstrate the usefulness of AE techniques for evaluation of structural integrity. Fracture mechanisms were studied in reinforced concrete (RC) beams with a single reinforcing bar and concrete beams reinforced with fiber plastic sheets by the use of a moment tensor analysis. Cyclic loadings were applied to large repaired RC beams, beams deteriorated due to corrosion of reinforcement and a corner of RC rigid frame. Fatigue tests were made in RC slabs used in a highway bridge. Field tests were conducted in RC beams with different damage levels in an aging dock, RC slabs of a highway bridge in service, RC beams of a high speed railway bridge in service, RC foundations under simulated seismic loadings and an arch dam during construction cooling and grouting. The laboratory tests showed that the Kaiser effect starts to break down when shear cracking starts to play a primary role. It has been demonstrated that high AE activity is observed during unloadings after serious damage (slips between the concrete and the reinforcement or those between the original concrete and the repaired part) has occurred. The moment tensor analysis was shown to be very effective for quantitative evaluation of fracture mechanisms and processes in concrete. This article reviews the results of the laboratory tests and field applications and discusses proper methods for AE evaluation of structural integrity in concrete. KEYWORDS Acoustic emission, cyclic loading test, evaluation criteria, fatigue, Kaiser effect, moment tensor analysis, concrete, structural integrity INTRODUCTION In recent years the deterioration and cracking of concrete structures such as bridges and buildings has been a significant problem. Proper techniques for the inspection of damaged structures are important in making rational decisions regarding rehabilitation, repair or replacement. Thus, the development of techniques to evaluate degradation of concrete structures in long-term service has been one of the most important issues for an effective maintenance program.

188 Since acoustic emission (AE) is very sensitive to the initiation and the growth of cracks in materials and structures, it has been widely used to evaluate cracking processes in concrete specimens and structures. There are two ways to analyze AE data acquired by conventional AE instrumentation. The first one, which has been widely and more frequently employed, is parameter analysis. It analyzes relative AE activities based on the measurement of such features as hit, count, energy, amplitude and so forth. The second, which has made a remarkable progress for the last two decades, is quantitative waveform analysis such as source characterization [1,2,3] and moment tensor analysis [4]. Quantitative information on AE sources is derived by applying theoretical treatment to the waveforms recorded by a multi-channel transient recorder. A series of tests has been conducted to demonstrate the usefulness of AE techniques for fracture studies in laboratory. Fracture mechanisms were studied in RC beams with a single reinforcing bar [5] and concrete beams reinforced with fiber plastic sheets [6] by the use of the moment tensor analysis. Cyclic loadings were applied to large repaired RC beams [7], beams deteriorated due to corrosion of reinforcement [8] and a comer of RC rigid frame [9]. Fatigue tests were performed in an RC slab used in a highway bridge [10]. The laboratory tests indicated that the Kaiser effect starts to break down when shear cracking starts to play a primary role. It was shown that high AE activity is observed during unloadings after serious damage (slips between the concrete and the reinforcement or those between the original concrete and the repaired part) has occurred. The moment tensor analysis was shown to be very effective for quantitative evaluation of fracture mechanisms and processes in concrete. Field applications have been made for various types of concrete structures to investigate the effectiveness of AE techniques for the evaluation of structural integrity. Tests were carried out in RC beams with different damage levels in an aging dock [11], RC slabs of a highway bridge in service [10], RC beams of a high speed railway bridge in service [12], RC foundations under simulated seismic loadings [13] and an arch dam during construction cooling and grouting [14]. A concrete beam integrity (CBI) ratio, the ratio of the load at onset of AE and the maximum prior load, has been proposed as an effective criterion to measure the severity of the damage induced in concrete structures [15]. The high AE activities during unloadings have been shown to be an effective index to estimate the level of deterioration. This article reviews the results of the laboratory tests and field applications. Test procedure and evaluation criteria are discussed in conjunction with cracking mechanisms and AE sources. LABORATORY TESTS

RC Beams with a Single Reinforcing Bar [5] Shown in Fig. 1 is a configuration of the specimen used for the cyclic bending test in laboratory. A single reinforcing bar of 19 mm dia with lateral lugs is encased eccentrically in the rectangular concrete beam. Concrete cover (depth of reinforcing bar) i s 30 mm. Compressive and tensile strengths of the concrete were 36.2 and 3.5 MPa, respectively. Six PAC R15 (150 kHz resonant) sensors were attached on the specimen to perform both a moment tensor analysis using the SIGMA code [4] and parameter analysis. The specimens were subjected to repeated four-point bending loadings by a strain-control type machine. The maximum load of each loading cycle was increased gradually in order to investigate the

189 relationship between the cracking process and AE behavior. Figure 2 presents the relationship between the number of AE hits and the applied load. AE signals are detected at a lower load than the maximum prior load (49 kN) during the second loading. Accordingly, the Kaiser effect breaks down during the second loading. It was shown that the Kaiser effect starts to break down when the crack width exceeds 0.12 mm. The breakdown of the effect becomes clearer as the cracking progresses in the third, fourth and fifth loadings. High AE activities are observed during the third, fourth and fifth unloadings. The moment tensor analysis revealed that the contribution of shear cracks increases as the breakdown of the Kaiser effect becomes clearer with the progress of the fracture. It was also indicated that high AE activity is observed during the third, fourth and fifth unloadings after the maximum width of the surface cracks has exceeded about 0.25 mm. The moment tensor analysis found that the shear cracks generated near the reinforcing bar is responsible for this activity. The origin of these emissions was attributed to rubbings between the faces of the existing cracks or friction between the reinforcement and concrete. i[

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190 Concrete Beams Reinforced with Fiber Plastic Sheets F6J

Center notched concrete beams reinforced with carbon fiber (CFRP) and glass fiber (GFRP) reinforced plastic sheets were subjected to three-point bending. Shown in Fig. 3 is a configuration of the specimen. The sheets were externally bonded on the bottom of the specimen in the tensile side. A concrete specimen with no reinforcement was also tested for reference. AE sensor for parameter analysis ~t

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191 Given in Fig.5 are results of the moment tensor analysis. It is observed that cracks are generated in a small region at the notch tip in Stage I. In Stage II, the initiation and extension of an inclined crack (45 - degree) occur from the tensile zone to the notch tip along with the main crack extension. Finally cracks are generated near the interface between the fiber sheet and concrete in Stage III. It was found that the contribution of shear cracking increases significantly as the fracture progresses from Stage I to Stage III. These results were in good agreement with visual observations of the surface cracks. It was thus shown that the moment tensor analysis is very effective for quantitative evaluation of fracture processes and mechanisms.

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192

Repaired RC Beams [7] The configuration of the specimen and the locations of displacement transducers and AE sensors are shown in Fig. 6. Six PAC R15 sensors were placed lineally on the top plane of the specimen. The repaired part is in the tensile side of the specimen. The depth and length of the repaired part are 100 mm and 2200 mm, respectively. In addition to steel bars as reinforcement, stirrups were embedded in the specimen to prevent beams shear failure. The specimens were subjected to repeated four point bending loadings by a strain-control type machine. During each loading, measurements of AE, crack width, slip length between the repaired part and the original part, and strain of concrete and reinforcement were made by using AE sensors and two different types of displacement transducers. These measurements indicated that the initiation of the early tensile microcracks, main tensile cracks, local slips, and large-scale slips are clearly detected by AE hit measurement. It was shown that once large-scale slips have occurred at the interface between the original concrete and the repaired part, AE starts to emanate at much lower load than the previous maximum load, that is, the Kaiser effect no longer holds for the next loading and high AE activity can be seen even during unloading. Thus, the breakdown of the Kaiser effect and the high AE activity during unloading can be a good indicator for the occurrence of large-scale slips in repaired RC beam. Amplitudes of all hits are plotted versus time together with the displacement in Fig. 7. It is obvious that the initiation of the early tensile microcracks or the local slips and the mechanical rubbings of the interlocked faces due to large-scale slips gave amplitude levels between 40 and 60 dB, while the initiation of the main tensile crack at 38.2 kN produced very high amplitudes that reached nearly 80 dB. Thus, the different AE sources could be clearly distinguished by comparing the amplitude data with the results of the visual observation and the measurement by displacement transducers.

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193 A concrete beam integrity (CBI) ratio, given below, was proposed as a criterion to measure the severity of damage induced in repaired concrete beams. CBI ratio = load at onset of AE / maximum prior load In the field of fiber-reinforced plastic (FRP) structures, AE tests have been widely used to evaluate structural integrity and testing has been standardized by ASME Code, Section V, Article 11. In this code, the Felicity ratio obtained from the ratio of the load at onset of AE and the maximum prior load gives the criterion to measure the severity of previously induced damage. It has been shown that the Felicity effect, which is referred to as the breakdown of the Kaiser effect, is an indication of defects. The Felicity ratio has been well accepted to examine structural integrity of chemical plant equipment such as pressure vessels, tanks and piping. However, beams, pillars, columns, and slabs are inspected in concrete structures. In the case of chemical plant equipment, structures are loaded by pressurization. In contrast, concrete structures are subjected to tensile, shear and bending loadings by jacking or running a heavy vehicle. As shown in the test results, failure mechanisms vary with the progress of damage in RC beams. It is obvious that the decrease of the CBI ratio is related to the generation and propagation of shear cracks. Thus, the ratio can be a practical index for evaluating structural integrity of RC beams. Taking these points into consideration, the CBI ratio was introduced [16]. Listed in Table 1 are CBI ratios for each loading cycle, based on AE hit rate activity. The ratios obtained by AE energy rate activity for all channels are also given in the last column. As shown in Table 1, the CBI ratio decreases from the fifth loading after large-scale slips have occurred due to the fourth loading along the interface between the original concrete and the repaired part. It continues to decrease as the damaged areas grow. It is known that the occurrence of large-scale slips is an essential feature for damage that may result in a serious disaster in repaired concrete structures. As shown above, the CBI ratio has very good correlation with the onset of large-scale slips and the growth of damaged areas. Thus, the CBI ratio can be a very useful and effective criterion to measure the severity of damage induced in repaired RC beams. ~AJ~I .... I .... i .... I .... ) .... I .... i .... I .... ) .... I .... l .... I .... i .... I .... i .... I .... i .... I .... i ....

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194 It was also revealed that high AE activity is observed during unloadings once large-scale slips have initiated between the original concrete and the repaired part. The source of these emissions was ascribed to mechanical rubbings between the interlocked faces introduced by the large-scale slips. Table 1 Concrete Beam Integrity (CBI) ratios during the repeated loading tests of repaired RC beams. CH2

CH3

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(hit)

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(hit)

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0.69

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0.80

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0.50

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RC Beams Deteriorated due to Corrosion of Reinforcement [8] Shown in Fig. 8 are dimensions (cm) of the specimen and sensor locations. Six PAC R6 (60 kHz resonant) sensors were attached to the specimen to perform the moment tensor analysis as well as AE parameter analysis. The lower quarter part of the specimen was

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195 immersed in a 3% sodium chloride solution and an anodic current was galvano-statically charged to the main steel bars until the maximum width of surface cracks due to corrosion of the bars reached 1 mm or 4 mm. Thus, three different types of specimens i.e. specimen with no corrosion damage and those with the surface cracks determined as above were subjected to repeated four-point bending loadings.

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196 Indicated in Fig. 9 are relationships between AE hits and the applied load for the specimens with the different deteriorated levels. It is observed that the Kaiser effect starts to break down during the third loading in the case of the specimen with no corrosion damage. However, it tends to break down during the second loading in the case of the deteriorated specimen (crack width 1 mm) and the breakdown is very clear during the second loading in the heavily deteriorated one (crack width 4 mm). Table 2 Concrete Beam Integrity (CBI) ratios for the second and the third loadings.

Loading cycle

II

III

No damage

1.00

0.75

Crack width 1ram Crack width 4ram

0.71 0.28

0.49 0.10

CBI ratios for the second and the third loadings are summarized in Table 2. It is obvious that the CBI ratios exhibit smaller values than 1 because of the breakdown of the Kaiser effect during the second loading in the deteriorated specimens. It is also seen that the ratio becomes smaller as the deterioration due to corrosion of the reinforcement becomes greater. During the third loading, the CBI ratios are smaller than 1 for all the specimens. Again the ratios exhibit smaller values in the specimens with the greater deterioration induced by the corrosion. Thus, it has been confirmed that the CBI ratio can be an effective criterion to measure the severity of the damage due to corrosion of the reinforcement in RC beams. It is also observed in Fig. 9 that different levels of AE activity are detected during unloadings, depending on the different damage levels. In the specimen with no corrosion damage, relatively high AE activity is first observed during the 2nd unloading, as shown in Fig. 9 (a). However, some AE activity is already detected during the 1st unloading in the case of the deteriorated specimen (crack width lmm). High activity is seen during the 2nd unloading (Fig. 9 (b)). Quite high AE activity is observed during the 1st and the 2nd unloadings in the heavily deteriorated specimen (crack width 4 mm), as seen in Fig. 9 (c). Thus the levels of AE activity during unloadings reflect the damage levels induced in the specimens. Since high AE activity corresponds to the occurrence of serious damage, it can be an effective index to estimate the level of deterioration.

Corner of an RC Rigid Frame E9_] The growth of tensile cracks, shear cracks and bond failure of the reinforcement in an "L" shaped RC rigid frame was monitored by AE. The results were compared with visual observation and the measured value of crack widths and deflections. The apparatus for applying load and measuring deflections is presented in Fig. 10. From the tests, it was shown that different AE sources could be clearly discriminated by comparing the AE parameter data with the results of visual observation and crack width measurement. It was revealed that the Kaiser effect exists so long as the width of tensile cracks is smaller than about 0.15 - 0.20mm. However, it fails after the width has reached this value and with the onset of shear cracks. The Kaiser effect, thus, becomes a very effective method for estimating the level of deterioration in concrete structures. Moment tensor analysis was applied to orientations in the fracture process. Given orientations determined from eigenvectors components and crack directions presented

classify crack types and to determine crack in Fig. 11 are the results of the analysis. Crack obtained by decomposition of moment tensor as crack-opening vectors for tensile cracks and

197 fault-motion vectors for shear cracks are in good agreement with the visual observation of the surface cracks. The moment tensor analysis showed that shear cracks start to appear and play a primary role when the Kaiser effect fails. It is known that shear crack initiation at the interface between the reinforcement and concrete or that between the repaired part and the original concrete is vitally important in evaluating structural integrity of concrete members. Therefore, the moment tensor analysis which can distinguish shear cracks from others is a very efficient method to assess damage levels in concrete structures.

9

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Fig. 11 Results of the moment tensor analysis.

Bearing

198

Fatigue Test of an RC Slab Used in a Highway Bridge [10_] Applicability of AE technique for evaluation of fatigue damage in an RC slab was studied in laboratory. AE was monitored from the initial loading to the final failure under fatigue loadings in a model RC slab of a highway bridge. Figure 12 indicates a relationship between number of loading cycles and crack density defined as crack length per 1 square meter. Four stages are clearly observed in the cracking process. Shown in Fig. 13 are histories of AE hit rate, amplitude and the maximum load as a function of number of loading cycles. High AE activity is observed in the early time of Stage I due to the initiation of early cracks. However, it diminishes rapidly and then increases again as the crack density increases. In stage II, the activity slowly increases showing some instability, while it becomes stable in Stage III. In Stage IV, it starts to increase rapidly from N = 8.8 x 105, i.e. just before the final failure (N = 9.17 x 105). It is thus shown that the process of the final failure (the transition from Stage III to Stage IV) under fatigue loadings can be predicted and evaluated by monitoring AE signals. The initiation of the final failure corresponds to the time when the AE activity increases significantly in Stage IV. It is known that AE activity under fatigue loadings strongly depends on the loading phase [17]. It has been reported that the AE signals detected near the maximum load is mainly due to main crack extension and called Peak Load AE. Meanwhile the AE activities observed during unloading and re-loading are considered to be from mechanical sources such as frictions due to closure and opening of the crack faces and called Closure and Opening AE, respectively. Given in Fig. 14 is the relationship between loading phase and AE activity in terms of hit rate from Stage IV to the final failure. The most distinct fact seen here is that the activity of closure AE increases very rapidly at low load levels as the cracking process approaches the final failure. This is because many AE signals due to mechanical causes are detected since many cracks have already existed in the specimen at this stage. This result suggests that the process of the final failure can be practically monitored by detecting the closure AE observed near the minimum load. Thus precise analysis of relationship between the loading phase and AE activity makes it possible to evaluate fracture processes under fatigue loading.

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200 FIELD APPLICATIONS

An Aging Dock [11] Structural integrity of RC beams was evaluated in an aging dock. Shown in Fig. 15 is a cross section of the tested pier. A repaired beam and a damaged one without repair were subjected to three loadings by running a dump truck with three different load levels i.e. empty (ll3kN), half load (142kN), and full load (171kN). Figure 16 schematically illustrates how the tests were performed. Two PAC R15 (150kHz resonant) and R6 (60kHz resonant) sensors were attached to the beams. However, only the data collected by R6 sensors could be analyzed since there were no significant AE signals detected by R15 sensors due to high attenuation at higher frequencies. A strain gage was attached to the main reinforcing bar to measure strain changes during the loadings. Two cracks the maximum opening width of which reached 0.8 mm were visually observed on the surface of the unrepaired beam and measurements of corrosion potential confirmed that serious damages due to corrosion of the reinforcement existed. Strain gage measurements showed that the strain change is much larger in the unrepaired beam than the repaired one for the same loadings. 25.00 3.00

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AE s e n s o r Loading method

Fig. 16 Schematic illustration of the loading test.

201

AE hit rate, strain and amplitude histories for the damaged beam are given in Fig. 17. Although the repaired beam (no damage) was very quiet, high AE activity was observed since the first loading in the unrepaired one. The Kaiser effect breaks down during the third loading and high AE activities are seen during the second and third unloadings. The AE source during the third unloading was thought to be frictions due to slips between the reinforcement and the concrete. The amplitudes from this source are smaller than 60 dB, as shown in Fig. 17. The CBI ratio is smaller than 0.6 during the third loading. Thus, it has been shown that the CBI ratio and the AE activities during unloadings are very good indicators for extensive deterioration in RC beams.

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RC Slabs of a Highway Bridge in Service [10} AE was monitored under live loads in RC slabs of a highway bridge which had been in service for more than 27 years. Figure 18 schematically illustrates the structure and the location of AE sensors. The dimensions of the slabs are 4.8m in length, 3.4m in width and 180mm in thickness. Asphalt pavement of 60mm in thickness is placed on the surface of

202 the slabs. A median strip covers almost half part of the upper side of the slabs. were monitored continuously for two hours under live fatigue loads in service.

AE signals

Number of AE hit increased almost linearly as a function of time during the continuous monitoring in CH1. The same tendency was observed in CH2, CH3 and CH4 sensors. Other AE features such as energy and count gave the same kind of history. Therefore, it was thought that there was no significant change in the live loads due to traffic during the monitoring and AE measurement was carried out under a stable condition.

Median Strip Stringer

4.8rr

Main Girder

4.8r

3.4m

Fig. 18 Schematic illustration of the bridge in service. Note that two PAC R6 (60kHz resonant AE sensors) are attached to each slab. Figure 19 indicates cumulative number of AE hits (a) and AE energy (b). The number of detected hit is almost the same in CH1, 3 and 4 (about 8 x 10 4) but it is about 50 percent greater in CH2. In contrast, AE energy exhibits about 10 6 in CH2 and CH4, while 2.6 x 10 7 and 3.0 x 107 are detected in CH1 and CH3, respectively. Thus the detected AE energy is more than one order of magnitude greater in CH1 and CH3 than in CH2 and CH4. This is because CH1 and CH3 sensors detected the signals generated by live loads due to the traffic since the sensors were attached to the underside of the traffic lane. However, AE signals due to indirect causes such as vibration of the structure were thought to be detected in CH2 and CH4 because the median strip was on the area where the sensors were placed.

203 x103 20o

I

I Number of AE Channel

xlo,-

so l

(b)

L_

1~. o

I '

i

'

~

9

'

~

'

1

'

.

Number of AE Channel

Fig. 19 Cumulative number of AE hits (a) and AE energy (b). Comparison of AE energy in CH1 attached to Slab A and that in CH3 attached to Slab B indicates that it is about 15% greater in CH3 than in CH1 although AE measurement was made under the same traffic condition. Visual observations of crack density found that the damage level of Slab B is greater than Slab A. Accordingly, the difference of the detected AE energy between CH1 and CH3 was attributed to the difference of damage levels between the slabs.

RC Beam of a High Speed Railway Bridge in Service [12] AE was monitored in an RC beam of a high speed railway bridge in service. Figure 20 schematically illustrates how the tests were performed. Six PAC R6 (60kHz resonant) sensors were placed on two sides of the beam. A strain gauge was attached to a main bar to monitor loading process. The beam was shown to be repeatedly loaded and unloaded due to train pass. Accordingly, the beam has been exposed to fatigue loads since its construction. Given in Fig. 21 are histories of AE hit rate and strain during a pass of one car. It is clearly observed that AE activities are detected during loading and unloading. It should be noted that no activity is seen at the maximum loading phase. Visual observation found that the maximum width of surface cracks reached 0.3mm. As has been mentioned in the previous case study, AE activity is strongly dependent on loading phase under fatigue loading. It has been shown that the AE activity due to crack extension is detected near the maximum load. Since no AE activity was detected at the maximum loading phase, it was concluded that no crack extension was taking place in this beam. The AE activities observed during loading and unloading were attributed to frictions of the existing crack faces.

RC Foundation under Simulated Seismic Loading [13] After the great Hanshin earthquake, numerous studies have been made to evaluate damage levels, toughness and deformation characteristics of RC columns exposed to the earthquake shock. RC columns were subjected to simulated seismic lateral loading under different axial loads. AE was monitored to characterize the seismic behavior of column foundation.

204

lr -1

/r L

Monitored a r e a and i s e n s o r location

.J

ross s e c t i o n i

C r o s s section

Central span

Fig. 20 Schematic illustration of AE monitoring in RC beam of a high speed railway bridge in service.

60 40

12

Strain - - A E Hit Rate I

9

(3 G) 0 0

v

6

.=__ 20 L..

\

0 -20 104.5

104.7

104.9

105.1

---I:-

3

"r" LU <

0 105.3

"[]me (see)

Fig. 21 Histories of AE hit rate and strain during a pass of one car. Configuration of the specimen and the coordinates system for sensor locations are shown in Fig. 22. The specimen tested in this study consists of an RC column and foundation. The dimensions are 40(W) x 40(D) x 170(H) cm and 100(W) x 200(D) x l15(H) cm, respectively. Figure 23 shows lateral loading schedule applied to the specimens. The specimens were subjected to three cycles for each of the maximum predetermined displacements, corresponding to the drift angles. Loading stages are schematically illustrated in the figure. Six PAC R6 (60kHz resonant) sensors were employed to detect AE signals generated in the foundation.

205 AE parameter analysis showed that there is a large difference in the AE activity between Case 1 (axial load 156.9kN) and Case 2 (1.58MN) in the early stages of the cyclic loadings. AE activity in terms of AE hit and amplitude was much higher in Case 1 than in Case 2 in the early stages of the damage process. Typical results from the moment tensor analysis are given in Fig. 24. The analysis showed that the ratio of shear cracks increases from Stage I (32%) to Stage VI (47%) in Case 1, while the ratio decreases from Stage I (42%) to Stage III (20%) in Case 2. It was thus

Axial Load !, eoo ,

.~ ! ~

Loadingpointz~

"

Axial Load . T s$0 Loadingdirection. !

I

~

I I

Lo~ing ~

I,000

---~X

direction

2.100

i

I

i

I

(mm)

Fig. 22 Configuration of the specimen and the coordinates system.

160 120

E vE

80

r-Q)

40

o 0 i5 -40 9 -80

Loading Stages

ii ^II^^t IAAIAjt//!/lll

_.1 -120

Drift angle (rad.)

1/

-160

Time

Fig. 23 Lateral loading schedule.

/

~ 1/13

206 Stage I

I

t

1

I

I %/

k

%

z .~___> y

0

o

Stage IV

I

%

$

+

I

".,k

<

•

-~ •

zL_~y

t

•

z ~___~ x 0

0

Fig. 24 Results of the moment tensor analysis for Case 1. revealed that the cracking behavior represented by the contribution of shear cracks is quite different between Case 1 and Case 2 in the early stages of the fracture process due to the different axial loads.

Arch Dam during Construction Cooling and Grouting [14] AE monitoring was conducted in order to ensure safe construction of an arch dam severe climate conditions. The AE was detected during the secondary cooling in the time while the construction was suspended due to low temperature and heavy snow. spring time just before the construction restarted, AE was monitored during grouting joint of dam blocks.

under winter In the into a

A multi-channel digital signal processing system was used to perform a quantitative AE waveform analysis based on the moment tensor analysis as well as the conventional AE parameter measurement. Low frequency AE sensors (15kHz resonant) were employed to detect AE signals since the signals with ordinary frequencies attenuate greatly during the propagation in concrete of the dam. Twelve sensors were buried from the dam surface and four were mounted on the corridor floor inside the dam.

207 Shown in Fig. 25 is elevation view of an arch dam under construction. The top of the dam is 244m long and the height from the bottom is l16m. It is thought that this is the largest above ground structure ever tested by AE. The parameter analysis made during the secondary cooling showed that AE activity increases almost linearly as a function of time. This result suggests that the activity is related to mechanical or extraneous noises. AE activity observed during grouting, in terms of AE hit, count and energy, is presented in Fig. 26. Significant AE activities are observed at lh 47min after the grouting started. This

Right-hand side

Left-hand side Top width 244.0m ~

Block 9

Block 8 9239. Om

~

~_-_~ ~

I

!

d

EL.150.Om

V_..

EL. 164.0m

EL. 123. Om

........

Fig. 25 Elevation view of the tested arch dam. Mar

2000g. U

~

- Water f i l l i n g !

1200Q

~1 0 .2E6.

start L

<

RQQQ

~

- y

4E5,

l

d '

. '

.

. . . '.eleod

.

Time(Scc)

IEti.

! '

'

'361eo~

lET

8QQQ~

4000Q

sEs,

Re-blocking s t a r t

aooo

Gg:G1:31

9 . 6E6

Blocking s t a r t

166661

2Q.97

8

Tim~)

'

'366o~

'

'~66oo

8E6

t/J 4E6 2

/

2 0 0 0 G ~ '3&~o~ Timc(sec)

Fig. 26 AE activity observed during the grouting.

208 is the time when the water filling started at the neighboring joints. High AE activity is also seen at 4h 10min when the blocking started. Some activities are observed at approximately 6h 30min at the start of re-blocking. However, overall AE activities are quite low except at the particular stages above. It is reported that these activities should be related to the local instability (pressure distribution)in the joint due to the inhomogeneous penetration of cement milk. Neither critical AE activity nor intense AE cluster were found in the AE monitoring. Therefore it was concluded that the dam was safe during both the secondary cooling and the grouting. DISCUSSIONS Cracking Processes and AE Behavior

AE behavior has been studied during cracking processes in various types of RC beams. Each fracture stage in reinforced concrete is schematically illustrated in Fig. 27.

Fracture

Stage $

Load& Reinforcement

.l'l.?

"I. "i. .I "l "

Tensile microcracks

Internal cracks ; .9/ , / / , ( / / / / / , ( / / / " / . . . ,

9~ : 4 .

r

,, ; , ( ~

r

,I,

,I,

I/'/////d//'/////// :1-:.'.-[

Secondary tensile cracks ] or shear cracks

" -T.

"-~ "-. ~

"//,~//,t'/.,d(//'..~'/,~.

- T& :

" "

Slips between the reinforcement and the concrete

Slips between the repaired part and the original concrete

Repaired Part

Fig. 27 Schematic illustration of fracture stages in reinforced concrete.

209 It has been reported that AE hits with medium level of amplitudes (40 to 60 dB) are emitted when early tensile microcracks initiate as the first stage of the fracture. If main tensile cracks are generated, AE signals with larger amplitudes (80 to 100 dB) are detected. After the width of surface cracks has exceeded a certain value as the damage progresses to the second stage of the fracture, internal cracks such as secondary tensile cracks or shear cracks start to initiate near the interface between the concrete and the reinforcement. In the cyclic loading test [9] of an L-shaped rigid frame, the Kaiser effect started to break down after the crack width reached 0.15 to 0.2 mm and high AE activity was seen during unloadings after shear cracks started to play a primary role. It has been thus shown that the Kaiser effect no longer exists and high AE activities are observed during unloadings when the fracture progresses to the second stage. This is because shear cracks start to play a primary role around the interface between the reinforcement and concrete, generating AE events due to mechanical rubbing of crack faces during loading and unloading. The amplitudes due to this mechanism are smaller than 80dB, as indicated in Figs. 7 and 17. When the fracture progresses to the third stage, slips (shear cracks) start to take place during loading and unloading between the reinforcement and concrete or between the repaired part and the original concrete. The amplitudes from this source are no larger than 60 dB as seen in Figs.7 and 17. Thus, the damage level of RC beams is strongly related to the initiation of shear cracks. Therefore, it should be possible to evaluate structural integrity of RC beams by analyzing emissions due to shear cracking.

Moment Tensor Analysis It has been shown that the moment tensor analysis using the SIGMA code is very effective to analyze a grout injection process [18] of dam and fracture processes of concrete specimens [5, 6, 8, 9] and structures [13, 14]. This analysis found that the Kaiser effect starts to break down and high AE activities are observed during unloadings after the opening width of the surface crack has exceeded 0.12 - 0.2mm and shear cracks start to play a primary role in reinforced concrete. The essential condition to obtain good and meaningful results from the analysis is to have accurate 3D (3 dimensional) source location with more than six sensors. So far as 3D source location is possible, any sensor positioning is applicable. Generally specimen, placed at However,

the analysis can be easily performed in laboratory specimens. Compact tension for instance, has an ideal configuration for this analysis. AE sensors should be as many sides as possible, so that they can cover the whole cracking zone. the side with a notch should be avoided to have accurate source location.

If the specimen is a thin plate such as a certain type of tensile specimen, 3D source location is impossible. In such a case, SIGMA 2D (SIGMA analysis for 2 dimensional sensor placement with four sensors) can be applied [19]. If we want to monitor a known defect area in a concrete plate or a vessel wall and only have access to the outside of the structure, all the sensors must be placed in the outside plane. The moment tensor analysis is possible in such a case. It has been reported that the analysis was successfully made in a large concrete block with all the six sensors placed on the top plane [20]. Special algorithm for 3D source location was necessary in this case. If a displacement sensor which measures surface displacement is available as an additional

210 seventh sensor, we can evaluate a volume of the formed crack and its time history by inverse analysis. Angles, sizes, and formation velocities of fracture facets due to hydrogen assisted cracking of a sttainless overlay have been quantitatively evaluated [21]. Because the cracks are produced inside the material and the location accuracy of the AE events (solvable waveform sets) is limited, the verification of SIGMA solutions cannot be performed easily by visual aid. To estimate the errors of SIGMA solutions and screen out poor solutions, a post-analysis procedure has been developed [22]. Synthesized AE waveforms are first computed from the crack kinematics determined from the SIGMA solutions obtained by the experiments. Then, the SIGMA procedure is applied to the synthesized waveforms. Finally, the solutions obtained from the experiments are compared with those of theoretical solutions determined from the synthesized waveforms. In this way, it is possible to screen out poor solutions and to verify the reliability of the solutions determined from the experiments. Thus, the moment tensor analysis is a very promising technique for quantitative AE evaluation. It offers a wide range of applications for not only characterizing materials but also evaluating structural integrity of various types of structures. At present this quantitative method is easily applicable in both laboratory and field with a commercially available AE instrument and software such as the SIGMA code. Evaluation Criteria and Test Procedure

In evaluating the structural integrity of concrete members, shear crack initiation at the interface between the reinforcement and concrete or that between the repaired part and the original concrete is vitally important. Therefore, the detection of shear cracks (slips) can be a practical criterion to measure the severity of damage induced in RC structures. As has been shown so far, the breakdown of the Kaiser effect and the high AE activity during unloadings correspond to the occurrence of serious damage such as the slip between the Table 3 An example of evaluation criteria for damages induced in RC beams.

Fracture Stage Early microcracks Main tensile cracks Secondary tensile cracks Internal shear cracks Slips between reinforcement and concrete Ill or Slips between

repaired part and original concrete

Damage Level (Crack width: W) Low

(W<0.12 ~-0.20mm) Medium (0.12 "~ 0.20mm

<W)

Amplitude (dB)

CBI Ratio

40 "~ 60

Larger than

80 ~

100

40 "~ 80

Low

Medium

Smaller than

High (0.5ram<W)

0.8 "~- 0.9

AE Activity

during Unloading

40 ~

60

0.8

High

211 reinforcement and concrete or that between the repaired part and the original concrete. Therefore, they can be effective indices to estimate the level of deterioration. An example of evaluation criteria for damage induced in RC beams is given in Table 3. As demonstrated, the CBI ratio and the AE activity during unloadings, which can be obtained under repeated loadings with increasing maximum loads, are very useful for evaluating structural integrity of RC structures. In the present series of tests, AE signals were detected by PAC R15 (150 kHz resonant) and R6 (60 kHz resonant) sensors, setting the system examination threshold at 40 dB. An attenuation study made prior to the tests showed that R15 and R6 sensors can cover the areas of approximately 0.5 m and 2 m in the RC beams, respectively. Since the maximum sensor distance was smaller than 0.5 m for R15 sensors and 1 m for R6 sensors and the amplitude range of the detected AE signals was quite large, the effect of either the sensitivity of the AE channel or the attenuation in the structure on the CBI ratio is considered to be small in the examinations. However, dependence of minimum detectable AE on the system examination threshold must be always taken into consideration. Therefore, an attenuation study and sensitivity calibration of the sensors by a pencil lead break or electronic waveform generator with a pulse should be performed prior to test. Based on the series of the tests we have conducted, the following brief test procedure and evaluation criteria are proposed for AE tests of RC structures [15]. (1) AE Sensor Low frequency sensor (60 kHz resonant) : large area (whole structure) High frequency sensor (150 kHz resonant) : small area (2) Attenuation Study and Sensitivity Calibration An attenuation study and sensitivity calibration of sensors should be performed prior to test by a pencil lead break or electronic waveform generator with a pulse. (3) Loading Method Running a dump truck, trailer or a train 1st loading" Empty 2nd loading : Half loaded 3rd loading : Fully loaded A strain gauge should be preferably attached to the main reinforcing bar to measure strain changes during loadings. The maximum load should be determined in accordance with the allowable load. (4) AE Data Analysis Hit rate Count (Energy) rate Amplitude history AE activity (hit, count, etc.) vs. load Linear source location by low frequency sensor if possible Zone location by low frequency sensor or high frequency sensor (5) Evaluation Criteria Serious damages (slips between the concrete and the reinforcing bars or those between the concrete and the repaired part) in RC members are indicated by: CBI ratio < 0.8 High AE activity during unloading.

212 CONCLUSION

The development of reliable NDT methods to evaluate degradation of concrete structures is urgently requested. AE has been widely used to study materials characteristics and cracking processes in concrete specimens and structures. The AE tests conducted in laboratory and structures show that it is a very promissing technique for evaluation of structural integrity in concrete members. REFERENCES

.

10.

11.

12.

Waddley H. N. G., and Scruby C. B.(1983) Elastic Wave Radiation from Cleavage Crack Extension, Intern. J. Frac., Martinus Nijhoff Publishers, pp.lll-128. Kim, K. Y., and Sachse W. (1984) Characteristics of AE Signals from Indentation Cracks in Glass, Progress in Acoustic Emission H (JSNDI), Proc. 7th Intern. AE Symp., Oct. 23-26 1984, Zao, Japan, pp.163-172. Enoki, M., Kishi T., and Kohara S.(1986) Determination of Microcracking Moment Tensor of Quasi-cleavage Facet by AE Source Characterization, Progress in Acoustic Emission III (JSNDI), Proc. 8th Intern. AE Symp., Oct. 21-24, Tokyo, Japan, pp.763-770. Ohtsu, M. (1991) Simplified Moment Tensor Analysis and Unified Decomposition of Acoustic Emission Source: Application to in Situ Hydrofracturing Test, Journal of Geophysical Research, No. 1B, pp.6211-6221. Yuyama, S., Okamoto, T., Shigeishi, M. and Ohtsu, M. (1995) Quantitative Evaluation and Visualization of Cracking Process in Reinforced Concrete by a Moment Tensor Analysis of Acoustic Emission, Materials Evaluation, Vol. 53, No. 6, pp. 751-756. Li, Z. W., Yuyama S., Osawa I., Kimpara I., Kageyama K., and Yamaguchi K. (1998) Fracture Mechanics Study of Concrete Beams Reinforced with FRP Sheets by a Moment Tensor Analysis of Acoustic Emission, Fracture Mechanics of Concrete Structure, Proc. FRAMCOS-3, October 12-16, 1998, Gifu, Japan, AEDIFICATIO Publisher, pp.1863-1872. Yuyama, S., Okamoto, T. and Nagataki, S. (1994) Acoustic Emission Evaluation of Structural Integrity in Repaired Reinforced Concrete Beams, Materials Evaluation, Vol. 52, No. 1, pp. 86-90. Murakami, Y. and Yuyama, S. (1996) Acoustic Emission Evaluation of Structural Integrity in Reinforced Concrete Beams Deteriorated Due to Corrosion of Reinforcement, Progress in AE Fill (JSNDI), Proc. 13th Inter. AE Symp., Nov. 27-30, 1996, Nara, Japan, pp. 217-224. Yuyama, S., Okamoto, T., Shigeishi, M. and Ohtsu, M. (1995) Acoustic Emission Generated in Comers of Reinforced Concrete Rigid Frame Under Cyclic Loading, Materials Evaluation, Vol. 53, No. 3, pp. 409-412. Yuyama, S., Li. Z.W., Yoshizawa. M., Tomokiyo. T., and Uomoto, T. (2000) Evaluation of Fatigue Damage in Reinforced Concrete Slab by Acoustic Emission, Non-Destructive Testing in Civil Engineering-2000, Uomoto T. ed., Elsevier, 25-27 April 2000, Tokyo, Japan, pp.283-292. Kamada, T., Iwanami, M., Nagataki, S., Yuyama, S. and Ohtsuki, N. (1996) Application of Acoustic Emission Evaluation of Structural Integrity in Marine Concrete Structures, Progress in AE 8 (JSNDI), Proc. 13th Inter. AE Symp., Nov. 27-30, 1996, Nara, Japan, pp. 355-360. Yuyama and Li, Z. W. (2000) Acoustic Emission Method, Technical Report, Research Committee on Evaluation of Degradation in Concrete Structures, Institute of Industrial Science, The University of Tokyo, pp.51-57.

213 13.

14. 15.

16.

17. 18. 19. 20.

21. 22.

Yuyama, S., Li, Z. W., Ito, Y., and Arazoe, M. (1999) Quantitative Analysis of Fracture Process in RC Column Foundation by Moment Tensor Analysis of Acoustic Emission, Construction and Building Materials, Vol. 13, No. 1-2, pp.87-97. Minemura, O., Sakata N., Yuyama, S., Okamoto, T. and Maruyama, K. (1998) Acoustic Emission Evaluation of an Arch Dam during Construction Cooling and Grouting, Construction and Building Materials, Vol.12, No. 6-7, pp.385-392. Yuyama, S., Okamoto, T., Shigeishi, M., Ohtsu, M. and Kishi, T. (1999) A Proposed Standard for Evaluating Structural Integrity of Reinforced Concrete Beams by Acoustic Emission, Acoustic Emission : Standards and Technology Update, ASTM STP 1353, Vahaviolos, S. J. Ed., American Society for Testing and Materials, pp.25-40. Yuyama, S., Nagataki, S., Okamoto, T. and Soga, T. (1990) Several AE Sources Observed during Fracture of Repaired Reinforced Concrete Beams, Progress in AE V (JSNDI), Proc. lOth Intern. AE Symp., Oct. 22-25, 1990, Sendai, Japan, pp. 345 - 353. Yuyama, S., Kishi, T., and Hisamatsu, Y. (1984) Fundamental Aspects of AE Monitoring on Corrosion Fatigue Process in Austenitic Stainless Steel, J. Mater. Ener. Syst., Am. Soc. Met., Vol. 5, No.4, pp.212-221. Ueda, T., Ohtsu, M., Shigeishi, M. and Yuyama, S. (1991)AE Waveform Analysis for Rock Mass in Grout Injection of Dam, Proceedings of the 4th Worm Meeting on AE (ASNT), pp.223-229, Sep. 1991, Boston, MA. Shigeishi, M., and Ohtsu, M. t,1992) A SIGMA Analysis of the 2-Dimensional PMMA Model, Progress in Acoustic Emission I11 (JSNDI), Proc. 11th Intern. AE Symp., Oct. 26-29, 1992, Fukuoka, Japan, pp.211-217. Murakami, Y., Yuyama, S., Shimizu, T., Kouyama, H. and Matsushima, M. (1993) Deformation Behavior and AE Characteristics for Anchorage Pulling Tests on the Foundations of Power Transmission Pylons (Part II, Moment Tensor Analysis), Proceedings of the 9th National AE Conference (JSNDI), Oct. 1993, Okinawa, Japan, pp.143-150. Yuyama, S., Imanaka, T. and Ohtsu M. (1988) Quantitative Evaluation of Microfracture due to Disbonding by Waveform Analysis of Acoustic Emission, The Journal of the Acoustical Society of America, Vol. 82, No.3, pp. 976-983. Ohtsu, M., Arao, K. and Yuyama, S. (1994) Post-Analysis of SIGMA Solutions for Error Estimation in Reinforced Concrete Members, Progress in Acoustic Emission VII (JSNDI), Proc. 12th Intern. AE Symp. , Oct. 17-20, 1994, Sapporo, Japan, pp.411-416.

This Page Intentionally Left Blank

215

DIAGNOSIS OF MA CHINER Y USING A CO US TIC EMISSION TECHNIQUES Takeo Yoshioka Mechanical Engineering Laboratory 1-2, Namiki, Tsukuba, Ibaraki, 305-8564 Japan

ABSTRACT

Mechanical engineering has been progressively developed in the 20th century and machinery has supported the main part of production due to its development. Therefore, it is now very important to operate machinery safely and efficiently. Machinery comprises a lot of machine elements and there are more rolling bearings assembled into the machinery than any other machine elements. The probability that rolling bearings will fail in operation of machinery is high, because problems with machinery occur first at machine elements. This paper describes diagnosis of machinery, mainly rolling bearings using acoustic emission (AE) techniques. Many papers have described attempts to diagnose machinery due to the trends of AE generation with operating time. It is essential in the diagnosis of rolling bearings to make clear that the generation of AE corresponds to rolling bearing failure. This proves the merit and effectiveness of AE techniques. Hence, new location methods of AE source for rolling bearings developed in order to prove that AE were generated at failure positions of rolling contact fatigue are introduced. It was elucidated by the location methods that we could predict occurrence of fatigue failure when we observed AE emitted from bearings.

KEYWORDS

Acoustic emission, AE source location, Diagnosis, Rolling bearing, Rolling contact fatigue

216 INTRODUCTION An investigation report [ 1] on maintenance of machinery in production facilities in Japan was presented in1995. In the investigation questionnaires were sent to 511 factories representing 18 different industries. The report showed that failure of rolling bearings most commonly occurred from the viewpoint of tribological trouble and was at a level of about 29% as shown in Figure 1. Another paper [2] reported that failure rate of rolling bearing reached 47% in the case of the down time

k

being more than 3 h at a steel-making plant. From the facts described above, it is natural that rolling beatings are chosen as the research topic of diagnosis.

[Sliding bearing 6% ~ ~ . ~

~~,:

~

~

S l i d e ow a y

ISol

How easily do rolling beatings fail? One

of

the

reasons

why

machinery

problems are caused by failure of rolling bearings, as described above, is that the

Figure 1 Failure rate on machine elements in investigation report [ 1]

number of rolling beatings assembled into machinery is some orders of magnitude larger than any other machine elements.

For example, at a steel making plant [3], 9000 gearings and 2000 sliding bearings were assembled into the machinery while 100,000 sets of rolling bearings were there. Therefore, even if the failure rate of rolling bearings is equivalent to the rate of other machine elements, Aeotmtie

the

2.5*/'0

number

of

rolling

bearings

assembled into machinery increases the

IFluorescene 2.8%

number of bearing failures. Another

ITorque 6.3% ~ '

reason is that usage of rolling bearings near the limiting conditions of their

Rotational

performance increases the number of Temperature ~ 9.5% ~

I' ,

5::::::::: ::::::::::: :::::::::::::::::::::::::::::::::::::::::::::::

,,,~,,',",,',',',",:::::::::::::::::::::::::::::::::::::::::::::

~ Oil Analysis ~::i::i::ii::iiiiiililiiii::i!iiiiiii~

bearing failures, too, as the performance of rolling bearings is more understood than that of other machine elements. At present, the vibration method has mainly been used in the practical

Figure 2 Detection method of bearing failure[ 1]

field in order to detect failure of rolling bearings as shown in Figure 2. As compared with the vibration method, AE

217 techniques are minor and this is one reason why the merit of AE techniques is not widely known 9 This report describes research on diagnosis of rolling bearings using AE techniques and clarifies characteristics of AE techniques as diagnosis of rolling bearings. The report also considers views on diagnosis of rolling bearings 9

RESEARCH

ON

DIAGNOSIS

OF

ROLLING

BEARINGS

USING

ACOUSTIC

EMISSION TECHNIQUES Failure

35-

James, R., et al. [4] conducted the first research

on diagnosis

of

rolling bearings using AE techniques

30-

% 25-

~ 20 -

and succeeded to detect failures of bearings assembled into machinery in an oil refining plant. They measured the relative root mean square (r.m.s.)

~15

Na0 -

value and amplitude distribution of AE

5-

and found failures of rolling contact fatigue,

chemical

deterioration

of

corrosion grease

in

-

_. 9' - -

00

and

~

5

.....

i

I

10

15

V-

~

20

. . ~ - 8 days I I

25

I

I

30

35

40

Operation day, days

rolling

Figure 3 Trend of relative AE r.m.s. value in process of rolling contact fatigue failure at centrifuge [4]

bearings. Figure 3 shows an example of the trends of AE r.m.s, value until occurrence of rolling contact fatigue failure of the bearing assembled into 10 4

the centrifuge. The AE r.m.s, value had increased remarkably in the 8 days

Bearing of pump B

103-

before failure 9 The measurement of the

., 102 --

AE r.m.s, value was proved to be effective in detecting fatigue failure.

Bearing of pump A

~

10--

O

Figure

4

illustrates

that

chemical

corrosion could be detected at the bearing of a pump due to observation of AE amplitude distribution. In other words, it is clear from Fig.4 that AE with larger amplitude were emitted from

the

corroded

bearing.

The

~ <

10.1 15

! 25

! 30

I .... 35

I 40

I 45

I 50

Amplitude of AE, dB

Figure 4 AE amplitude distribution and chemical corrosion[4]

,,J 55

218 example of grease deterioration is

~5'0 I ~4.o ;>

shown in Figure 5. The relative AE

Supply of grease

r.m.s, value decreased rapidly after

E3.o

supplying new grease to the bearing, when it increased

"~ 2.0

due to grease

deterioration progressively day by day.

~1.0

Useful information can be derived !

0 ! 4/72 5/72

6/72

I

7/12

t

I

I

!

I

9/72 10/72 11/72 12/72

8/72

Month/Year

from measurement results of AE, if we want to evaluate lubricating condition in rolling bearings and measure AE at

Figure 5 Relative AE r.m.s, value and grease deterioration [4]

the rolling bearings. Bloch, H., R, and Finely, R., W., [5,6] extended research of James, et al.

and realized to monitor automatically bearings by introducing the computer. They measured the r.m.s value of AE emitted from bearings in the same way as James, et al.. Ensor, L., C., et al. [7] measured ring down counts of AE during rolling contact fatigue tests of a radial bearing, and Adachi, A., and Ishikawa, H., [8] investigated cumulative AE events in the process of rolling contact fatigue in the thrust test bearing. For example, the result obtained by Adachi, A., and Ishikawa, H., is

Xll I00

= 0

r

< >. o.._, u

shown in Figure 6. It is observed in Fig.6 that

Spall Spalling---~/q

9O 8O

many AE were reproducibly generated just before shutdown due to occurrence of spalling. They

70

assumed that AE generated in the process of

60

rolling contact

5O

40 30 10 O0

were caused

by the

Hawman, M., W., and Galinaitis, W., S., [9]

"I

E 20

fatigue

propagation of fatigue cracks. analyzed signals emitted at bearings with an

A 9

artificial defect in frequency and estimated the ,

_]__

I

!

_

10 20 30 40 50 60 70 80)<105 Number of stress cycles, cycles

Figure 6 Cumulative AE event count and rolling contact fatigue test

[8]

bearing element with the defect on the result of analysis.

Moreover,

they

observed

AE

and

vibration of the bearing with defect and showed that AE

technique

was better than

vibration for

detection of defective bearings. Berrymann, F., et al. [ 10] analyzed signals through the bandpass filter between 440 and 460 kHz and estimated the bearing element with the defect. Inoue, N., [ 11] estimated the failed bearing element by the periodicity of AE on roller bearings and showed that AE technique could detect fatigue failure earlier than vibration by

219 comparing the trend of AE r.m.s value with that of vibration acceleration. Sato, I., et al. [ 12] observed AE signals from the viewpoint of frequency range and shape, and developed AE signals from the viewpoint of synchronism of AE generation with rotation of the bearing, respectively, and tried to diagnose defect, corrosion, contamination of oil and lubrication condition of roller bearings. Nishimoto, S., and Oguma, N., [ 13] assumed in their experiment that AE caused by propagation of rolling contact fatigue cracks had a frequency from 250 kHz to 260 kHz and confirmed that the interval time of AE generation agreed with the time interval by coming into contact with each point on rolling elements. Tan, C., C., [ 14] and Tandon, N., and Nakra, B., C., [ 15] investigated the influence of the size of defect and rotational speed to energy of AE and indicated the superiority of AE technique over vibration. Neill, G., D., et al. [ 16] experimented in order to detect defects of rolling bearings with apparatus simulating a pump and showed the superiority of AE technique in the same way as Tan. Rogers, L., [17] tried to locate the position of AE source in the rolling bearing of offshore gas production platform slewing cranes by means of arrival time interval of AE to two sensors. However, no failures were recorded during his measurement. The purpose of most of the papers mentioned above is to detect rolling contact fatigue failure. In contract with them, Li, X., and Inasaki, I., [ 18] measured some statistics on AE in order to detect seizure and contamination of rolling bearings built into a spindle of machine tools. Examples of bearing seizure are shown in Figure 7. The X-axis indicates the running time to stop caused by seizure and the Y-axis indicates the normalized ratio of the average value to the standard deviation of developed AE amplitude. We can see that the normalized ratio increased a few minutes 3.0T .....:;....~.....; .........i......- ......!......T......"-...... - ~--i....!--F---['-'

before seizure. From

the

above-

mentioned papers, it is clear that there

is

prediction

the

probability

of

rolling

of

contact

fatigue failure by observing AE during

operation

of

' ie

9,~1 i i i ! ! i i i ~ i i ~ i I !/i ,~.OT .... ---"'."--l . . . . . . . . . . . . 2. . . . . ~ ...... ~ ....... ~........ '.-....... ~ ...... i . . . . . J - - t ~ ....... ! ....... ~ - " - ! ~ t ! ! ! i i ! i i ! i i i ~ i i !!,

rolling

bearings since AE are generated before fatigue failure. Also the AE technique may be superior to that of vibration for the detection

...... i ....... ...... ....... ! ..... i

u . u t - + ~ - - . ~ . - . 4 9

9

i

'

~

"

:

9

-18 -16 -14 -12 -10 -8

:-

~

-6

t

~

-4

....~......i.... : ~

]

-2

,~

0

Running time, min Figure 7 Bearing seizure and standard deviation of AE amplitude[ 18]

of bearing failure. However, the relationship between the process of rolling contact fatigue and generation of AE has not been clarified in full, because only trends of AE and vibration with time were measured in the

220 papers. Moreover, it has never been proven whether AE in the process of rolling contact fatigue are caused just by the propagation of fatigue cracks or not.

P R E D I C T I O N O F R O L L I N G C O N T A C T FATIGUE FAILURE It is essential in prediction of rolling bearing failure to confirm whether detected AE from the bearing are caused by bearing failure or not. We cannot confirm the cause of AE if we only measure the trend of AE. In regard to rolling contact fatigue failure, location of AE source position may be a superior method in order to clarify the cause of AE. That is to say, the cause of AE is confirmed by the agreement of the AE source position with the position of rolling contact fatigue failure. Yoshioka, T., and Fujiwara, T., [19,20] developed a location method of AE source position for the test bearing simulating a thrust bearing. And Yoshioka, T., and Mano, H., [21 ] developed another location method for a radial bearing. These methods decide the contact point between tings and rolling elements within the loading zone at the moment of AE generation as the AE source position. Experimental research on AE location of rolling bearings is described below. ( i ) Research on thrust ball bearing [22] Figure 8 illustrates the test Shaft washer-.. ~

AE sensor

bearing

simulating

a

thrust

ball

bearing and arrangement of an AE sensor and a ball position sensor. The Retainer ~ . . ~ ~ ~ . _ ~

test bearing comprises a shaft washer,

Housing w a s h e r ~ ~ ' ~ ) (Sample) ~,, ~ ~ ~ _ ~ ~ ( ~

a housing washer, three balls and a

Raceway t

r

a

Housing/~

c

k

retainer, and rotates under an axial

~ ~Ball Maker

load. The AE sensor detects AE position waves emitted from the test bearing and is transformed into AE signals, sensor

Figure 8 Test bearing and arrangement of AE sensor and ball position sensor [22]

while the ball position sensor detects passage of a marker on the outside

surface of a retainer in order to measure the time elapsed from the passage to AE generation. When the source position of AE was located, the ball position on the raceway track was decided on the basis of the measured time elapsed as the source position of AE. In experiments on rolling contact fatigue, the test bearing was run under an axial load of

221

!t

3.14 kN at a rotational speed of the shaft washer of 660 rpm in a mineral oil bath. The measuring

1000

conditions of AE were as follows : the resonance

AEeventratec, n,

frequency of AE sensor was 320kHz, the frequency bandwidth was from 200 to 400 kHz, the amplification degree was 80 dB and threshold level for both event rate and location was 1.0 V. Vibration acceleration from 0.01 to 20 kHz was detected and the fatigue test

405

was automatically terminated as soon as its r.m.s value

........

tttllil

exceeded 7.0 rn/s 2 due to the occurrence of spalling. An example of trends of AE event rate and

20

vibration acceleration during fatigue tests is shown in

(b)

Figure 9. The X-axis indicates the running time and

39 40 Running time, h ,! E I t

the Y-axis in Fig.9 (a) is the AE event rate in counts/min and that in Fig.9 (b) is the r.m.s value of

P1

acceleration in m/s 2. Spalling occurred in the raceway track surface of housing washer after 40.26h and the acoustic signals were continually emitted in

Spalling

the 20 min or more before the experiment was stopped. However, it is clear from Fig.9 (b) although

the

AE

event

rate

P3

Figure 9 Trends of AE event rate and vibration acceleration during fatigue test[22]

test was stopped. It can be seen in Fig.9 (a) that many

that,

P2

g

]~

1000

was

increasing, the acceleration did not vary and rose rapidly at the end of the experiment. Location results of AE source positions during the time shown in Fig.9 are shown in Figure 10. The X-axis indicates the positions on the raceway track and the Y-axis the number of AE. On the X-axis, the whole raceway track is measured on a scale from 0.0 to 1.0. The source position of AE was located by dividing the raceway track into 120 equal parts. The whole raceway track length was 105.2 mm, and the resolution of location was

~ 100 oo

~>

i

P3

0 w--

soot

< 700~

n

100I 0 I~

il P2

100T

Oa-i 0.0

fl

tl

P1

I

0.5

! I

1.0

Position on raceway track Figure 10 Location results of AE source positions [22]

approximately 0.9 mm. The figure shows the location results from period P 1 to P3, which correspond to those below the X-axis in Fig.9. The locating time of P3 was about 18 min before failure, while the locating time was 30 min in

222 other periods. There were few AE in period P1; on the contrary, for periods P2 and P3, three conspicuous peaks can be seen, which show that many AE were generated at three particular positions on the raceway track. The positions emitted for period P3 are the same as those for P2. After the test, the housing washer was removed and its raceway track was inspected; spalling was found at the position corresponding to the right-hand peak in Fig. 10. Although there was one spalling site, it can be seen in Fig.10 that the three peaks are placed at equal intervals. The reason is that three balls were assembled at the same intervals in the test bearing. This shows that the failure point on the raceway generated AE when each ball passed that point. It becomes clear from Fig.9 (a) and Fig.10 that AE at the failure position began at Ta=39.84 h; i.e., the fatigue crack started to propagate at Ta. Figure 9 (b) shows that the fatigue failure appeared in the surface at Tv=40.26h. Therefore, Tv-Ta, the propagation time in this case, was 25 min. From the standpoint of diagnosis, the propagation time of fatigue crack corresponds to prediction time of failure. From the fact, as described above, it was proven that the developed AE source location method was correct and AE caused by the propagation of crack could be detected during rolling contact fatigue test. Also, it becomes clear that AE method is superior to that of vibration in the detection of rolling contact fatigue failure. Moreover, it was found that we could predict occurrence of rolling contact fatigue failure in rolling bearings if we observed AE.

( ii ) Research on radial ball bearing [21 ] A~

V

Figure 11 illustrates the arrangement of a rker

test bearing and the sensors for the AE source location method. The test bearing simulates a deep groove ball bearing. A loading zone is induced in the blacked out area of the test bearing, when the pure radial load is applied to it. An AE sensor, an inner ring position sensor and a ball position sensor are fixed on the housing of the test bearing as shown in Fig.ll. The inner ring position sensor detects the passage o f an inner ring marker on the inner ring to measure

Figure 11 Test bearing and arrangement of sensors for the AE source location [21 ]

the circumferential position of the inner ring in the loading zone due to the time elapsed from the time when the inner ring marker passes through

223 in front of it to the time when position sensor detects the passage

of

each

ball

____l f [ Vibr~

Vibration sensor )

AE is generated. The ball

i

to

l

l Recorder ]

sonsor)los i,,os ooe l

measure the contact points between the inner raceway or

.....................................

i~i~!i!iii!~ii~ ~ ( Inner ring "~~ AE source ii;i;v~~ / position |~.~ locator iiiiiii~i iii :~i1% sensor j ~!11 IIi~ I [i~easurement device

the outer raceway and balls within the loading zone at the

t~

AE generation.

1 'n~ ~ ~ !!!!!!!iiii]~Ball pos'tio ~ii~iii:!::~:~il | sensor / ~~iiiii!G,,.~,~,,,~,,~,~,~,::~::,:i:,i~..~: ~, ....................... J . ~ ~ - - ~ - - ~ ~ . "~.~Housing I Loaa I Test bearing

The block diagram in Figure 12 shows an AE and vibration measurement system.

.Rea g test!ng

The signals detected by the AE sensor, the inner ring

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

of time interval

~ .......... Personal comouter ~otter

.

Figure 12 Block diagram of AE and vibration measurement system [21 ]

position sensor and the ball position sensor are sent to the

locator. The AE source locator decides the possible AE source positions according to these signals at every AE generation during the fatigue test, and the location results cumulated for each 5 min are stored in the personal computer temporarily. A vibration sensor is fixed on the housing of the test bearing. The test bearing simulating a deep groove ball bearing #6204 is shown in Figure 13. The balls roll on the inner raceway without groove shoulders in order to increase stress in a contact !

surface and accelerate the fatigue test. Figure 14 illustrates the bearing test machine used in

the

.. . rlousmg ~

Wost

t:~ Support bearing ~ ( D e e p groove ball bearing)

bearing ~

~:

Ac../Pulley

rolling

contact fatigue test. A main shaft is

Dead weight lever system

L

supported by two deep groove ball bearings test

and

the

bearing

is

D

Motor

\

attached to the end

l

of the shaft. The load

is

statically

applied to the test bearing by means

Figure 13 Test bearing [21 ]

Figure 14 Bearing test machine [21]

224 of a dead weight lever system as a pure radial load. An example of results of a fatigue test is shown in Figure 15. These are the trends of the vibration and the AE event rate in the case of the test bearing being stopped at 74.3 h. The Xaxis indicates the running time in hours and the Y-axis in Fig.15 (i) is the r.m.s, value of the vibration acceleration in m/s 2 and that in Fig. 15 (ii) the AE event rate in counts/min. The r.m.s. value increased in a stage at the point of time P2 corresponding to 73.7 h in Fig.15 (i), when a minute spalling appeared in the surface of the inner raceway. On the other hand, it could be seen in Fig. 15 (ii) that many AE were generated from the point of time P 1 of 72.9 h before the spalling. ,,-, 2000r~

[Test stop

o o

7.5-

V

M in;;; spalling

;>

o

.,.-~

5.0-

~

<

~

0

1000

~

E

~ , / t d ~ a , . . , , , t L 1. t, ..=Ld t,..,,-,- l Irn" " l , , ~ r "

~ , " , ' U - " ,,,n',--T ",1

2.5( i ) Vibration acceleration 3000-

r..)

0

]!,, , ,

1 40 80 Position on inner raceway, address No. ( i ) 5 min data including P1

,-, =::z 2000raO

o

..= E 2000-

o

=:::;

0

~D

1000 1000-

;>

l,i

<

.,,~

E (..)

O-

4' P1

7'3

!

P2 7'4'

Running time,

1

0 "'ll"l] . .II'.~i,,l=l=ll:l,, . . . . .I'.llt::=,,,=l . . . . . .''''l . i...."'",,i ~,,~. III I I'l:lll :IIII~....... .......i 1 40 80 Position on inner raceway, address No. (ii) 5 rain data just before P2

h

( ii ) AE event rate

Figure 16 Location results of AE source position [21]

Figure 15 Trends of the vibration and the AE event rate [21 ] Figure 16 illustrates the location results of the AE source position on the inner raceway. All AE generated for 5 min including the time P 1 and just before the time P2 were cumulated in the location results. The X-axis indicates the position on the inner raceway in an address scale from 1 to 80 and the Y-axis indicates the cumulative AE events in counts for 5 min. In

225 this connection, the resolution of the radial bearing location was approximately 1.04 mm. We can see in Fig.16 that several peaks appeared at the interval of 10 addresses. The highest peak was at address No.31 on the inner raceway and the position of the highest peak was decided as an actual AE source position. The spalling position on the inner raceway was measured after fatigue test, and it was confirmed that the spalling position agreed with the located position. In addition, the same position was always located in the period from the time P1 to the time P2. Therefore, the AE detected in this period was presumed to be caused by the propagation of the rolling contact fatigue crack below the surface of the spalling position. Moreover, the time from P1 to P2 was 0.8 h, and it was considered that this time was equal to the propagation time of the fatigue crack. Figure 17 shows a photograph of spalling which appeared at address No.31 on the inner raceway in this test. Figure 18 illustrates the relationship between

:iii!~i~i~!~iii~!~gg~

the located address of the AE source position and the measured address of the spalling position in 14 shows the correlation coefficient of 1. The plots

.....

within

the

limits

of

.+. :i!il >...

9

. ....::.!ii::!iiii::i

Accordingly, it was proven that the developed AE

I

" ~9

From the above facts, it is clear that the developed AE source location method for the radial rolling bearing was

correct.

Moreover,

it

was

confirmed

that

Figure 17 [21]

~

O :~

~,. 60 ~

rolling bearings agreed with those of the thrust rolling

~

M

It was proven in the previous item that the developed AE source location methods are very useful in order to

Photograph of spalling

:

~ :~!~:~i~::~i~i~i~:/:~:: i~i~::~i~::~::~ii::~i!::iii::!i!iii~::i::!::!::i::i::~::~::i!~:?:;:?:i!i!!ii::i::iii::i::i::i::i::i.:1

i~:i~i!~i~i~i~i~i!i!i!~i~i~i~i~i~i~i~i~i~i~!~i~i~i~!~!~!~i~i !,:ili!i,,,,ii!,,,iii,,ii,,iii,,i~,,~iii,,!,,!,!ii,,i,i,i

!iii!!i!i i{!!iil

40 iiiili!iiii!!!iiii!!i!i!iil

bearings.

generation [23]

80

r~

conclusions obtained in the experiments on the radial

Diagnosis by measurement of time interval of AE

I

0.2 mm

Revolutionary direction of balls

source location method could determine the AE reproducibility.

:~.i:~!!i~

ii i .::::::~:~i::iiiiiiiiiiiiii~' ,iii::iii~;~:. '::.:.:o. .............,,.~iiii~i~iiii":!ii:i!::::~. . ::::::%i:::i::iiiiiiiiiiii~. . .i::i ................~!~i!i!:i:~i:i:i:~:i:i.: i~i~::ii: . . !i~

+ 1 address.

source position correctly, accurately and with good

:

.

:.:::

obtained by the experiments are distributed near the line

~:~;~::::i ~

~ii! ~ i:~i!i~i~

fatigue tests. The solid line of 45 degrees in Fig.18

solid

~-''~; ....................................

.~:~ ...:..: . : :::::ii!i!~i:~i!~i::5. . . . . . . . . ....~:::~:~:~

iiiiiiiiii iiiiii ii

~~-.~::iiiiiiiiiiiiiiiiiii ii! iiii!i',iiiiii i',i

00 20 40 60 80 Spalling position, address No.

Figure 18 Relationship between located address of AE source position and measured address of spalling position [21 ]

understand correctly the activity of a rolling contact fatigue crack. However, the methods may

226 not be applied to the maintenance field, since they are complicated and need 2 or 3 signals detected from the bearing. The measurement method of time interval of AE generation described here detects only AE to diagnose bearings and then may be easily applied in the Threshold level ~

(a)

Co)

(,:)

(d)

field.

...................... ; ~ < I : - - - ~

Figure

AE signals

principle

19 illustrates the

of the measurement

method. When the amplitude of

t= 64 ms

AE signal (a) first exceeds the 1

2

threshold level, the clock in the

3

clock circuit begins to oscillate

Figure 19 Measurement principle of time interval of AE generation [23]

and creates pulses. This continues during a period of t. If the

amplitudes

of

other

AE

signals (b) and (c) exceed the threshold level within the period of t, the time intervals between (a) and (b) or (c) are measured as the integration of the clock pulses. Therefore, in this case the time intervals between (a) and (b) and between (a) and (c) are n~ and n2, respectively. The AE signal (d) which is generated after the period of t is treated as a new trigger of the next oscillation of the clock pulse. An example of measurement results is shown in Figure 20. This figure show trends in '4

o~

o ~

E

500

-

Spalling appearence,~ <

r-T-]

2.0 :~ :~ ,d

i ': i : ? ii

:

i iI

o,..~

0

o

0

(a) Vibration acceleration

~Z

~

.

I - :

:

O

,[,i

....

L~

~ ....

i ....

0

i ....

i ....

i ....

20

i ....

r,

,,,, J Jt Jl ,,i

....

i ....

i ....

40

I

....

60

Time interval of AE generation, ms (a) 5 min data (M.)just after 22.8 h

:

""

-

:.3

:i

o o

500

-

o

1000

i:ii:

< ~

<

19

21 Running time, h

23 u u M1

(b) AE event rate Figure 20 Trends of vibration acceleration and AE event rate [231

0

Q)

,,,,'~,,

0

,

....

t,,f,,l,t

20

....

, .........

, ....

40

r~,,,ff~

....

~,~,, ~ ,,~,'~,

60

Time interval of AE generation, ms

(b) 5 min data (M)just after 23.3 h Figure 21 Measurement results of time interval of AE generation [23]

227 the vibration acceleration and the AE event rate of the test bearing stopped at 23.4 h. The r.m.s. value of the vibration acceleration also increased rapidly and exceeded the stop level at 23.4 h by a spalling appearance in Fig.20 (a). On the other hand, many AE were generated, as shown in Fig.20 (b), just after the start of this test. Figure 21 shows the measurement results of time intervals of AE generation at the test bearing and all AE events generated for 5 min after 22.8 h (M1) and after 23.3 h (M2) were cumulated in it. In Fig.21, AE were generated at the time intervals of 6, 11.5, 17.25 and 23 ms. They were very close to Tb=5.8 ms and its multiples of 11.6, 17.4 and 23.2 ms, respectively. Now, the time interval Tb is from the time a point on a ball comes into contact with one raceway to the time the point comes into contact with another raceway and is calculated on the basis of bearing kinetics. Therefore, we could presume that an AE source existed on a ball. If a rotational axis of a ball was not changed within the loading zone, it came into contact with the inner and the outer raceway at the above-mentioned time intervals of Tb. After passing through the unloading zone, the axis changed at any angle. Accordingly, the phase of AE generation shifted at each revolution of the ball. The four groups of the peaks appearing after 40 ms in Fig.21 were formed by the shift of phases of AE generations. We observed the same pattern in the measurement of time intervals of AE generation as Fig.21 during the test. In the case of the inner raceway failure, the measured time intervals agreed with the calculated time intervals. Therefore, it was confirmed by rolling contact fatigue tests that the developed method was useful to identify the element in which a fatigue crack was propagating. Moreover, it was possible using the method to predict the appearance of spalling.

R E S E A R C H ON O T H E R M A C H I N E E L E M T N T S USING A C O U S T I C EMISSION TECHNIQUES There have not necessarily been any papers on diagnosis of other machine elements using acoustic emission techniques. Examples of a sliding bearing and gears are described below. Figure 22 shows an apparatus used for tests of metal contact in a sliding bearing [24]. The apparatus not only applies a radial load to the test bearing, but also tilts the bearing. Figure 23 illustrates trends of AE energy rate and temperatures of the test bearing and lubrication oil when metal wipe, that is, metal contact occurred in the test bearing under the pure radial load. It is clear in Fig.23 that AE technique is more sensitive to metal wipe than temperature. Kondo, K., and Takada, J., [25] used a developed oil film transmitter to detect AE signals emitted at the gear mesh position in the gearbox. Figure 24 is a photograph of detected

228

. no tio% t_... ~._~p~! ! 1 / ~ Measurement Load device f o r ~ ~ r___' _ ~ . [ ~]system misalignment ! g ' - " i ~ g ~ 5~ [ "~[-----~Thermometer I I~

Figure 22

Apparatus for test of sliding bearing [24] ,

,~~400 ~

~

70

,_~,,,

,

,

. . . .

Temperatare of sliding bearing Temperature of lubrk ation oil

0

,

......

L

<

:r_

5

10 15 Time, rain

20

25

Figure 23 Trends of AE energy rate and temperatures of test bearing and lubrication oil [24] AE and vibration signals. The .....

? ........

i

! .......

'~

i ....

~

~ .....

-i

i ......

!

:

!

.......

i ........

i

~ "

i

9

photograph Enveloped AE signal

9 I

"

!

-r

!

~

shows

that

AE

technique is more sensitive than vibration. Moreover, they found

Instantaneous AE r.m.s, signal

in their experiment that the

Vibration signal

related to gear meshing, and

,;

detected AE signal was directly suggested that diagnosis by AE

.

.

.

.

.

.

i

Figure 24 Photograph of AE and vibration signals at gears [25]

technique could be effectively used for estimation of tooth load and lubrication condition. In both researches, it becomes

229 clear that AE techniques are useful for diagnosis of machine elements.

SUMMARY

Researches on diagnosis of machinery using acoustic emission (AE) techniques were surveyed. Many papers on diagnosis of rolling bearings have been presented, however there have been only a few reports on other machine elements. In regard to researches on rolling bearings, most of them concentrate on rolling contact fatigue and defects as failures in rolling bearings, and there are very few reports about other kinds of failures, for instance, seizure, wear and so on. Some papers were related to detection of artificial defects in the raceway surface of rolling bearings. To establish predictive diagnosis is very important to clarify the processes of rolling contact fatigue and occurrence of defect. The author's view of the future development of machinery diagnosis is described below. Diagnosis using AE techniques will expand to predict failures in many kinds of machine elements and concerning rolling bearings, failures other than rolling contact fatigue. Under the present situation, researches on machinery diagnosis tend to concentrate on rolling bearings and rolling contact fatigue failure. It is considered that diagnosis of machinery will progress to multi-sensing technology. Since various kinds of failures happen in machinery, it is difficult to predict the occurrence of failures using only single sensing. Therefore, new methods will be looked for and applied to diagnosis. At present, the author and co-researchers have developed a new multi-sensing method that detects simultaneously AE and vibration using one sensor manufactured by fusion technique. We expect the new multi-sensing method to find and predict failures that have never been detected. REFERENCES

1.

Japan Lubricating Oil Society, (1995) Investigation report on maintenance of machinery

2.

Nishikawa, T., (1995) Meintenansu, 187, pp.30.

3.

Sakai, K., Kurahashi, M., (1988) Junkatsu, 33,3,pp. 182.

4.

James, R., Reber, B., Baird, B., Neale, W., (1973) The Oil & Gas Journal, 17, Dec.,pp.49.

5.

Bloch, H., P., Finely, R., W., (1978) Proc. of 7th Turbomachinery Symposium, Dec..

6.

Finely, R., W., (1980) Material Evaluation, 38, Aug., pp. 15.

7.

Ensor, L., C., Feng, C., C., Whittier, R., M., Diercks, A., D., (1975) NASA Contract NAS

8.

Adachi, A., Ishikawa, H., (1979) Kinzoku, 49, 7, pp.56.

8-29916.

230 9.

Hawman, M. W., Galinaitis, W. S., (1988) Ultrasonic Symposium, pp.885.

10. Berrymann, E, Michie, P., Smulders, A., Vermeiren, K., (1989) 1989 Condition monitoring and Preventive Maintenance Proceedings, STLE SP-27, pp. 144. 11. Inoue, N., (1989) puranto enjinia, 21, 3, pp.8. 12. Sato, I., Yoneyama, T., Yanagibashi, M., Taguchi, Y., Tanaka, T., (1989) Hihakaikensa, 38, 5, pp.432. 13. Nishimoto, S., Oguma, N., (1990) Koyo Engineering Journal, 137, pp.34. 14. Tan, C., C., (1990) The Institute of Engineers Australia Tribology Conference, pp. 110. 15. Tandon, N.,

Nakra, B., C., (1990) Journal of Acoustic Emission, 9, 1, pp.25.

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231 AUTHOR

INDEX

Carlos, M.F. Carvalho, F.C.S. Cole, P.T.

t59 145 169

Eisenbl~itter, J. Enoki, M.

127 1

Fleischmann, P.

179

Hamstad, M.A.

77

Kishi, T. Kwon, O.-Y.

1 93

Labuz, J.F. Lee, K. Lenain, J.C.

145 93 179

Manthei, G.

127

Niitsuma, H. Nishino, H.

109 35

Ohtsu, M. Ono, K.

19,187 35,57

Spies, T.

127

Takemoto, M.

35

Vahaviolos, S.J. Van De Loo, P.J.

159 169

Wang, W.D.

159

Yoshioka, T. Yuyama, S.

215 187

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233 SUBJECT

INDEX

Acoustic emission

Acoustic Emission (AE) AE source discrimination AE source location

1,77, 109,127, 145,187, 215 159 159 215

Mapping Microcrack Microcracking Microseismicity Modal analysis Moment tensor Moment tensor analysis

109 1 127 109 35 1 19,187

Composite materials Concrete Crack initiation Crack kinematics Cross-correlation Cyclic loading test

77 187 93 19 35 187

Neural network Non Destructive Testing (NDT) Nondestructive evaluation

159 159 77

Damage mechanics Deconvolution Deformation Denoising Diagnosis Discrete wavelet Dispersion curves Displacement discontinuity model

19 1 57 35 215 35 159 145

Pattern recognition Phase transformation Plane strain testing Post-failure response

159 57 145 145

Rock mechanics Rolling bearing Rolling contact fatigue

127 215 215

Elastodynamics Evaluation criteria

19 187

Fatigue Fatigue damage Fitness for Service Fracture

187 93 159 57

Sensor calibration Short fatigue cracks SIGMA code Signal analysis Signal classification Simulation analysis Source characterization

Geologic structures Guided wave

109 35

Source mechanism Structural integrity

145 93 19 57 35 57 1, 77, 145 57 77, 127, 159 127 187

Imaging Induced seismicity Inverse wavelet

109 109 35

Time-frequency analysis Transfer function

159 57

Kaiser effect

187

Laser interferometer Location

1 1

Wave propagation Waveform based acoustic emission Wavelet de-noising Wavelet transform Windowed Fourier transform

77 159 93 35 35

Source function Source location

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