Complex System Maintenance Handbook (Springer Series in Reliability Engineering)

Springer Series in Reliability Engineering

Series Editor Professor Hoang Pham Department of Industrial Engineering Rutgers The State University of New Jersey 96 Frelinghuysen Road Piscataway, NJ 08854-8018 USA

Other titles in this series The Universal Generating Function in Reliability Analysis and Optimization Gregory Levitin Warranty Management and Product Manufacture D.N.P Murthy and Wallace R. Blischke Maintenance Theory of Reliability Toshio Nakagawa System Software Reliability Hoang Pham Reliability and Optimal Maintenance Hongzhou Wang and Hoang Pham Applied Reliability and Quality B.S. Dhillon Shock and Damage Models in Reliability Theory Toshio Nakagawa Risk Management Terje Aven and Jan Erik Vinnem Satisfying Safety Goals by Probabilistic Risk Assessment Hiromitsu Kumamoto Offshore Risk Assessment (2nd Edition) Jan Erik Vinnem The Maintenance Management Framework Adolfo Crespo Márquez Human Reliability and Error in Transportation Systems B.S. Dhillon

Khairy A.H. Kobbacy • D.N. Prabhakar Murthy Editors

Complex System Maintenance Handbook

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Khairy A.H. Kobbacy, PhD Management and Management Sciences Research Institute University of Salford Salford, Greater Manchester M5 4WT UK

D.N. Prabhakar Murthy, PhD Division of Mechanical Engineering The University of Queensland Brisbane 4072 Australia

ISBN 978-1-84800-010-0

e-ISBN 978-1-84800-011-7

DOI 10.1007/978-1-84800-011-7 Springer Series in Reliability Engineering series ISSN 1614-7839 British Library Cataloguing in Publication Data A Complex system maintenance handbook. - (Springer series in reliability engineering) 1. Maintenance 2. Reliability (Eningeering) 3. Maintenance - Management I. Murthy, D. N. P. II. Kobbacy, Khairy A. H. 620'.0046 ISBN-13: 9781848000100 Library of Congress Control Number: 2008923781 © 2008 Springer-Verlag London Limited Watchdog Agent™ is a trademark of the Intelligent Maintenance Systems (IMS) Center, University of Cincinnati, PO Box 210072, Cincinnati, OH 45221, USA. www.imscenter.net Apart from any fair dealing for the purposes of research or private study, or criticism or review, as permitted under the Copyright, Designs and Patents Act 1988, this publication may only be reproduced, stored or transmitted, in any form or by any means, with the prior permission in writing of the publishers, or in the case of reprographic reproduction in accordance with the terms of licences issued by the Copy-right Licensing Agency. Enquiries concerning reproduction outside those terms should be sent to the publishers. The use of registered names, trademarks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant laws and regulations and therefore free for general use. The publisher makes no representation, express or implied, with regard to the accuracy of the information contained in this book and cannot accept any legal responsibility or liability for any errors or omissions that may be made. Cover design: deblik, Berlin, Germany Printed on acid-free paper 9 8 7 6 5 4 3 2 1 springer.com

To our wives Iman and Jayashree for their patience, understanding and support

Preface

Modern societies depend on the smooth operation of many complex systems (designed and built by humans) that provide a variety of outputs (products and services). These include transport systems (trains, buses, ferries, ships and aeroplanes), communication systems (television, telephone and computer networks), utilities (water, gas and electricity networks), manufacturing plants (to produce industrial products and consumer durables), processing plants (to extract and process minerals and oil), hospitals (to provide services) and banks (for financial transactions) to name a few. Every system built by humans is unreliable in the sense that it degrades with age and/or usage. A system is said to fail when it is no longer capable of delivering the designed outputs. Some failures can be catastrophic in the sense that they can result in serious economic losses, affect humans and do serious damage to the environment. Typical examples include the crash of an aircraft in flight, failure of a sewerage processing plant and collapse of a bridge. The degradation can be controlled, and the likelihood of catastrophic failures reduced, through maintenance actions, including preventive maintenance, inspection, condition monitoring and design-out maintenance. Corrective maintenance actions are needed to restore a failed system to operational state through repair or replacement of the components that caused the failure. Maintenance has moved from being an engineering activity after a system has been put into operation into an important issue that needs to be addressed during the design and manufacturing or building of the system. Maintenance impacts on reliability (a technical issue) with serious economic and commercial implications. This implies that operators of complex systems need to look at maintenance from an overall business perspective that integrates the technical and commercial issues in an effective manner. The literature on maintenance is vast. Over the last 50 years, there have been dramatic changes due to advances in the understanding of the physics of failure, in technologies to monitor and assess the state of the system, in computers to store

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and process large amounts of relevant data and in the tools and techniques needed to build model to determine the optimal maintenance strategies. The aim of this book is to integrate this vast literature with different chapters focusing on different aspects of maintenance and written by active researchers and/or experienced practitioners with international reputations. Each chapter reviews the literature dealing with a particular aspect of maintenance (for example, methodology, approaches, technology, management, modelling analysis and optimisation), reports on the developments and trends in a particular industry sector or, deals with a case study. It is hoped that the book will lead to narrowing the gap between theory and practice and to trigger new research in maintenance. The book is written for a wide audience. This includes practitioners from industry (maintenance engineers and managers) and researchers investigating various aspects of maintenance. Also, it is suitable for use as a textbook for postgraduate programs in maintenance, industrial engineering and applied mathematics. We would like to thank the authors of the chapters for their collaboration and prompt responses to our enquiries which enabled completion of this handbook on time. We also wish to acknowledge the support of the University of Salford and the award of CAMPUS Fellowship in 2006 to one of us (PM). We gratefully acknowledge the help and encouragement of the editors of Springer, Anthony Doyle and Simon Rees. Also, our thanks to Sorina Moosdorf and the staff involved with the production of the book.

Contents

Part A An Overview Chapter 1: An Overview K. Kobbacy and D. Murthy ...................................................................................... 3 Part B Evolution of Concepts and Approaches Chapter 2: Maintenance: An Evolutionary Perspective L. Pintelon and A. Parodi-Herz.............................................................................. 21 Chapter 3: New Technologies for Maintenance Jay Lee and Haixia Wang....................................................................................... 49 Chapter 4: Reliability Centred Maintenance Marvin Rausand and Jørn Vatn .............................................................................. 79 Part C Methods and Techniques Chapter 5: Condition-based Maintenance Modelling Wenbin Wang........................................................................................................ 111 Chapter 6: Maintenance Based on Limited Data David F. Percy ..................................................................................................... 133 Chapter 7: Reliability Prediction and Accelerated Testing E. A. Elsayed ........................................................................................................ 155

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Chapter 8: Preventive Maintenance Models for Complex Systems David F. Percy ..................................................................................................... 179 Chapter 9: Artificial Intelligence in Maintenance Khairy A. H. Kobbacy ......................................................................................... 209 Part D Problem Specific Models Chapter 10: Maintenance of Repairable Systems Bo Henry Lindqvist............................................................................................... 235 Chapter 11: Optimal Maintenance of Multi-component Systems: A Review Robin P. Nicolai and Rommert Dekker ................................................................ 263 Chapter 12: Replacement of Capital Equipment P.A. Scarf and J.C. Hartman................................................................................ 287 Chapter 13: Maintenance and Production: A Review of Planning Models Gabriella Budai, Rommert Dekker and Robin P. Nicolai ................................... 321 Chapter 14: Delay Time Modelling Wenbin Wang........................................................................................................ 345 Part E Management Chapter 15: Maintenance Outsourcing D.N.P. Murthy and N. Jack ................................................................................. 373 Chapter 16: Maintenance of Leased Equipment D.N.P. Murthy and J. Pongpech .......................................................................... 395 Chapter 17: Computerised Maintenance Management Systems Ashraf Labib ......................................................................................................... 417 Chapter 18: Risk Analysis in Maintenance Terje Aven ............................................................................................................ 437 Chapter 19: Maintenance Performance Measurement (MPM) System Uday Kumar and Aditya Parida .......................................................................... 459 Chapter 20: Forecasting for Inventory Management of Service Parts John E. Boylan and Aris A. Syntetos .................................................................... 479

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Part F Applications (Case Studies) Chapter 21: Maintenance in the Rail Industry Jørn Vatn ............................................................................................................. 509 Chapter 22: Condition Monitoring of Diesel Engines Renyan Jiang, Xinping Yan ................................................................................. 533 Chapter 23: Benchmarking of the Maintenance Process at Banverket (The Swedish National Rail Administration) Ulla Espling and Uday Kumar ............................................................................. 559 Chapter 24: Integrated e-Operations–e-Maintenance: Applications in North Sea Offshore Assets Jayantha P. Liyanage ........................................................................................... 585 Chapter 25: Fault Detection and Identification for Longwall Machinery Using SCADA Data Daniel R. Bongers and Hal Gurgenci .................................................................. 611 Contributor Biographies ....................................................................................... 643 Index ..................................................................................................................... 653

Part A

An Overview

1 An Overview K.A.H. Kobbacy and D.N.P. Murthy K. Kobbacy and D. Murthy

1.1 Introduction The efficient functioning of modern society depends on the smooth operation of many complex systems comprised of several pieces of equipment that provide a variety of products and services. These include transport systems (trains, buses, ferries, ships and aeroplanes), communication systems (television, telephone and computer networks), utilities (water, gas and electricity networks), manufacturing plants (to produce industrial products and consumer durables), processing plants (to extract and process minerals and oil), hospitals (to provide services) and banks (for financial transactions) to name a few. All equipment is unreliable in the sense that it degrades with age and/or usage and fails when it is no longer capable of delivering the products and services. When a complex system fails, the consequences can be dramatic. It can result in serious economic losses, affect humans and do serious damage to the environment as, for example, the crash of an aircraft in flight, the failure of a sewage processing plant or the collapse of a bridge. Through proper corrective maintenance, one can restore a failed system to an operational state by actions such as repair or replacement of the components that failed and in turn caused the failure of the system. The occurrence of failures can be controlled through maintenance actions, including preventive maintenance, inspection, condition monitoring and design-out maintenance. With good design and effective preventive maintenance actions, the likelihood of failures and their consequences can be reduced but failures can never be totally eliminated. The approach to maintenance has changed significantly over the last one hundred years. Over a hundred years ago, the focus was primarily on corrective maintenance delegated to the maintenance section of the business to restore failed systems to an operational state. Maintenance was carried out by trained technicians and was viewed as an operational issue and did not play a role in the design and operation of the system. The importance of preventive maintenance was fully appreciated during the Second World War. Preventive maintenance involves additional costs and is worthwhile only if the benefits exceed the costs. Deciding

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the optimum level of maintenance requires building appropriate models and use of sophisticated optimisation techniques. Also, around this time, maintenance issues started getting addressed at the design stage and this led to the concept of maintainability. Reliability and maintainability (R&M) became major issues in the design and operation of systems. Degradation and failure depend on the stresses on the various components of the system. These depend on the operating conditions that are dictated by commercial considerations. As a result, maintenance moved from a purely technical issue to a strategic management issue with options such as outsourcing of maintenance, leasing equipment as opposed to buying, etc. Also, advances in technologies (new materials, new sensors for monitoring, data collection and analysis) added new dimensions (science, technology) to maintenance. These advances will continue at an everincreasing pace in the twenty-first century. This handbook tries to address the various issues associated with the maintenance of complex systems. The aim is to give a snapshot of the current status and highlight future trends. Each chapter deals with a particular aspect of maintenance (for example, methodology, approaches, technology, management, modelling analysis and optimisation) and reports on developments and trends in a particular industry sector or deals with a case study. In this chapter we give an overview of the handbook. The outline of the chapter is as follows. Section 1.2 deals with the framework that is needed to study the maintenance of complex systems and we discuss some of the salient issues. Section 1.3 presents the structure of the book and gives a brief outline of the different chapters in the handbook. We conclude with a discussion of the target audience for the handbook.

1.2 Framework for Study of Maintenance A proper study of maintenance requires a comprehensive framework that incorporates all the key elements. However, not all the elements would be relevant for a particular maintenance problem under consideration. The systems approach is an effective approach to solving maintenance problems. In this approach, the real world relevant to the problem is described through a characterisation where one identifies the relevant variables and the interaction between the variables. This characterisation can be done using language or a schematic network representation where the nodes represent the variables and the connected arcs denote the relationships. This is good for qualitative analysis. For quantitative analysis, one needs to build mathematical models to describe the relationships. Often this requires stochastic and dynamical formulations as system degradation and failures occur in an uncertain manner. In this section, we discuss the various key elements and some related issues. We use the term “asset” to denote a complex system or individual equipment. It can include infrastructures such as buildings, bridges etc. in addition to those listed in Section 1.1.

An Overview

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1.2.1 Stakeholders For an asset there can be several stakeholders as indicated in Figure 1.1.

Figure 1.1. Stakeholders for maintenance of an asset

The number of parties involved would depend on the asset under consideration. For example, in case of a rail network (used to provide a service to transport people and goods) the customers can include the rail operators (operating the rolling stock) and the public. The owner can be a business entity, a financial institution or a government agency. The operator is the agency that operates the track and is responsible for the flow of traffic. The service provider refers to the agency carrying out the maintenance (preventive and corrective). It can be the operator (in which case maintenance is done in-house) or some external agent (if maintenance is outsourced) or both (when only some of the maintenance activities are outsourced). The regulator is the independent agency which deals with safety and risk issues. They define the minimum standards for safety and can impose fines on the owner, operator and possibly the service provider should the safety levels be compromised. Government plays a critical role in providing the subsidy and assuming certain risks. In this case all the parties involved are affected by the maintenance carried out on the asset. If the line is shut either frequently and/or for long duration, it can affect customer satisfaction and patronage, the returns to the operators and owners and the costs to the government. 1.2.2 Different Perspectives We focus our attention on the case where the asset is owned by the owner and maintenance is outsourced. In this case, we have two parties – (i) owner (of the asset) and (ii) service agent (providing the maintenance). Figure 1.2 is a very simplified system characterisation of the maintenance process where the main-

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tenance activities are defined through a maintenance service contract. The problem is to determine the terms of the service contract.

Figure 1.2. System characterisation for maintenance out-sourcing

Each of the elements of Figure 1.2 involves several variables. For example, the maintenance service contract involves the following: (i) duration of contract, (ii) price of contract, (iii) maintenance performance requirements, (iv) incentives and penalties, (v) dispute resolution, etc. The maintenance performance requirements can include measures such as availability, mean time between failures and so on. The characterisation of the owner’s decision-making process can involve costs, asset state at the end of the contract, risks (service agent not providing the level and quality of service) and so on. The interests and goals of the owner are different from that of the service agent. The study of maintenance is complicated by the unknown and uncontrollable factors. It could be rate of degradation (which depends on several factors such as material properties, operating environment etc) and other commercial factors (high demand for power in the case of a power plant due to very hot weather). 1.2.3 Key Issues and the Need for Multi-disciplinary Approach The key issues in the maintenance of an asset are shown in Figure 1.3. The asset acquisition is influenced by business considerations and its inherent reliability is determined by the decisions made during design. The field reliability and degradation is affected by operations (usage intensity, operating environment, operating load etc.). Through use of technologies, one can assess the state of the asset. The analysis of the data and models allow for optimizing the maintenance decisions (either for a given operating condition or jointly optimizing the maintenance and operations). Once the maintenance actions have been formulated it needs to be implemented.

An Overview

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Figure 1.3. Key Issues in maintenance of an asset

To execute effective maintenance one needs to have a good understanding of a variety of concepts and techniques for each of the issues. Another issue is the computer packages that allow one to collect and analyze data and build models and derive the optimal solutions. The linking of the technical and commercial issues is indicated in Figure 1.4 and this requires an inter-disciplinary approach.

Figure 1.4. Linking technical and commercial issues

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The disciplines involved are as follows 1.2.3.1 Engineering The degradation of an asset depends to some extent on the design and building (or production) of the asset. Poor design leads to poor reliability that in turn results in high level of corrective maintenance. On the other hand, a well-designed system is more reliable and hence less prone to failures. Maintainability deals with maintenance issues at the design and development stage of the asset. 1.2.3.2 Science This is very important in the understanding of the physical mechanisms that are at play and have a significant influence on the degradation and failure. Choosing the wrong material can have a serious consequence and impact on the subsequent maintenance actions needed. 1.2.3.3 Economic Maintenance costs can be a significant fraction of the total operating budget for a business depending on the industry sector. There are two types of costs – annual cost and cost over the life cycle of the asset. The costs can be divided into direct (labour, material etc.) and indirect (consequence of failure). 1.2.3.4 Legal This is important in the context of maintenance out-sourcing and maintenance of leased equipment. In both cases, the central issue is the contract between the parties involved. Of particular importance is dispute resolution when there is a disagreement between the parties in terms of the violation of some terms of the contract. 1.2.3.5 Statistics The degradation and failures occur in an uncertain manner. As such, the analysis of such data requires the use of statistical techniques. Statistics provide the concepts and tools to extract information from data and for the planning of efficient collection systems. 1.2.3.6 Operational Research Operation research provides the tools and techniques for model building, analysis and optimization. Often, analytical approaches fail and one needs to use simulation approach to evaluate the outcomes of different decisions and to choose the optimal (or near optimal) strategies. 1.2.3.7 Reliability Theory Reliability theory deals with the interdisciplinary use of probability, statistics and stochastic modelling, combined with engineering insights into the design and the scientific understanding of the failure mechanisms, to study the various aspects of reliability. As such, it encompasses issues such as (i) reliability modelling, (ii) reliability analysis and optimization, (iii) reliability engineering, (iv) reliability science, (v) reliability technology and (vi) reliability management.

An Overview

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1.2.3.8 Information Technology and Computer Science The operation and maintenance of complex assets generates a lot of data. One needs efficient ways to store and manipulate the data and to extract relevant information from data. Computer science provides a range of artificial intelligence techniques such as data mining, expert systems, neural networks etc., which are very important in the context of maintenance. 1.2.4 Maintenance Management Maintenance management deals with the overall management of the maintenance of an asset. The management needs to be done at three different levels (strategic, tactical and operational) as indicated in Figure 1.5. - BUSINESS PERSPECTIVE - TECHNICAL & COMMERCIAL - IN-HOUSE vs. OUT-SOURCING - REPLACEMENT / DESIGN CHANGES

MAINTENANCE STRATEGY

STRATEGIC LEVEL

- DEGRADATION (RELIABILITY SCIENCE) - MAINTENANCE POLICIES - LOGISTICS (SPARES, FACILITIES ETC)

MAINTENANCE PLANNING AND SCHEDULING

TACTICAL LEVEL

- DATA COLLECTION - DATA ANALYSIS (ROOT CAUSE, OTHER FACTORS)

MAINTENANCE WORK EXECUTION

OPERATIONAL LEVEL

Figure 1.5. Maintenance management

The strategic level deals with maintenance strategy. This needs to be formulated so that it is consistent and coherent with other (production, marketing, finance, etc.) business strategies. The tactical level deals with the planning and scheduling of maintenance. The operational level deals with the execution of the maintenance tasks and collection of relevant data.

1.3 Structure of the Handbook The handbook integrates the vast literature on maintenance with each chapters focussing on a different aspect of maintenance and written by active researchers with international reputation and/or experienced practitioners from industry. Each chapter either reviews the literature dealing with a particular aspect of maintenance (for example, methodology, approaches, technology, management, modelling ana-

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lysis and optimisation), reports on developments and trends in a particular industry sector, or deals with a case study. The book is structured into five parts and each of the last four parts contains several chapters. The topic of the different chapters is as indicated below. Part A:

An Overview

Chapter 1:

An Overview (Khairy Kobbacy and Pra Murthy)

Part B:

Evolution of Concepts and Approaches

Chapter 2: Chapter 3: Chapter 4:

Maintenance: An Evolutionary Perspective (Liliane Pintelon and Alejandro Parodi Herz) New Technologies for Maintenance (Jay Lee and Haixia Wang) Reliability Centred Maintenance (Marvin Rausand and Jorn Vatn)

Part C:

Methods and Techniques

Chapter 5: Chapter 6: Chapter 7: Chapter 8: Chapter 9:

Condition-based Maintenance Modelling (Wenbin Wang) Maintenance Based on Limited Data (David F. Percy) Reliability Prediction and Accelerated Testing (Elsayed A. Elsayed) Preventive Maintenance Models for Complex Systems (David F. Percy) Artificial Intelligence in Maintenance (Khairy A.H. Kobbacy)

Part D:

Problem Specific Models

Chapter10: Chapter 11:

Chapter 14:

Maintenance of Repairable Systems (Bo Henry Lindqvist) Optimal Maintenance of Multi-component Systems: A Review (Robin P. Nicolai and Rommert Dekker) Replacement of Capital Equipment (Philip A. Scarf and Joseph C. Hartman) Maintenance and Production: A Review of Planning Models (Gabriella Budai, Rommert Dekker and Robin P. Nicolai) Delay Time Modelling (Wenbin Wang)

Part E:

Management

Chapter 15: Chapter 16:

Maintenance Outsourcing (Pra Murthy and Nat Jack) Maintenance of Leased Equipment (Pra Murthy and Jarumon Pongpech) Computerised Maintenance Management Systems (Ashraf Labib) Risk Analysis in Maintenance (Terje Aven) Maintenance Performance Measurement (MPM) System (Uday Kumar and Aditya Parida) Forecasting for Inventory Management of Service Parts (John E. Boylan and Aris A. Syntetos)

Chapter 12: Chapter 13:

Chapter 17: Chapter 18: Chapter 19: Chapter 20:

An Overview

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Part F:

Applications (Case Studies)

Chapter 21: Chapter 22:

Maintenance in the Rail Industry (Jorn Vatn) Condition Monitoring of Diesel Engines (Renyan Jiang and Xinping Yan) Benchmarking of the Maintenance Process at Banverket (The Swedish National Rail Administration) (Ulla Espling and Uday Kumar) Integrated e-Operations–e-Maintenance: Applications in North Sea Offshore Assets (Jayanta P. Liyanage) Fault Detection and Identification for Longwall Machinery Using SCADA Data (Daniel Bongers and Hal Gurgenci)

Chapter 23:

Chapter 24: Chapter 25:

A brief outline of each chapter is as follows Chapter 2: Maintenance: An Evolutionary Perspective In the past few decades industrial maintenance has evolved from a non-issue into a strategic concern. During this period the role of maintenance has drastically been transformed. This chapter, while considering the fundamental elements of maintenance and its environment, describes the evolution path of maintenance management and the driving forces of such changes. It basically explains how and why maintenance practice has evolved in time. It includes basic notions of maintenance and clearly classifies and distinguishes between different types of maintenance actions, policies and concepts currently available. The chapter concludes by enlightening the reader with some new challenges in maintenance Chapter 3: New Technologies for Maintenance Predictive maintenance is critical to any engineering system, especially complex systems, in order to avoid system breakdown. With the recent advances in pervasive computing, prognostics can be easily embedded in any devices and systems. When smart machines are networked and remotely monitored, and when their data is modelled and continually analyzed with sophisticated embedded systems, it is possible to go beyond mere “predictive maintenance” to intelligent “prognostics”, the process of pinpointing exactly which components of a machine are likely to fail and then autonomously trigger service and order spare parts. This chapter addresses the paradigm shift in modern maintenance systems from the traditional “fail and fix” practices to a “predict and prevent” methodology. Recent advances in prognostic technologies and tools are presented, and future work directions are discussed. Chapter 4: Reliability Centred Maintenance This chapter gives an introduction to reliability centred maintenance (RCM). The RCM analysis process is divided into 12 distinct steps. Each step is thoroughly described and discussed. The main RCM process is similar to the processes outlined in RCM standards and guidelines, but has more focus on the optimization of maintenance intervals. A new approach is proposed based on generic RCM analyses related to specified classes of consequences. The new approach will significantly reduce the workload of the RCM analysis. A computer tool OptiRCM

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that has been developed by the authors, is used to illustrate the new approach. Several examples from railway applications are provided. Chapter 5: Condition-based Maintenance Modelling This chapter presents a model for supporting condition based maintenance decision making. The chapter discusses various issues related to the subject, such as the definition of the state of an asset, direct or indirect monitoring, relationship between observed measurements and the state of the asset, and current modelling developments. In particular, the chapter focuses on a modelling technique used recently in predicting the residual life via stochastic filtering. This is a key element in modelling the decision making aspect of condition based maintenance. A few key condition monitoring techniques are also introduced and discussed. Methods of estimating model parameters are outlined and a numerical example based on real data is presented. Chapter 6: Maintenance-based on Limited Data Reliability applications often suffer from paucity of data for making informed maintenance decisions. This is particularly noticeable for high reliability systems and when new production lines or new warranty schemes are planned. Such issues are of great importance when selecting and fitting mathematical models to improve the accuracy and utility of these decisions. This chapter investigates why reliability data are so limited and proposes statistical methods for dealing with these difficulties. It considers graphical and numerical summaries, appropriate methods for model development and validation, and the powerful approach of subjective Bayesian analysis for including expert knowledge about the application area. Chapter 7: Reliability Prediction and Accelerated Testing This chapter presents an overview of accelerated life testing (ALT) methods and their use in reliability prediction at normal operating conditions. It describes the most commonly used models and introduces new ones which are “distribution free”. Design of optimum test plans in order to improve the accuracy of reliability prediction is also presented and discussed. The chapter provides, for the first time, the link between accelerated life testing and maintenance actions. It develops procedures for using the ALT results for estimating the optimum preventive maintenance schedule and the optimum degradation threshold level for degrading systems. The procedures are demonstrated using two numerical examples. Chapter 8: Preventive Maintenance Models for Complex Systems Preventive maintenance (PM) of repairable systems can be very beneficial in reducing repair and replacement costs, and in improving system availability. Strategies for scheduling PM are often based on intuition and experience, though considerable improvements in performance can be achieved by fitting mathematical models to observed data. For simple repairable systems comprising few components or many identical components, compound renewal processes are appropriate. This chapter reviews basic and advanced models for complex repairable systems and demonstrates their use for determining optimal PM intervals. Computational

An Overview

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difficulties are addressed and practical illustrations are presented, based on subsystems of oil platforms and Chapter 9: Artificial Intelligence in Maintenance AI techniques have been used successfully in the past two decades to model and optimise maintenance problems. This chapter reviews the application of Artificial Intelligence (AI) in maintenance management and introduces the concept of developing intelligent maintenance optimisation system. The chapter starts with an introduction to maintence management, planning and scheduling and a brief definition of AI and some of its techniques that have applications in maintenance management. A review of literatures is then presented covering the applications of AI in maintenance. We have focused on five AI techniques namely Knowledge Based Systems, Case Based Reasoning, Genetic Algorithms, Neural Networks and Fuzzy Logic. This review also covers “hybrid” systems where two or more AI techniques are used in an application. A discussion of the development of the prototype hybrid intelligent maintenance optimisation system (HIMOS) which was developed to evaluate and enhance PM maintenance routines of complex engineering systems then follows. The chapter ends with a discussion of future research and concluding remarks. Chapter 10: Maintenance of Repairable Systems A repairable system is traditionally defined as a system which, after failing to perform one or more of its functions satisfactorily, can be restored to fully satisfactory performance by any method other than replacement of the entire system. An extended definition used in this chapter includes the possibility of additional maintenance actions which aim at servicing the system for better performance, referred to as preventive maintenance (PM). The common models for the failure process of a repairable system are renewal processes (RP) and non-homogeneous Poisson processes (NHPP). The chapter considers several generalizations and extensions of the basic models, for example the trend renewal process (TRP) which includes NHPP and RP as special cases, and having the property of allowing a trend in processes of non-Poisson type. When several systems of the same kind are considered, there may be an unobserved heterogeneity between the systems which, if overlooked, may lead to wrong decisions. This phenomenon is considered in the framework of the TRP process. We then consider the extension of the basic models obtained by introducing the possibility of PM using a competing risks approach. Finally, models for periodically inspected systems are studied, using a combination of time-continuous and time-discrete Markov chains. Chapter 11: Optimal Maintenance of Multi-component Systems: A Review This chapter gives an overview of the literature on multi-component maintenance optimization focusing on work appearing since the 1991 survey by Cho and Parlar. A classification scheme primarily based on the dependence between components (stochastic, structural or economic) is introduced. Next, the papers are also classified on the basis of the planning aspect (short-term vs. long-term), the grouping of maintenance activities (either grouping preventive or corrective maintenance, or opportunistic grouping) and the optimization approach used (heuristic, policy

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classes or exact algorithms). Finally, attention is paid to the applications of the models. Chapter 12: Replacement of Capital Equipment This chapter deals with models of replacement of capital equipment. Capital replacement models may be classified as economic life models or dynamic programming models. The former are concerned with determining the optimal lifetime of an item of equipment taking account of costs over some planning horizon. The latter considers replacement decisions dynamically, determining whether plant should be retained or replaced after each period. We begin by looking at simple economic life models. These are applied in a case study on escalator replacement. Economic life models are then extended to consider first an inhomogeneous fleet and then second a network system viewed as an inhomogeneous fleet with interacting items. A number of different dynamic programming models are introduced for singular systems and then expanded to homogeneous and inhomogeneous fleets and networks of assets. Chapter 13: Maintenance and Production: A Review of Planning Models This chapter gives an overview of the relation between planning of maintenance and production. Production planning and scheduling models where failures and maintenance aspects are taken into account are considered first. The planning of maintenance activities are considered next, where both preventive as well as corrective maintenance are discussed. Third, the planning of maintenance activities at such moments in time where the items to be maintained are not or less needed for production, also called opportunity maintenance is considered. Apart from describing the main ideas, approaches, and results a number of applications are provided. Chapter 14: Delay Time Modelling This chapter presented a modelling tool that was created to model the problems of inspection maintenance and planned maintenance interventions, namely Delay Time Modelling (DTM). This concept provides a modelling framework readily applicable to a wide class of actual industrial maintenance problems of assets in general and inspection problems in particular. The delay time defines the failure process of an asset as a two-stage process. The first stage is the normal operating stage from new to the point that a hidden defect has been identified. The second stage is defined as the failure delay time from the point of defect identification to failure. It is the existence of such a failure delay time which provides the opportunity for preventive maintenance to be carried out to remove or rectify the identified defects before failures. With appropriate modelling of the durations of these two stages, optimal inspection intervals can be identified to optimise a criterion function of interest. This chapter first gives an outline of the delay time concept then introduces two delay time inspection models of a single component and a complex system respectively. The parameters estimation techniques used in DTM are discussed. Extensions to the basic delay time model are highlighted and future research in DTM concludes the chapter.

An Overview

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Chapter 15: Maintenance Outsourcing It is often uneconomical for businesses to carry out their own maintenance on complex equipment. The alternative is to ‘out-source’ the maintenance function and use an external agent, under a service contract, to carry out some or all of the maintenance actions (preventive and corrective). This chapter develops the framework needed to study decision-making for maintenance outsourcing from both the customer (equipment owner) and service agent perspectives. The relevant literature is reviewed and a game theoretic approach to maintenance outsourcing and the use of agency theory is discussed. The link between maintenance outsourcing and extended warranties is highlighted and the scope for future research in both areas is examined. Chapter 16: Maintenance of Leased Equipment For leased equipment, the lessor has to carry out the maintenance of the equipment over the lease period. To ensure satisfactory performance and maintenance, the lease contract has penalty terms which result in the lessor having to compensate the lessee if the number of failures exceeds some specified number and/or the time to rectify each failure exceeds some specified value. This implies that the lessor needs to take into account these penalties in determining the optimal maintenance strategy. The chapter starts with a conceptual framework to discuss the different issues involved and then looks at models to help the lessor in developing the optimal maintenance strategy. Chapter 17: Computerised Maintenance Management Systems Computerised maintenance management systems (CMMSs) are vital for the coordination of all activities related to the availability, productivity and maintainability of complex systems. Modern computational facilities have offered a dramatic scope for improved effectiveness and efficiency in, for example, maintenance. CMMSs have existed, in one form or another, for several decades. In this chapter, the characteristics of CMMSs have been investigated and have highlighted the need for them in industry and identified their current deficiencies. A proposed model is then presented to provide a decision analysis capability that is often missing in existing CMMSs. The effect of such model is to contribute towards the optimisation of the functionality and scope of CMMSs for enhanced decision analysis support. The use of AI techniques in CMMSs is illustrated. The features of next generation maintenance systems are finally highlighted. Chapter 18: Risk Analysis in Maintenance Risk analysis can be used for selection and prioritisation of maintenance activities, and this application of risk analysis has been given increased attention in recent years. This chapter presents and discusses the use of risk analysis for this purpose. The chapter reviews some critical aspects of risk analysis important for the successful implementation of such analyses in maintenance. This relates to risk descriptions and categorisations, uncertainty assessments, risk acceptance and risk informed decision making, as well as selection of appropriate methods and tools. Both qualitative and quantitative approaches are covered. A detailed risk analysis is outlined showing the effect of maintenance on risk.

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Chapter 19: Maintenance Performance Measurement (MPM) System It is important that factors influencing the performance of maintenance process should be identified, and measured, so that they can be monitored and controlled for improvement. In this chapter, besides an overview of performance measurement, maintenance performance indicators, associated issues and challenges for developing a maintenance performance measurement framework, and indicators as in use by different industries are discussed. The framework considers stakeholders, business environment, multi-criteria and hierarchical needs amongst other. Chapter 20: Forecasting for Inventory Management of Service Parts This chapter addresses issues pertinent to forecasting for the inventory management of service parts. In some sectors, such as the aerospace and automotive industries, a very wide range of service parts are held in stock, with significant implications for availability and inventory holding. Their management is therefore an important task. First, a number of possible approaches to classifying service parts for forecasting and inventory management related purposes are reviewed. Second, parametric and non-parametric approaches to forecasting service parts requirements are discussed followed by the presentation of appropriate metrics for measuring the performance of the inventory management system. The existing empirical evidence on various forecasting methods is then summarised. Finally, the conclusions of this work are presented along with the identification of some natural avenues for further research. Chapter 21: Maintenance in the Rail Industry The chapter presents two case studies in railway maintenance. The first case study presents an optimisation model preventive maintenance of a train bogie. In the model a dynamic approach to grouping of maintenance activities is used enabling, e.g., opportunity maintenance. Data from the Norwegian State Railways have been used in the calculation example. The second case study present a life cycle cost approach to prioritization of larger maintenance and renewal projects under budget constraints. Chapter 22: Condition Monitoring of Diesel Engines Various techniques have been widely used to monitor the condition of diesel engines. Analysis of engine lubricant is a most widely used condition monitoring technique. In this chapter, a case study applying oil analysis technique to monitor the condition of marine diesel engines is presented. The case study focuses on analysis and modelling of oil monitoring data. The study first introduces the concept of state discriminant capability of condition variables and uses it to identify the significant condition variables, and then develops a state discriminant model to determine the state of the monitored system based on the current observation. The model parameters are obtained by directly minimizing the misjudgment probability. We believe that the proposed model has a great potential to be used due to its plausible mathematical basis and simplicity though it needs further testing with new data.

An Overview

17

Chapter 23: Benchmarking of the Maintenance Process at Banverket (The Swedish National Rail Administration) For sustaining a competitive edge in the business, railway companies all over the world are looking for ways and means to improve their maintenance performance. Benchmarking is a very effective tool that can assist the management in their pursuit of continuous improvement of their operation. Three different benchmarks have been studied based on a project benchmarking of the maintenance process across borders, another project dealing with benchmarking of maintenance outsourcing by different track regions in Sweden, and a third project studying the level on transparency among the European railway administrations. The chapter discuss the pro and cons, the areas for improvement and the need for improvement of benchmarking metrics and framework. Chapter 24: Integrated e-Operations–e-Maintenance: Application in North Sea Offshore Assets Ongoing developments in Norway brings a good example of how an industry-wide re-engineering process has triggered major changes in operations and maintenance practice of complex and high-risk assets leading towards what is termed integrated e-operations e-maintenance. It aims towards a step-change to the conventional operations and maintenance practices of offshore assets. Initiatives have already been taken to exploit new methods, smart techniques, and digital technologies to enable remote monitoring of offshore equipment condition and asset performance in landbased onshore support facilities using large ICT networks. This has already proved to have direct positive implications on the technical and safety integrity of assets, and subsequently on the plant economics. This chapter shares current experience and knowledge with reference to ongoing developments in the Norwegian oil and gas industry. It highlights current offshore asset maintenance practice, changing technical and economic environment that lead towards an e-approach, development and implementation of integrated e-operations and e-maintenance solutions in the North sea, key features of the e-approach in North sea assets, and future challenges to be fullyintegrated and fail-safe. Chapter 25: Fault Detection and Identification for Longwall Machinery Using SCADA Data In an attempt to improve equipment availability and facilitate informed, preventative maintenance, engineers may choose to implement one or more fault detection and identification (FDI) technologies. For complex systems (systems for which component interactions are not understood and model uncertainties are significant), data-driven methods of FDI are often the only practicable solution. The development of a data-driven FDI system for longwall mining equipment using SCADA data is described here. Significant data preprocessing was required to generate a quality example set. Missing value estimation (MVE) techniques were required to complete the highdimensional stream of condition monitoring data from existing sensors. A cost function, in combination with a linear discriminant analysis, was used to ‘align’ the inaccurate, categorical delay records with those delays inferred by the SCADA data. A neural network was developed to determine the state of the system as a

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function of the real-time SCADA data input. Validation of this algorithm with unseen condition monitoring data showed misclassification rates of machine faults as low as 14.3%.

1.4 Target Audience The unique features of the book are as follows: 1. A coverage of the different approaches to maintenance. 2. Deals with many different aspects (scientific, technical, commercial, management, quantitative modelling) etc. 3. Blends theory with practice. As such it should appeal to both researchers and practitioners. For researchers (from different disciplines) it should provide a starting point for new research into different aspects of maintenance. For practitioners it should provide the concepts and tools so that these can be used for improvements in the overall business performance. Also we hope that it will serve as a reference book for use in postgraduate programs in maintenance

Part B

Evolution of Concepts and Approaches

2 Maintenance: An Evolutionary Perspective Liliane Pintelon and Alejandro Parodi-Herz L. Pintelon and A. Parodi-Herz

2.1 Introduction Over the last decennia industrial maintenance has evolved from a non-issue into a strategic concern. Perhaps there are few other management disciplines that underwent so many changes over the last half-century. During this period, the role of maintenance within the organization has drastically been transformed. At first maintenance was nothing more than a mere inevitable part of production, now it is an essential strategic element to accomplish business objectives. Without a doubt, the maintenance function is better perceived and valued in organizations. One could considered that maintenance management is no longer viewed as an underdog function; now it is considered as an internal or external partner for success. In view of the unwieldy competition many organizations seek to survive by producing more, with fewer resources, in shorter periods of time.To enable these serious needs, physical assets take a central role. However, installations have become highly automated and technologically very complex and, consequently, maintenance management had to become more complex having to cope with higher technical and business expectations. Now the maintenance manager is confronted with very complicated and diverse technical installations operating in an extremely demanding business context. This chapter, while considering the fundamental elements of maintenance and its environment, describes the evolution path of maintenance management and the driving forces of such changes. In Section 2.2 the maintenance context is described and its dynamic elements are briefly discussed. Section 2.3 explains how maintenance practice have evolved in time and different epochs are distinguished. Further, this sections devotes special attention to describe a common lexicon for maintenance actions and policies to further focuss on the evolution of maintenance concepts. Section 2.4 underlines how the role of the maintenance manager has been reshaped as a consequence of the changes of the maintenance function. Finally, the chapter concludes with Section 2.5 identifying the new challenges for maintenance.

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2.2 Maintenance in Context To discuss the context in which maintenance management is embedded, one may raise the question what is maintenance as such? Most authors in maintenance management literature, one way or another, agree on defining maintenance as the “set of activities required to keep physical assets in the desired operating condition or to restore them to this condition”. While this defines what maintenance is about, it may suggest that maintenance is simple, which it is not, as will be confirmed by any maintenance practitioner. Hence “maintenance management” is needed to ingrain maintenance practice in a complex and dynamic context. From a pragmatic view, the key objective of maintenance management is “total asset life cycle optimization”. In other words, maximizing the availability and reliability of the assets and equipment to produce the desired quantity of products, with the required quality specifications, in a timely manner. Obviously, this objective must be attained in a cost-effective way and in accordance with environmental and safety regulations. Figure 2.1 clearly shows that maintenance is embedded in a given business context to which it has to contribute. What is more, it shows that the maintenance function needs to cope with multiple forces and requirements within and outside the walls of the organization. Beyond any doubt, the tasks of maintenance are complex, enclosing a blend of management, technology, operations and logistics support elements. People Legislation

Management

Society

Technology

Total asset life cycle optimization

Technological evolution

Operations

e-business

Logistics Support Outsourcing Market

Information Technology Competition

Figure 2.1. Maintenance in context

To cope with and to coordinate the complex and changing characteristics that constitute maintenance in the first place, a management layer is imperative. Management is about “what to decide” and “how to decide”. In the maintenance arena, a manager juggles with technology, operations and logistics elements that mainly need to harmonize with production. Technology refers to the physical assets which maintenance has to support with adequate equipment and tools. Operations indicate the combination of service maintenance interventions with

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core production activities. Finally, the logistics element supports the maintenance activities in planning, coordinating and ultimately delivering, resources like spare parts, personnel, tools and so forth. In one way or another, all these elements are always present, but their intensity and interrelationships will vary from one situation to another. For example, the elevator maintenance in a hospital vs. the plant maintenance in chemical process industries stipulates a different maintenance recipe tailored to the specific needs. Clearly, the choice of the structural elements of maintenance is not independent from the environment. Besides, other factors like the business context, society, legislation, technological evolution, outsourcing market, will be important. Furthermore, relative new trends, such as the e-business context, will influence the current and future maintenance management enormously. A whole new era for maintenance is expected as communication barriers are bridged and coordination opportunities of maintenance service become more intense. 2.2.1 Changes in the Playing Field of Maintenance One should expect that neither maintenance management nor its environment are stationary. The constant changes in the field of maintenance are acknowledged to have enabled new and innovative developments in the field of maintenance science. The technological evolution in production equipment, an ongoing evolution that started in the twentieth century, has been tremendous. At the start of the twentieth century, installations were barely or not mechanized, had simple design, worked in stand-alone configurations and often had a considerable overcapacity. Not surprisingly, nowadays installations are highly automated and technologically very complex. Often these installations are integrated with production lines that are right-sized in capacity. Installations not only became more complex, they also became more critical in terms of reliability and availability. Redundancy is only considered for very critical components. For example, a pump in a chemical process installation can be considered very critical in terms of safety hazards. Furthermore, equipment built-in characteristics such as modular design and standardization are considered in order to reduce downtime during corrective or preventive maintenance. However, predominantly only for some newer, very expensive installations, such as flexible manufacturing systems (FMS), these principles are commonly applied. Fortunately, a move towards higher levels of standardization and modularization begins to be witnessed at all level of the installations. As life cycle optimization concepts are commendable, it becomes mandatory that at the early design stages supportability and maintainability requirements are well thought-out. Parallel to the technological evolution, the ever-increasing customer focus causes even higher pressure, especially on critical installations. As customers’ service in terms of time, quality and choice becomes central to production decisions, the more flexibility is required to cope with these varying needs. This calls for well-maintained and reliable installations capable to fulfil shorter and more reliable lead-times estimation. Physical assets are ever more important for business success.

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Maintenance does not escape from the (r)evolution in information communication technology (ICT), which has tremendously changed business practices. However, we comment further on this topic in Section 2.3, by illustrating the impact on the role of the maintenance manager as such. Furthermore, new production and management principles such as Just-in-time (JIT) philosophy, Lean principles, total quality management (TQM) and so forth, have emerged. These production trends intend, by all means, to reduce waste and remove non-value added transactions. It is not surprising that work-in-process (WIP) inventories are one of the key issues for improvement. Clearly, WIP inventories incur high costs as a consequence of the capital immobilization, expensive floor space, etc. As processes happen to be streamlined, WIP inventories are no longer a buffer for problems; accordingly, asset availability and reliability are ever more imperative. Albeit, these principles were initially inspired for production and manufacturing environments are currently also applied and translated in service context. Above all, the business environment has also changed. Competition has become fierce and worldwide due to the globalization. The latter not only implies that competitors are located all over the world, but also that decisions to move production or service activities from a non-efficient site (e.g. due to high operations and maintenance costs) to another site are quickly taken, even if the other location belongs to another continent. Obviously, with the advent of globalization and intense competitive pressures, organizations are looking for every possible source of competitive advantage. This implies that the nature of business environment has become more complex and dynamic requiring different competitive strategies. Many companies are critically evaluating their value chain and often decide to drastically reorganize it. This results in focusing on the core business. Consequently outsourcing of some non-core business activities and the creation of new partnerships and alliances are being considered by many organizations. Not surprisingly, maintenance as a support function is no exception for outsourcing. Yet, it may not be so simple. Outsourcing maintenance of technical systems can become a sensitive issue if it is not handled with diligence. Technical systems are unique and situation specific. For example, outsourcing maintenance of utilities or elevators can be relatively straightforward, but when it comes to production floor equipment it can be a strategic issue that has to be handled with extreme care. These circumstances suggest that outsourcing needs to be considered at operational, tactical and strategic level; see Figure 2.2 The simplest, and also the most common, form of outsourcing is “operational outsourcing”. At this level, a specific task is outsourced and the relationship between supplier and customer is strictly limited to a sell-buy situation. The impact on the internal organization of the customer is also limited. As outsourcing moves up in the organizational pyramid the relationship between supplier and customer changes and “tactical outsourcing” maybe required. At this level of outsourcing the customer shares management responsibility with the supplier and a simple kind of partnership is established. The impact on the internal organization is also greater. Finally, moving towards the organization’s top and for more critical maintenance services, a new form of outsourcing is created, the so-called “strategic outsourcing”. This type of outsourcing is also labelled as “transformational out-

Maintenance: An Evolutionary Perspective

25

sourcing” because of its impact on the customer’s internal organization. Here a complete outsourcing is carried out, the maintenance department is cut away from the customer and moved to the supplier. The relationship between customer and supplier is a strong partnership: the customer has fully entrusted the supplier with one of its strategic maintenance activities. This level of outsourcing is yet less common than the former ones. The rationales of whether or not to outsource maintenance activities are complex and require a well-thought and structured outsourcing process. As mentioned maintenance outsourcing can cover a lot of alternatives. Fortunately, besides, traditional outsourcing of maintenance activities to equipment suppliers or the use of some small local firms, there is nowadays a growing market of medium sized and large outsourcing firms. These firms offer a range of consulting support, specialized services and even full service to allow strategic outsourcing to work.

Strategic “Transformational”

Full service To think with… e.g. outsourcing of all maintenance, BOT, ...

Tactic “Partnership”

Service package e.g. MRO, utilities, facilities, ...

To manage…

Projects e.g. renovation, shutdown, ... Operational “Supplier – Customer”

Specialised services

To organise…

e.g. high tech equipment, piping, insulation, ...

Generic services

e.g. temporary extra capacity (painting, welding, ...)

To carry out…

Figure 2.2. Outsourcing decision levels

Societal expectations concerning technology is also creating boundary conditions for maintenance management. The attention paid to sustainability (3P: people, profit, planet) is a clear sign of this. Legislation is getting more and more stringent. This is especially important here because of its impact on occupational safety and environmental standards. Note that most of the above-mentioned trends for industrial installations can be easily translated to the service sector. Think, for example, of automated warehouses in distribution centre, hospital equipment or building utilities.

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2.3 Maintenance Practices Over Time Consequent to the transformation the maintenance context, the maintenance function has also drastically evolved from a non-issue into a strategic concern (see Figure 2.3). At first maintenance was nothing more than an inevitable part of production; it simply was a necessary evil. Repairs and replacements were tackled when needed and no optimization questions were raised. Later on, it was conceived that maintenance was a technical matter. This not only included optimizing technical maintenance solutions, but it also involved attention of the organization on the maintenance work. Further on, maintenance became a full-blown function, instead of production sub-function. Clearly, now maintenance management has become a complex function, encompassing technical and management skills, while still requiring flexibility to cope with the dynamic business environment. Top management recognizes that having a well thought out maintenance strategy together with a careful implementation of that strategy could actually have a significant financial impact. Nowadays, this has led to treating maintenance as a mature partner in business strategy development and possibly at the same level as production. In turn, these strategies formally consider establishing external partnerships and outsourcing of the maintenance function. “Necessary evil” 1940

1950

“Technical matter” 1960

1970

“Profit contributor” 1980

1990

“Cooperative partnership” 2000

Decade

Figure 2.3. The maintenance function in a time perspective

The fact that maintenance has become more critical implies that a thorough insight into the impact of maintenance interventions, or the omission of these, is indispensable. Per se, good maintenance stands for the right allocation of resources (personnel, spares and tools) to guarantee, by deciding on the suitable combination of maintenance actions, a higher reliability and availability of the installations. Furthermore, good maintenance foresees and avoids the consequences of the failures, which are far more important than the failures as such. Bad or no maintenance can appear to render some savings in the short run, but sooner or later it will be more costly due to additional unexpected failures, longer repair times, accelerated wear, etc. Moreover, bad or no maintenance may well have a significant impact on customer service as delivery promises may become difficult to fulfil. Hence, a well-conceived maintenance program is mandatory to attain business, environmental and safety requirements. Despite the particular circumstances, if one intends to compile or judge any maintenance programme, some elementary maintenance terms need to be unambiguous and handled with consistency. Yet, both in practice and in the literature a lot of confusion exists. For example, what for some is a maintenance policy others refer to as a maintenance action; what some consider preventive maintenance others will refer to as predetermined or scheduled maintenance. Furthermore, some argue that some concepts can almost be considered strategies or philosophies, and

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so on. Certainly there is a lot of confusion, which perhaps is one of the breathing characteristics of such a dynamic and young management science. The terminology used to describe precisely some maintenance terms can almost be taken as philosophical arguments. However, the adoption of a rather simplistic, but truly germane classification is essential. Not intending to disregard preceding terminologies, neither to impose nor dictate a norm, we draw attention, in particular, to three of those confusing terms: maintenance action, maintenance policy and maintenance concept. In the remainder of this chapter the following terminology is adopted. Maintenance Action. Basic maintenance intervention, elementary task carried out by a technician (What to do?) Maintenance Policy. Rule or set of rules describing the triggering mechanism for the different maintenance actions (How is it triggered?) Mainenance Concept. Set of maintenance polices and actions of various types and the general decision structure in which these are planned and supported. (The logic and maintenance recipe used?) 2.3.1 Maintenance Actions Basically, as depicted in Figure 2.4, maintenance actions or interventions can be of two types. They are either corrective maintenance (CM) or precautionary maintenance (PM) actions. 2.3.1.1 Corrective Maintenance Actions (CM) CM actions are repair or restore actions following a breakdown or loss of function. These actions are “reactive” in nature; this merely implies “wait until it breaks, then fit it!”. Corrective actions are difficult to predict as equipment failure behavior is stochastic and breakdowns are unforeseen. Maintenance actions such as replacement of a failed light bulb, repair of a ruptured pipeline and the repair of a stalled motor are some examples of corrective actions. 2.3.1.2 Precautionary Maintenance Actions (PM) PM actions can either be “preventive, predictive, proactive or passive” in nature. These types of actions are moderately more complex than the former. To describe fully each one of them, a book can be written on its own. Nonetheless, the fundamental ideas aim at diminishing the failure probability of the physical asset and/or to anticipate, or avoid if possible, the consequences if a failure occurs. Some PM actions (preventive and predictive) are somewhat easier to plan, because they can rely on fixed time schedules or on prediction of stochastic behaviours. However, other types of PM actions become ongoing tasks, originating from the attitude concerning maintenance. Somehow they became part of the tacit knowledge of the organization. Some precise examples of precautionary actions which can be mentioned are lubrication, bi-monthly bearing replacements, inspection rounds, vibration monitoring, oil analysis, design adjustments, etc. All these tasks are considered to be precautionary maintenance actions; however, the underlying principles may be different.

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ACTIONS

POLICIES

CONCEPTS

TPM RCM

Optimizing existing concept

CIBOCOF

Q&D

BCM

Ad hoc

reactive

Customized concept

LCC

preventive

predictive

T/UBM

CBM

FBM DOM

OBM

proactive

passive

Corrective

Precautionary

reactive

Predictive, preventive, proactive and passive

Figure 2.4. Actions, policies and concepts in maintenance1

Although it seems a very clear-cut way of defining elementary maintenance interventions, it still may be difficult in practice to assign some interventions to either class. An example here is routine maintenance on medical equipment such as a breathing device. Cleaning and sterilizing this equipment can be called precautionary maintenance since the equipment is not defective at the moment of the intervention. On the other hand, it is very difficult to predict when an intervention will be needed, and this is a typical characteristic of a corrective intervention. Furthermore, even within precautionary maintenance, it is not always simple to classify certain actions into simple types. This is due to the changing perception on maintenance and the fast evolution of its techniques. 2.3.1.3 Acuity of Maintenance Actions As maintenance knowledge is enhanced and more advance enabling technologies are available, the perception on which maintenance action is “right” has changed a lot during the last decennia. In the 1950s almost all maintenance actions were corrective. Per se maintenance was considered as an annoying and unavoidable cost, which could not be managed. Later on, in the 1960s many companies switched to precautionary (preventive) maintenance programs as they could recognize that some failures on mechanical component had a direct relation with the time or number of cycles in use. This belief was mainly based on physical wear of components or age-related fatigue characteristics. At that time, it was accepted 1

See abbreviations list at the end of this chapter

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29

that preventive actions could avoid some of the breakdowns and would lead to cost savings in the long run. The main concern was how to determine, based on historical data, the adequate period to perform preventive maintenance. Certainly, not enough was known about failure patterns, which, among other reasons, have led to a whole separate branch of engineering and statistics: reliability engineering. In the late 1970s and early 1980s, equipment became in general more complex. As result, the super-positioning effect of the failure pattern of individual components starts to alter the failure characteristics of simpler equipment. Hence, if there is no dominant age-related failure mode, preventive maintenance actions are of limited use in improving the reliability of complex items. At this point, the effectiveness of applying preventive maintenance actions started to be questioned and was considered more carefully. A common concern about “over-maintaining” grew rapidly. Moreover, as the insidious belief on preventive maintenance benefits was put at risk, new precautionary (predictive) maintenance techniques emerged. This meant a gradual, though not complete, switch to predictive (inspection and condition-based) maintenance actions. Naturally, predictive maintenance was, and still is, limited to those applications where it was both technically feasible and economically interesting. Supportive to this trend was the fact that conditionmonitoring equipment became more accessible and cheaper. Prior to that time, these techniques were only reserved to high-risk applications such as airplanes or nuclear power plants. In the late 1980s and early 1990s a different footprint on maintenance history occurred with the emergence of concurrent engineering or life cycle engineering. Here maintenance requirements were already under consideration at earlier product stages such as design or commission. As a result, instead of having to deal with built in characteristics, maintenance turned out to be active in setting design requirements for installations and became partly involved in equipment selection and development. All this led to a different type of precautionary (proactive) maintenance, the underlying principle of which was to be proactive at earlier product stages in order to avoid later consequences. Furthermore, as the maintenance function was better appreciated within the organization, more attention was paid to additional proactive maintenance actions. For example, as operators are in straight and regular contact with the installations they could intuitively identify and “feel” right or wrong working conditions of the equipment. Conditions such as noise, smell, rattle vibration, etc., that at a given point are not really measured, represent tacit knowledge of the organization to foresee, prevent or avoid failures and its consequences in a proactive manner. Yet these actions are indeed typically not performed by maintenance people themselves, but are certainly part of the structural evolution of maintenance as a formal or informal partner within the organization. The last type of precautionary (passive) maintenance actions are driven by the opportunity of other maintenance actions being planned. These maintenance actions are precautionary since they occur prior to a failure, but are passive as they “wait” to be scheduled depending on others probably more critical actions. Passive actions are in principle low priority for the maintenance staff as, at a given moment in time, they may not really be a menace for functional or safety failures. However, these actions can save significant maintenance resources as they may reduce the

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number of maintenance interventions, especially when the set up cost of maintenance is high. For example, when maintenance actions are planned or need to be carried out on offshore oil platforms or on windmills in remote locations, getting to the equipment equipment can be costly. Therefore, optimizing the best combination of maintenance actions, at that point in time, is mandatory. This may invoke replacing components with significant residual life that in different circumstances would not be replaced. 2.3.2 Maintenance Policies As new maintenance techniques happen to be available and the economic implications of maintenance action are comprehended, a direct impact on the maintenance policies is expected. Several types of maintenance policies can be considered to trigger, in one way or another, either precautionary or corrective maintenance interventions. As described in Table 2.1, those policies are mainly failure-based maintenance (FBM), time/used-based maintenance (TBM/UBM), condition-based maintenance (CBM), opportunity-based maintenance (OBM) design-out maintenance (DOM), and e-maintenance. Table 2.1. Generic maintenance policies Policy

Description

FBM

Maintenance (CM) is carried out only after a breakdown. In case of CFR behaviour and/or low breakdown costs this may be a good policy.

TBM / UBM

PM is carried out after a specified amount of time (e.g. 1 month, 1000 working hours, etc.). CM is applied when necessary. UBM assumes that the failure behaviour is predictable and of the IFR type. PM is assumed to be cheaper than CM.

CBM

PM is carried out each time the value of a given system parameter (condition) exceeds a predetermined value. PM is assumed to be cheaper than CM. CBM is gaining popularity due to the fact that the underlying techniques (e.g. vibration analysis, oil spectrometry,...) become more widely available and at better prices. The traditional plant inspection rounds with a checklist are in fact a primitive type of CBM.

OBM

For some components one often waits to maintain them until the “opportunity” arises when repairing some other more critical components. The decision whether or not OBM is suited for a given component depends on the expectation of its residual life, which in turn depends on utilization.

DOM

The focus of DOM is to improve the design in order to make maintenance easier (or even eliminate it). Ergonomic and technical (reliability) aspects are important here.

CFR = Constant failure rate, IFR=Increasing failure rate

For the more common maintenance policies many models have been developed to support tuning and optimization of the policy setting. It is not our intention to explain the fundamental differences between these models, but rather to provide an overview of types of policies available and why these have been developed. Much

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has to do with the discussion in the previous section regarding the acuity of maintenance actions. Therefore, it is clear that policy setting and the understanding of its efficiency and effectiveness continues to be fine-tuned as any other management science. We advocate the reader, particularily interested in the underlying principles and type of models, to review McCall (1965), Geraerds (1972), Valdez-Flores and Feldman (1989), Cho and Parlar (1991), Pintelon and Gelders (1992), Dekker (1996), Dekker and Scarf (1998) and Wang (2002) for a full overview on the state-of-the-art literature. The whole evolution of maintenance was based not solely on technical but rather on techno-economic considerations. FBM is still applied providing the cost of PM is equal to or higher than the cost of CM. Also, FBM is typically handy in case of random failure behaviour, with constant failure rate, as TBM or UBM are not able to reduce the failure probability. In some cases, if there exists a measurable condition, which can signal the probability of a failure, CBM can be also feasible. Finally, a FBM policy is also applied for installations where frequent PM is impracticable and expensive, such as can be the maintenance of glass ovens. Either TBM or UBM is applied if the CM cost is higher than PM cost, or if it is necessary because of criticality due to the existence of bottleneck installation or safety hazards issues. Also in case of increasing failure behaviour, like for example wear-out phenomena, TBM and UBM policies are appropriate. Typically, CBM was mainly applied in those situations where the investment in condition monitoring equipment was justified because of high risks, like aviation or nuclear power regeneration. Currently, CBM is beginning to be generally accepted to maintain all type installations. Increasingly this is becoming a common practice in process industries. In some cases, however, technical feasibility is still a hurdle to overcome. Another reason that catches the attention of practitioners in CBM is the potential savings in spare parts replacements thanks to the accurate and timely forecasts on demand. In turn, this may enable better spare parts management through coordinated logistics support. Finding and applying a suitable CBM technique is not always easy. For example, the analysis of the output of some measurement equipment, such as advanced vibration monitoring equipment, requires a lot of experience and is often work for experts. But there are also simpler techniques such as infrared measuring and oil analysis suitable in other contexts. At the other extreme, predictive techniques can be rather simple, as is the case of checklists. Although fairly low-level activity, these checklists, together with human senses (visual inspections, detection of “strange” noises in rotating equipment, etc.) can detect a lot of potential problems and initiate PM actions before the situation deteriorates to a breakdown. At present FBM, TBM, UBM and CBM accept and seize the physical assets which they intend to maintain as a given fact. In contrast, there are more proactive maintenance actions and policies which, instead of considering the systems as “a given”, look at the possible changes or safety measures needed to avoid maintenance in the first place. This proactive policy is referred to as DOM. This policy implies that maintenance is proactively involved at earlier stages of the product life cycle to solve potential related problems. Ideally, DOM policies intend to completely avoid maintenance throughout the operating life of installations, though, this may not be realistic. This leads one to consider a diverse set of maintenance requirements at the

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early stages of equipment design. As a consequence, equipment modifications are geared either at increasing reliability by raising the mean-time-between-failures (MTBF) or at increasing the maintainability by decreasing the mean-time-to-repair (MTTR). Per se DOM aims to improve the equipment availability and safety. Some equipment modifications may merely request ergonomic considerations to reduce MTTR, others may need totally new designs. Often DOM projects are combined with efforts to increase occupational safety or increase production capacity, such as set up reduction programs. A rather passive, but considerably important maintenance policy that needs to be mentioned is OBM. Typically OBM is applied for non-critical components with a relatively long lifetime. For these components no separate maintenance programs are scheduled; maintenance happens if an opportunity arises due to a maintenance intervention for another component of that machine. More recently in the mid-1990s, with the emergence of the Internet as an enabling technology and the growth of e-business as the standard on business communication, e-maintenance also appeared in the radar of maintenance policies. E-maintenance rather than a policy can also be considered as a means or enabler to some, if not all, the previous policies. However, it is more than just an acronym; it is a step forward to full-integrated maintenance techniques without the boundaries of place. It is in fact a maintenance policy on its own that can support other policies. In particular, academics and practitioners watch with anticipation the great impact it may have on CBM. Conditions measured on site can be remotely monitored, opening entirely new dimensions and opportunities for maintenance services. Therefore, e-maintenance has captured much attention of maintenance researchers given its great impact on business practice. An example of this evolution is telemaintenance, which allows the diagnosis of installation and to perform limited type of repairs from a remote location using ICT and sophisticated control and knowledge tools. 2.3.3 Maintenance Concepts The idea of an “optimized” maintenance program suggests that an adequate mix of maintenance actions and policies needs to be selected and fine-tuned in order to improve uptime, extend the total life cycle of physical asset and assure safe working conditions, while bearing in mind limiting maintenance budgets and environmental legislation. This does not seem to be straightforward, and may require a holistic view. Therefore, a “maintenance concept” for each installation is necessary to plan, control and improve the various maintenance actions and policies applied. A maintenance concept may in the long term even become a philosophy, tenet or attitude to perform maintenance. In some cases advance maintenance concepts are almost considered strategies on their own. What is certain is that maintenance concepts determine the business philosophy concerning maintenance, and that they are needed to manage the complexity of maintenance per se. In practice, it is clear that more and more companies are spending time and effort determining the right maintenance concept. As a matter of fact, maintenance concepts need to be formulated considering the physical characteristics and the context within which installations operate. Not

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surprisingly, as system complexity is increasing and maintenance requirements are becoming more complex, maintenance concepts will require different levels of complexity. Literature provides us with various concepts that have been developed through a combination of theoretical insights and practical experiences. Choosing and implementing the best concept in a given context is hard. To the question “what concept is best for us?”, no short and straightforward answer exists. The right answer to the question is determined by the context, with its complex interaction of technology, business, organization, and so forth. Designing and implementing a good concept will take time and effort. Many companies establish teams with members from different areas (engineering, production, maintenance, ...) to accomplish this difficult task. On the market, many consultants offer their services to assist in this process. This outside help may be very useful to get started and to obtain a better insight into own situation. However, it is useful to note that many consultants have “their” concept (e.g. RCM) they are used to implementing, which may bias their judgment on what concept is “right”. Nevertheless, some outside guidance can be useful, but in order to have a good concept that fits all the companies needs, this should be built by in-house people, using all the knowledge available. Several times in this chapter, it has been suggested that next to increasing systems complexity, maintenance has also evolved in time. This has led to three generations of maintenance concepts with its respective transition points. In the following paragraphs an overview is offered which is also portrayed in Table 2.2. In the past, equipment was generally much simpler; hence the need for maintenance decision support was moderate. For truly simple systems, even a single maintenance policy may possibly be considered a concept on its own. This is considered the simplest form, the “first generation”, of maintenance concepts. Here, only one maintenance policy or even type of action was applied to certain equipment. For a state-of-the-art review on this type of maintenance concepts see Wang (2002). With the advent of automation, installations became highly mechanized and the equipment turned out to be more complex and the interdependencies of the multi-unit systems could no longer be ignored. To maintain such installations efficiently a specific mixture of maintenance policies and actions was required. The need for decision structures became crucial. These circumstances prompted, at first instance, the concept of simple quick and dirty (Q&D) decision diagrams. Q&D charts could help to select adequate maintenance policies as only ‘yes’ or ‘no’ answers can be given to a series of structured but simple questions. The authors note that even though Q&D charts lack the holistic view required for well-conceived and sophisticated maintenance concepts, they are still widely used in practice on specific situations thanks to their simplicity. Examples are reported in Pintelon et al. (2000) and Waeyenbergh and Pintelon (2002). Eventually, superior maintenance concepts were claimed, as the complexity of maintenance decisions increased. As a result, in the last 40 years a vast range of maintenance concepts has been extensively documented in literature. This group of concepts is considered the “second generation” of maintenance concepts and provides a pool of knowledge for maintenance practitioners and researchers. Typical examples, and perhaps the most important ones, are total productive

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maintenance (TPM), reliability-centred maintenance (RCM) and life cycle costing (LCC) approaches. Table 2.2. Description of the maintenance concepts generations Generation

Concept

Description

Main strengths

Main weaknesses

1st

Ad hoc

Implementing FBM and UBM policies; rarely CBM, DOM, OBM

Simple

Ad hoc decisions

1st → 2nd

Q&D

Easy-to-use decision chart. It helps to decide on the “right” maintenance policy

Consistent, Allows for priorities

Rough questions, and answers

2nd

LCC

Detailed cost breakdown over the equipment’s lifetime helping to plan the maintenance logistics

Sound basic philosophy

Resource and data intensive

TPM

Approach with an overall view on maintenance and production. Especially successful in the manufacturing industry

Considers human/technical aspects, fits in kaizen approach. Extensive tool box

Time consuming implementation

RCM

Structured approach focused on reliability. Initially developed for high tech/high risk environment

Powerful approach, Stepby-step procedure

Resource intensive

RCM-based

Approaches focused on remediating some of the perceived RCM shortcomings

Improved performance through e.g. use of sound statistical analysis

Sometimes an oversimplification

Exploiting the company’s strengths and considering the specific business context

Ensuring consistency and quality in the concept developed

2nd → 3rd

Example: streamlined RCM, BCM, RBCM

3rd

Customized

In-house developed; cherrypicking from existing concepts Examples: CIBOCOF, VDM

All these concepts, as many others, enjoy several advantages and are doomed to specific shortcomings. Correspondingly, new maintenance concepts are developed, old ones are updated and methodologies to design customized maintenance concepts are created. These concepts enjoy a lot of interest in their original form and also give raise to many derived concepts. For example, streamlined RCM from RCM. One may consider that customized maintenance concepts constitute the “third generation” of this evolution. They have fundamentally emerged since it is very difficult to claim a “one fits all” concept in the complex and still constantly changing world of maintenance. They are inspired by the former concepts while trying to aviod in the future previously experienced drawbacks. One way or another, customized maintenance concepts mainly consist of a “cherry picking” of useful techniques and ideas applied in other maintenance concepts. This important, but relatively new concept is expected to grow in importance both in practice and with academicians. Concepts that belong to this generation are, for example, value driven maintenance (VDM) and CIBOCOF, which was developed at the Centre of

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Industrial Management (CIB), K.U. Leuven, Belgium. Additionally, in-house maintenance concepts, mostly developed in organization with fairly high maintenance maturity, also belong to this category of concepts. This, for example, was implemented in a petrochemical company that developed a customised concept, which was basically following the RCM logic. However, by extending RCM analysis steps and introducing risk-based inspections (RBI), a more focused and betterconceived maintenance plan could be developed. Moreover, the company borrowed some elements from TPM and incorporated these in their maintenance concept. For example, multi-skilled training programmes were implemented and special tool kits were designed for a number of maintenance jobs using TPM principles. Before the third generation of maintenance concepts was started, or actually even earlier, they were perceived as necessary. In the literature, a middle step is recognized to bridge the second generation with maintenance concepts such as business-centred maintenance (BCM) and risk based centred maintenance (RBCM) were developed. These concepts are merely RCM-related and still widely applied in many organizations. However, a slow but steady movement towards more customized maintenance concept is expected in the near future, as the maintenance function matures. Next, a straightforward description on the most important concepts is presented and important references are provided for the interested reader. 2.3.3.1 Quick & Dirty Decision Charts (Q&D) A Q&D decision chart is a decision diagram with questions on several aspects including; failure paterns, repair behaivours of the equipment, business context, maintenance capabilities, cost structure etc. Answering the questions for a given installation, the user proceeds through the branches of the diagram. The process stops with the recommendation of the most appropriate policy for the specific installation. The Q&D approach allows for a relatively quick determination of the most advantageous maintenance policy. It ensures a consistent decision making for all installations. Although some Q&D decision charts are available from literature (e.g. Pintelon et al. 2000), most companies adopting this approach prefer to draw up their own charts, which incorporate their experience and knowledge in the decision process. This can be implemented in several ways. For instance by defining specific questions, adding or deleting maintenance policies, establishing preferred sequence in which the different policies should be considered, etc. This approach however has the drawback of being rough (dirty). The questions are usually put in the basic yes/no format, limiting the answering possibilities. Moreover, answering the questions is usually done on a subjective basis; for example the question whether a given action or policy is feasible is answered based on experience rather than on a sound feasibility study. 2.3.3.2 Life Cycle Costing (LCC) Approaches LCC originated in the late 1960s and is now resurrecting. The basic principle of LCC is sometimes summarised by “it is unwise to pay too much, but is foolish to spend too little”. This refers to the two main underlying ideas of LCC. The first concerns the cost iceberg structure presented by Blanchard (1992) by whom LCC

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was revived. Mainly he proposes that when considering maintenance or equipment purchasing alternatives, one should not be limited to what momentarily can be seen: “the top of the iceberg”, such as direct maintenance costs (material, labour, etc.) or the purchase price. The indirectly relevant long run cost such as operational expenses, trainning cost, spares inventory costs, etc. are at least of the same order of magnitude. The second refers to the principle that the further one gets in the design or construction cycle of equipment, the more costly it will be to make modifications (e.g. DOM). Maintenance should be taken into account from the very first moment of designing a machine or system. LCC is a methodology for calculating or estimating the total cost of a system during the entire course of its life. This LCC approach implies a synthesis of costing analysis and engineering design principles that must satisfy life cycle requirements at minimum cost. In turn, design decisions are based on total cost of ownership (TCO) principles. In the literature, several LCC approaches can be distinguished. Among the more important ones are Terotechnology, Integrated Logistic Support/Logistics Support Analysis (ILS/LSA) and Capital asset management. During the 1970s, the Terotechnology concept originated in the UK and was the first formal attempt towards LCC (Parkes 1970). It describes a total view of maintenance management that combines management, technology, logistical support and financial control for industrial systems. Terotechnology is concerned with the specification and design for reliability and maintainability of physical assets. The application of Terotechnology also takes into account the processes of installation, commissioning, operation, maintenance, modification and replacement. Decisions are influenced by feedback of information on design, performance and cost, throughout the life cycle of a project. Although generally accepted as very useful, it was not until fairly recently that terotechnology or similar LCC was adopted by large-scale industry. This was largely due to the developments in ICT that made LCC easier. In the 1980s a different LCC-approach, integrated logistic support/logistics support analysis (ILS/LSA), originated in the military logistics support. Maintenance is regarded as an important issue within the integral logistical support. ILS comprises the spectrum of all activities related to the logistical support during its entire life cycle. These logistical support activities refer to maintenance concept development, the spare parts provisioning, the technical information, the maintenance crew, the training programs, etc. The goal of ILS may be summarized as achieving minimum life cycle costs. Furthermore, LSA is an iterative analytical process to identify and evaluate the logistic support for a new system. LSA constitutes the integration and application of various techniques and methods to ensure that supportability requirements are considered in the system design process. Finally, capital asset management, an LCC-approach with real concern of the financial performance of asset, was developed. Capital asset management provides information to make the financial and operational decisions that optimize equipment performance, from deployment through operations, maintenance and retirement. The key focus is not technical, but financial. Asset management aims at maximizing the return on investment (ROI) in capital assets so that they last longer, perform better and cost less to maintain.

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2.3.3.3 Total Productive Maintenance (TPM) TPM (Takahashi and Takeshi 1990) is much more than just a concept, actually it is even considered a maintenance philosophy, which derives to the greater part of its substance from a variety of non-Japanese management structures and practices, which were adapted by the Japanese to fit their culture. TPM involves total participation, at all levels of the organization. It aims at maximizing equipment effectiveness and establishing a thorough system of preventive maintenance. TPM fits entirely with the TQM philosophy and the JIT approach. The latter makes sure that problems of various nature (material related, breakdown, training related, ...) are tackled and solved one by one, instead of camouflaging them by using large buffer stocks as was the case with MRP approaches. The TPM toolbox consists of various techniques, some of which are universal ones such as 6sigma, Pareto or ABC analysis, Ishikawa or fishbone diagrams, etc. Other concepts and techniques such as SMED, poke yoke, jidoka, OEE, and the 5S are specific of the TPM philosophy. The last two are of extreme importance and worthy to be explained further. The overall equipment efectiveness (OEE) is a powerful tool to measure the effective use of production capacity. The strength of the concept is the integration of production, maintenance and quality issues into what is called the “six big losses” of useful capacity. Figure 2.5 illustrates this concept. On the other hand, the 5S form one of the basic principles of TPM: Seiri (or sorting out), Seiton (or systematic arrangement), Seiso (or Spic and span), Seiketsu (or standardizing) and Shitsuke (or self-discipline).

downtime losses

Loading time

Operating time

quality losses

Valuable operating time

loss of speed

Net operating time

planning delays planned maintenance

failures set-up and adjustment

stoppages reduced speed

6 big losses

planning losses

Total time

process defects reduced yields

Figure 2.5. The “big six losses” of overall equipment efectiveness

2.3.3.4 Reliability Centered Maintenance (RCM) RCM originates from the 1960s in North American aviation industry. Later on it was adopted by military aviation, and afterwards it was only implemented at high risk industrial plant such as nuclear power plants. Now it can be found in industry

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at large. Well known are the books by Nowlan and Heap (1978); Anderson and Neri (1990) and Moubray (1997) who contributed to the adoption of RCM by industry. Note that today many versions of RCM are around, streamlined RCM being one of the more popular ones. However, the Society for Automotive Engineers (SAE) holds the RCM definition that is generally accepted. SAE puts forward the following basic questions to be solved by the any RCM implementation; if any of these is omitted, the method is incorrectly being refered to as an RCM. To answer these seven questions a clear step-by-step procedure exists and decision charts and forms are available: • What are the functions and associated performance standards of asset in its present operating context? • How can it fail to fulfil its functions? (functional failures) • What causes each failure? (failure modes) • What happens when each failure occurs? (failure effects ) • In what way does each failure matter? (failure consequences) • What should be done to predict or prevent each failure? (proactive tasks and task intervals) • What should be done if a suitable proactive task cannot be found? (default actions) RCM is undeniably a valuable maintenance concept. It takes into account system functionality, and not just the equipment itself. The focus is on reliability. Safety and environmental integrity are considered to be more important than cost. Applying RCM helps to increase the asset’s lifetime and establish a more efficient and effective maintenance. Its structured approach fits in the knowledge management philosophy: reduced human error, more and better historical data and analysis, exploitation of expert knowledge and so forth. RCM is popular and many RCM implementations have started during the last decade. Although RCM offers many benefits, there are also drawbacks. From the conceptual point of view there are some weak points. For instance, the fact that the original RCM does not offer a task packaging feature and thus does not automatically offer a workable maintenance plan and the fact that the standard decision charts and forms offered are helpful but also far from perfect. A serious remark, mainly from the academic side, is about the scientific basis of RCM: the FMEA analysis, which is the heart of the RCM analysis, is often done on a rather ad hoc basis. Often available statistical data are insufficient or inaccurate, there is a lack of insight in the equipment degradation process (failure mechanisms) and the physical environment (e.g. corrosive or dusty environment) is ignored. The balance between valuable experience and equally valuable, objective statistical evidence is often absent. Many companies call in the (expensive) help of consultants to implement RCM; some of these consultants however are not capable of offering the help wanted and this – in combination with the lack of in-house experience with RCM – discredits this methodology. RCM is in fact an on-going process, which often causes reluctance to engage in a RCM project. RCM is undoubtedly a very resource consuming process, which also makes it difficult to apply RCM to all equipment.

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2.3.3.5 RCM-Related Concepts RCM as such has proven to be a very valuable concept, focussing on reliability and paying attention to safety and environment. Its structured approach ensures asset sustainability. However, there are some drawbacks that should be kept in mind and, if possible, remedied. In the literature one can find many RCM-related concepts such as Gits, Coetzee, BCM, RBCM, streamlined RCM, and so forth. All of them adopt RCM principles with the intention of solving some of its shortcomings. These group of concepts constitute the bridging step to the third generation of maintenance concepts. Gits (1984) developed an RCM-like maintenance concept. The main difference with the original RCM is the fact that the methodology delivers a workable maintenance plan. The focus of the concept is on technical and organizational aspects, rather than on economic considerations. This three-phase approach establishes the maintenance plan by quantifying and clustering basic maintenance rules. Those rules are harmonised in operational entities that describe what exactly must be done. Later on, Jones (1995) put forward risk based reliability centred maintenance (RBCM), a new variance of basic RCM. Basically, RBCM can be described as RCM, but with a strong statistical background. This tackles and eliminates the drawback of the ad hoc FMEA of the traditional RCM approach. Risk based inspections (RBI) are one of the core concepts here. The RBI methodology enables the assessment of the likelihood and potential consequences of pressure equipment failures. RBI provides companies with the opportunity to prioritize equipment inspections and optimize the inspection methods, frequencies and resources. Furthermore, RBI helps to develop specific equipment inspection plans and enable the implementation of RCM as such. This results in improved safety, lower failure risks, fewer forced shutdowns, and reduced operational costs. The risk-based approach requires a systematic and integrated use of expertise from the different disciplines that affect plant integrity. These include design, materials selection, operating parameters and scenarios, and understanding of the current and future degradation mechanisms and of the risks involved. So far, all preceding RCM inspired concepts aimed at improving technical drawbacks of RCM by coverting them into workable solutions. It was not until Kelly (1997), with his business-centred maintenance BCM, a full-fledged concept for determining a detailed maintenance plan, that the business as such gained the focal point. Kelly emphasised the importance of identifying, mapping and auditing the maintenance function. The BCM concept also pays attention to the necessary administrative support. Kelly calls his approach a BUTD approach, bottom-up/top-down approach. First, it is a top-down step that starting from the business context, the exact objectives for maintenance are outlined considering all corporate level. The second step is a bottom-up step. It aims at establishing a life maintenance plan for all equipments. In a third and last step, all item life plans are fitted in a maintenance strategy. Applying BCM thus results in a detailed maintenance schedule, ready for use. RCM implementation is complex, time consuming and is not straightforward. Hence, it should be implemented in a controlled fashion with total support of all levels of the organizations. Coetzee (2002) mentions that RCM is a core methodology to ensure that the organization can achieve world-class results. However, to

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achieve this objective the traditional RCM should be enhanced. Coetzee proposes a “new” RCM blending concept from different RCM authors’ related techniques. He also puts forward some innovations like the funnelling approach to ensure that RCM efforts are concentrated on the most important failure modes in the organization. Finally, there is a vast range of so-called “streamlined RCM” concepts. These concepts claim to be derivations of RCM. It is consultants who mainly promote streamlined RCM as the solution for the resource consuming character of RCM. Although streamlining sounds attractive it should be carefully applied, in order to keep the RCM benefits. Different streamlining approaches exist; however, very few are acceptable as formal RCM methodologies. Based on Pintelon and Van Puyvelde (2006), Table 2.3 provides a picture of popular streamlined RCM approaches. Table 2.3. Classification of streamlined RCM concepts

Characteristics

Pitfalls

Retro-active approach

Example

Starts from the existing maintenance plan. Determines the failure mode for all maintenance tasks and implements the last RCM steps for these.

Quite time-consuming to find the failure modes for all tasks.Functions” are detected on ad hoc basis. It Implies that the existing maintenance plan is good.

Generic approach

Uses generic lists of failure modes, or even generic analyses of technical systems

Ignores the operational context of the technical systems and the current maintenance practices. It assumes a standard level of analysis detail for all systems.

Skipping approach

Omits one or more steps. Typically, the first step (functions) is skipped and the analysis starts with listing the failure modes.

Omits the first and essential step of RCM, i.e. the functional analysis and as such also does not allow for a sound performance standard setting

Criticality approach

Limits the implementation to critical functions and/or failures for these a full RCM analysis is performed.

Often determines criticality on an ad hoc basis or uses criticality tools which are less reliable than the RCM approach

Troublemaker approach

Carries out a full RCM analysis for critical equipment only. Critical equipment is defined here as bottleneck equipment, which had a lot of maintenance problems in the past or is critical in terms of safety hazards.

Idem as above, although here all RCM steps are followed which guarantees a complete “picture”.

2.3.3.6 Customized Maintenance Concepts The value driven maintenance (VDM) methodology proposed by Haarman and Delahay (2004) builds a bridge between traditional maintenance philosophies and the shareholders’ value. Not only does VDM simplify the boardroom discussion, it also shows that far from being a cost center, maintenance is actually a major economic value within the overall business performance. It is built on established

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best maintenance practices and concepts such as TPM, RCM and RBI. It shows where the added-value of maintenance lies and how an organisation can be best structured to realise this value. One of the main contributions of VDM is that it offers a common language to management and maintenance to discuss maintenance matters. VDM identifies four value drivers in maintenance and provides concepts to manage by those drivers. For all four value drivers, maintenance can help to increase a company’s economic value. VDM makes a link between value drivers and core competences. For each of the core competences, some managerial concepts are provided. Most recently, Waeyenbergh (2005) presents CIBOCOF as a framework to developed customised maintenance concepts. CIBOCOF starts out from the idea that although all maintenance concepts available from the literature contain interesting ideas, none of them is suitable for implementation without further customization. Companies have their own priorities in implementing a maintenance concept and are likely to go for “cherry picking” from existing concepts. CIBOCOF offers a framework to do this in an integrated and structured way. Figure 2.6 illustrates the steps that this concept structurally goes through. A particularly interesting step is step 5, maintenance policy optimization, where a decision chart is offered to determine which mathematical decision model can be used to optimize the chosen policy (step 4). This decision chart guides the user through the vast literature on the topic. M2 Technical analysis M1 Start-up

Maintenance Plan

M5 Continuous improvement

M3 Policy decision making

M4 Implementation & Evaluation

Figure 2.6. CIBOCOF logic

2.4 Maintenance Manager As maintenance management evolved, so did the job of the maintenance manager. Clearly maintenance management is no longer a pure technical function. Business economics (cost-benefit considerations) and business context (how important are the installations in question?, what are the functional requirements?, …) play an important role. A good maintenance manager needs to have a technical background in order to have an eye for the “big picture” and not lose any aspect out of sight.

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Nowadays, the decisions expected from the maintenance manager are complex and sometimes can have far reaching consequences. He/she is (partly) responsible for operational, tactical and strategical aspects of the company’s maintenance management. This involves the final responsibility for operational decisions like the planning of the maintenance jobs and tactical decisions concerning the long-term maintenance policy to be adopted. More recently, maintenance managers are also consulted in strategic decisions, e.g. purchases of new installations, design choices, personnel policy, … The career path of today’s maintenance manager starts out from a rather technical content, but evolves over time into more financial and strategic responsibilities. This career path can be horizontal or vertical. It is also important that the maintenance manager is a good communicator and people manager, as maintenance remains a labor-intensive function. The maintenance manager needs to be able to attract and retain highly skilled technicians. On-going training for technicians is needed to keep track of the rapidly evolving technology. Motivation of maintenance technicians often requires special attention. Job autonomy in maintenance is more than in production, instructions may be vague, immediate assessment of the quality of work is mostly not possible, complaints are more often heard than compliments etc. Aspects like safety and ergonomics are an indispensable element in current maintenance management. Besides people, materials are another important resource for maintenance work. Maintenance material logistics mainly concerns the spare parts management and the determination of finding the optimum trade-off between high spare parts availability and the corresponding stock investments. The above described evolution in maintenance management incurs a sharp need for decision support techniques of various nature: statistical analysis tools for predicting the failure behaviour of equipment, decision schemes for determining the right maintenance concept, mathematical models to optimize the maintenance policy parameters (e.g. PM frequency), decision criteria concerning e-maintenance, decision aids for outsourcing decisions, etc. Table 2.4 illustrates the use of some decision support techniques for maintenance management. These techniques are available and have proven their usefulness for maintenance, but they are not yet widely adopted. In the 1960s most maintenance publications were very mathematically oriented and mainly focussed on reliability. The 1970s and early 1980s publications were more focused on maintenance policy optimization such as determination of optimum preventive maintenance interval, planning of group replacements and inspection modelling. This was a step forward, although these models still often were too focussed on mathematical tractability rather than on realistic assumptions and hypotheses. This caused an unfortunate gap between academics and practitioners. The former had the impression that industry and service sector were not “ready” for their work, while the latter felt frustrated because the models were too theoretical. Fortunately, this is changing. Academics pay more attention to the reallife background of their subject and practitioners discover the usefulness of the academic work. Moreover academic work gets broader and offers a more diverse range of models and concepts, such as maintenance strategy design models, e-maintenance concepts, service parts supply policies, and the like besides the more traditional maintenance optimization models. With the introduction of main-

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tenance software, the necessary data required for these models could be more easily collected. There still is a big gap between practitioners and academics, but it is already slowly closing. Table 2.4. OR/OM techniques and its application in maintenance Techniques

Application examples in maintenance management

Statistics

Describing failure behaviour

Reliability theory

Reliability prediction of complex systems

Markov theory

Availability studies of repairable systems

Renewal theory

Replacement decisions (group or individual)

Math programming

Maintenance policy parameter optimization

Decision theory

Decisions under uncertainty

Queueing theory

Trade-off personnel capacity - service level

Simulation

Comparison of alternative maintenance policies

Inventory control

MRO management: FMI, NMI, SMI and VSMI

Time and motion study

Estimation of maintenance intervention times

Scheduling – rostering

Daily planning of maintenance jobs

Project planning

Planning of turnaround, large renovation projects

MCDM

Selecting the best outsourcing partner

MRO = maintenance, repair and operating supplies, FMI = fast moving items, NMI = normal moving items, SMI = slow moving items, VSMI = very slow moving items, MCDM = multi-criteria decision making, OR/OM=Operations Research / Operations Management

The help from information technology (IT) is of special interest when discussing decision support for maintenance managers. Computerized maintenance management systems (CMMS), also called computer aided maintenance management (CAMM), maintenance management information systems (MMIS) or even enterprise asset management systems (EAM), nowadays offer substantial support for the maintenance manager. These systems too have evolved over time (Table 2.5). IT of course also supports the e-maintenance applications and offers splendid opportunities for knowledge management implementations. At the beginning of the knowledge management hype, knowledge management was mainly aimed at fields like R&D, innovation management, etc. Later on the potential benefits of knowledge management were also recognized for most business functions. For maintenance management, a knowledge management programme helps to capture the implicit knowledge and expertise of maintenance workers and secure this information in information systems, so making it accessible for other technicians. The benefits of this in terms of consistency in problem solving approach and knowledge retention are obvious. Other knowledge management applications can be, for example, expert systems, assisting in the diagnosis of complex equipment

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failures, or data mining on maintenance history records to learn about failure causes. A knowledge management programme will also help to keep track of individual skills and expertise and as such support personnel management over time. Table 2.5. Evolution of CMMS

1990s ...

1980s–1990s

1970s

Business IT systems

CMMS

Characteristics

1st generation

Mainly registration and data administration (EDP). Limited or no process support. Low priority mainframe applications. Limited software market, a lot of in-house development.

2nd generation

Cost control and work order management; MRO management most often included, ... Link with company’s financial information module. First MIS for maintenance Many stand-alone microcomputer applications. Dynamic, but not always reliable, software market.

3rd generation

Broader, e.g. also asset utilization, and EHS module External communication possible, e.g. e-MRO. Enhanced analytical capabilities. Multimedia and web enabled features. Matured market for embedded (part of e.g. ERP) or BoB.

Clearly, the evolution in maintenance management offers a challenging job environment for today’s maintenance manager. This maintenance manager needs to be aware of “the big picture”, i.e. the business context and the maintenance organization as a whole. Moreover, he/she needs to have a sound technological background and be prepared to keep informed of technological evolutions. The maintenance manager needs real management skills, to manage the resources – personnel and materials – in an efficient and effective way, while keeping asset utilization and asset life cycles in mind. Growing in the function of maintenance manager, will also mean acquiring new skills, e.g. in financial management. Last but not least, today’s maintenance manager needs to be flexible, flexible to face threats and to grab opportunities in today’s dynamic business environment where increasing globalisation, many mergers and acquisitions, growing outsourcing markets and emerging e-maintenance technologies are part of daily life.

2.5 Conclusions and New Challenges of Maintenance Maintenance management undoubtedly has undergone major changes during the past decade. It has moved from being low profile, necessary but difficult to manage problems, to be regarded a prominent business function, an important element in business strategy. Not only practitioners have changed their mind about maintenance; academics did as well. Maintenance nowadays is a professional business

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function and an area of intensive academic research. Efforts are aimed at advancing towards world class maintenance and providing methodologies to do so. Pintelon et al. (2006) describes several maintenance maturity levels required to achieve world class maintenance; these are illustrated in Figure 2.7.

Figure 2.7. Maturity levels of maintenance

Maintenance concept optimization has professionalized. Corrective and precautionary actions are combined in different policies, from reactive to preventive and from predictive to proactive policies. A sound insight into the pros and cons of each of these policies is available in practice and research supports the selection and optimization of these policies. These policies are no longer ad hoc and lose elements within maintenance management but policies are also embedded in maintenance concepts, focussing on reliability and productivity. These concepts ensure consistent decision making for all equipment and at the same time allow for individualized installation maintenance concepts. Decision tools are available to support this process. Top management nowadays, at least in most companies, recognizes the importance of maintenance as an element of their business strategy. Expectations for maintenance are no longer formulated as “keep things running”, but are based upon the overall business strategy. This strategy can be based on flexibility, quality and low cost. The maintenance organization, with its structural and infrastructural elements, is built accordingly. The previous paragraph may give the impression that all problems for maintenance management are already solved; this however is not the case. New opportunities in terms of, for example, outsourcing and e-maintenance exist. Moreover, there is a threatening gap between the top management level and the overall maintenance strategy determination and the tactical level on which the maintenance concepts are designed, detailed and implemented (Figure 2.8). The gap, however, is there between the alignment of the tactical and subsequent operational phase on the one hand and the strategic phase on the other. While both aspects are well studied, the link between the two is often not well established. This leads to disappointments with top management as well as frustration with maintenance managers. Research shows a similar gap. There is some — though

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still not enough — research on the link between maintenance and business strategy. The main focus of maintenance management research is still on the tactical and operational planning. Links between the former and the latter part of research however are still very rare. Closing this gap by linking maintenance and business throughout all decision levels is one of the major challenges for the future; every step taken brings us closer to real world-class maintenance.

Figure 2.8. Gap between maintenance and business strategy

2.6 List of Abbreviations BCM: Business-centred maintenance BoB: Best-of-breed BUTD: Bottom-up/top-down analysis CAMM: Computer aided maintenance management CBM: Condition-based maintenance CFR: Constant failure rate

CIBOCOF: Center Industrieel Beleid Onderhoudsontwikkelingsframework CM: Corrective maintenance CMMS: Computerized maintenance management systems DOM: Design-out of Maintenance DSS: Decision support systems

Maintenance: An Evolutionary Perspective

EAM: Enterprise asset management (system) EHS: Energy, health and safety EDP: Electronic data processing EUC: End user computing FBM: Failure-based maintenance FMEA: Failure modes and effect analysis FMI: Fast moving items FMS: Flexible manufacturing systems GUI: Graphical user interface ICT: Information communication technology IFR: Increasing failure rate ILS: Integrated logistics support IT: Information technology JIT: Just-in-time LCC: Life-cycle costing LSA: Logistics support analysis MCDM: Multi-criteria decisionmaking MIS: Management information systems MMIS: Maintenance management information system MRO: Maintenance repair and operating supplies

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MTBF: Mean-time-between-failures MTTR: Mean-time-to-repair NMI: Normal moving items OBM: Opportunity-based maintenance OEE: Overall equipment effectiveness OM: Operations management OR: Operations research PM: Precautionary maintenance Q&D: Quick & dirty decision charts R&D: Research & development RBI: Risk-based inspections RCBM: Risk-based centred maintenance RCM: Reliability-centred maintenance ROI: Return on investment SAE: Society of automotive engineering SMED: Single minute exchange of dies SMI: Slow moving items TBM: Time-based maintenance TCO: Total cost of ownership TPM: Total productive maintenance TQM: Total quality management UBM: Use-based maintenance VDM: Value-driven maintenance VSMI: Very slow moving items WIP: Work in progress

2.7 References Anderson, R.T., Neri, L., (1990), Reliability Centred Maintenance: Management and Engineering Methods, Elsevier Applied Sciences, London Blanchard, B.S., (1992), Logistics Engineering and Management, Prentice Hall, Englewood Cliffs, New Jersey Cho, I.D, Parlar, M., (1991), A survey on maintenance models for multi-unit systems. European Journal of Operational Research, 51:1–23 Coetzee, J.L., (2002), An Optimized Instrument for Designing a Maintenance Plan: A Sequel to RCM. PhD thesis, University of Pretoria, South-Africa Dekker, R., (1996) Applications of maintenance optimization models: A review and analysis. Reliability Engineering and System Safety, 52(3):229–240 Dekker, R., and Scarf, P.A., (1998) On the impact of optimisation models in maintenance decision making: the state of the art. Reliability Engineering and System Safety, 60:111–119 Geraerds, W.M.J., (1972), Towards a Theory of Maintenance. The English University Press. London.

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Gits, C.W., (1984), On the Maintenance Concept for a Technical System: A Framework for Design, Ph.D.Thesis, TUEindhoven, The Netherlands Haarman, M. and Delahay, G., (2004), Value Driven Maintenance – New Faith in Maintenance, Mainnovation, Dordrecht, The Nederlands Jones, R.B., (1995), Risk-Based Maintenance, Gult Professional Publishing (Elsevier), Oxford Kelly, A., (1997), Maintenance Organizations & Systems: Business-Centred Maintenance, Butterworth-Heinemann, Oxford McCall, J.J. (1965), Maintenance policies for stochastically failing equipment: A survey. Management Science, 11 (5):493–524 Moubray, J., (1997), Reliability-Centred Maintenance. Second Edition. ButterworthHeinemann, Oxford Nowlan, F.S., Heap, H.F., (1978), Reliability Centered Maintenance, United Airlines Publications, San Fransisco Parkes, D. in Jardine, A.K.S., (1970), Operational Research in Maintenance, University of Manchester Press, Manchester Pintelon, L., Gelders, L., Van Puyvelde, F., (2000), Maintenance Management, Acco Leuven/ Amersfoort Pintelon, L., Gelders, L., (1992) Maintenance management decision making. European Journal of Operational Research, 58:301–317 Pintelon, L., Pinjala, K., Vereecke, A., (2006), Evaluating the Effectiveness of Maintenance Strategies, Journal of Quality in Maintenance Engineering (JQME), 12(1):214–229 Pintelon, L., Van Puyvelde, F., (2006), Maintenance Decision Making, Acco, Leuven, Belgium Takahashi, Y. and Takashi, O., (1990) TPM: Total Productive Maintenance. Asian Productivity Organization, Tokyo Valdez-Flores, C., Feldman, R.M., (1989) A survey of preventive maintenance models for stochastically deteriorating single-unit systems. Naval Research Logistics, 36:419–446 Waeyenbergh, G., (2005), CIBOCOF – A Framework for Industrial Maintenance Concept Development, PhD thesis, Centre for Industrial Management – K.U.Leuven, Leuven, Belgium Waeyenbergh, G., Pintelon, L., (2002) A framework for maintenance concept development. International Journal of Production Economics, 77:299–313 Wang H., (2002), A survey of maintenance policies of deteriorating systems. European Journal of Operational Research, 139:469–489

3 New Technologies for Maintenance Jay Lee and Haixia Wang

3.1 Introduction For years, maintenance has been treated as a dirty, boring and ad hoc job. It’s seen as critical for maintaining productivity but has yet to be recognized as a key component of revenue generation. The question most often asked is “Why do we need to maintain things regularly?” The answer is “To keep things as reliable as possible.” However, the question that should be asked is “How much change or degradation has occurred since the last round of maintenance?” The answer to this question is “I don’t know.” Today, most machine field services depend on sensor-driven management systems that provide alerts, alarms and indicators. The moment the alarm sounds, it’s already too late to prevent the failure. Therefore, most machine maintenance today is either purely reactive (fixing or replacing equipment after it fails) or blindly proactive (assuming a certain level of performance degradation, with no input from the machinery itself, and servicing equipment on a routine schedule whether service is actually needed or not). Both scenarios are extremely wasteful. Rather than reactive maintenance, “fail-and-fix,” world-class companies are moving forwards towards “predict-and-prevent” maintenance. A maintenance scheme, referred to as condition based maintenance (CBM), was developed by considering current degradation and its evolution. CBM methods and practices have been continuously improved for the last decades; however, CBM is conducted at equipment level − one piece of equipment at a time, and the developed prognostics approaches are application or equipment specific. Holistic approach, real-time prognostics devices, and rapid implementation environment are potential future research topics in product and system health assessment and prognostics. With the level of integrated network systems development in today’s global business environment, machines and factories are networked, and information and decisions are synchronized in order to maximize a company’s asset investments. This generates a critical need for a real-time remote machinery prognostics and health management (R2M-PHM) system. The unmet needs in maintenance can be categorized into the following:

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1. Machine intelligence: intelligent monitoring, predict and prevent, and compensation, reconfiguration for sustainability (self-maintenance). 2. Operations intelligence: prioritize, optimize, and responsive maintenance scheduling for reconfiguration needs. 3. Synchronization intelligence: autonomous information flow from market demand to factory asset utilization. Based on the unmet needs in maintenance, many research and development questions concerning next generation maintenance systems can be raised. Some of them are the following: 1. How to adapt maintenance schedules to cope dynamically with shop-floor reality? 2. How to feed back information and knowledge gathered in maintenance to the designers of the process? 3. How to link maintenance policies to corporate strategy and objectives? 4. How to synchronize production scheduling based on maintenance performance? The rest of this chapter is organized as follows. Section 2 gives a state-of-theart review on maintenance technologies, which includes a maintenance paradigm overview and CBM prognostics approaches. Section 3 presents the newly developed platform of Watchdog Agent®-based real-time remote machinery prognostics and health management (R2M-PHM) system, the Watchdog Agent® toolbox method for multi-sensor performance assessment and prognostics, and real-life industrial case studies. Section 4 summarizes new developments and discusses future work.

3.2 State-of-the-art Reviews on Maintenance Technologies 3.2.1 Maintenance Paradigm Overview Looking back on the development history and forecasting the development tendency of maintenance technologies, the roadmap to excellence in maintenance can be illustrated as in Figure 3.1. 3.2.1.1 No Maintenance There are two kinds of situations in which no maintenance will occur: • •

No way to fix it: the maintenance technique is not available for a special application, or the maintenance technique is at too early stage of development. Isn’t worth it to fix it: some machines were designed to be used only once. When compared to maintenance cost, it may be more cost-effective just to discard it.

Neither of the scenarios above is within the scope of the discussion here.

Machine Performance and uptime

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Self-Maintenance or Maintenance-free Proactive Machine Maintenance (Failure Root causes analysis)

No Maintenance

Predictive Preventive Maintenance Maintenance (Scheduled Reactive Maintenance) Maintenance (Fire Fighting)

Figure 3.1. The development of maintenance technologies

3.2.1.2 Reactive Maintenance The aim of reactive maintenance is just to “fix it after it’s broken”, since most of the time a machine breaks down without warning and it is urgent for the maintenance crew to put it back to work: this is also referred to as “fire-fighting”. This fire-fighting mode of maintenance is still present in many maintenance operations today because accurate knowledge of the equipment behavior is lacking. Essentially, little to no maintenance is conducted and the machinery operates until a failure occurs. At this time, appropriate personnel are contacted to assess the situation and make the repairs as expeditiously as possible. In a situation where the damage to equipment is not a critical factor, plenty of downtime is available, and the values of the assets are not a concern, the fire-fighting mode may prove to be an acceptable option. Of course, one must consider the additional cost of making repairs on an emergency basis since soliciting bids to obtain reasonable costs may not be applicable in these situations. Due to market competition and environmental/safety issues, the trend is toward appropriating an organized and efficient maintenance program as opposed to firefighting. 3.2.1.3 Preventive Maintenance Preventive maintenance (PM) is an equipment maintenance strategy based on replacing, overhauling or remanufacturing an item at fixed or adaptive intervals, regardless of its condition at the time. These maintenance operations models can be characterized as long term maintenance policies (Wang 2002) that do not take into account instantaneous equipment status. Scheduled restoration tasks and scheduled discard tasks are both examples of preventive maintenance tasks. In preventive maintenance, breakdowns are tracked and recorded in a database, and the information accumulated provides a base for general preventive actions. The age-dependent PM policy can be considered as the most common maintenance policy in which a unit’s PM times are based on the age of the unit. The basic idea is to replace or repair a unit at its age T or failure whichever occurs first (Badia et al., 2002; Mijailovic 2003). Commonly used equipment reliability indices such as mean time between failure (MTBF) and mean time to repair (MTTR) are extracted

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from the historical databases of equipment behavior over time. These two indices provide a rough estimate of the time between two adjacent breakdowns and the mean time needed to restore a system when such breakdowns happen. Although equipment degradation processes vary from case to case, and the causes of failure can be different as well, the information contained in MTBF and MTTR can still be informative. Other indices can also be extracted and used, including the mean lifetime, mean time to first failure, and mean operational life, as discussed by Pham et al. (1997). With the introduction of minimal repair and imperfect maintenance, various extensions and modifications to the age-dependent PM policy have been proposed (Bruns 2002; Chen et al. 2003). Another preventive maintenance policy that received much attention is the periodic PM policy, in which degraded machines are repaired or replaced at fixed time intervals independent of the equipment failures. Various modifications and enhancements to this maintenance policy have also been proposed recently (Cavory et al. 2001). The preventive maintenance schemes are time-based without considering the current health state of the product, and thus are inefficient and less valuable for a customer whose individual asset is of the most concern. For the case of helicopter gearboxes, it was found that almost half of the units were removed for overhaul even though they were in a satisfactory operating condition. Therefore techniques for more economical and reliable maintenance are needed. 3.2.1.4 Predictive Maintenance Predictive maintenance (PdM) is a right-on-time maintenance strategy. It is based on the failure limit policy in which maintenance is performed only when the failure rate, or other reliability indices, of a unit reaches a predetermined level. This maintenance strategy has been implemented as condition based maintenance (CBM) in most production systems, where certain performance indices are periodically (Barbera et al. 1996; Chen and Trivedi 2002) or continuously monitored (Marseguerra et al. 2002). Whenever an index value crosses some predefined threshold, maintenance actions are performed to restore the machine to its original state, or to a state where the changed value is at a satisfactory level in comparison to the threshold. Predictive maintenance can be best described as a process that requires both technology and human skills, while using a combination of all available diagnostic and performance data, maintenance history, operator logs and design data to make timely decisions about maintenance requirements of major/critical equipment. It is this integration of various data, information and processes that leads to the success of a PdM program. It analyzes the trend of measured physical parameters against known engineering limits for the purpose of detecting, analyzing and correcting a problem before a failure occurs. A maintenance plan is devised based on the prediction results derived from condition based monitoring. This method can cost more up front than PM because of the additional monitoring hardware and software investment, cost of manning, tooling, and education that is required to establish a PdM program. However, it provides a basis for failure diagnostics and maintenance operations, and offers increased equipment reliability and a sufficient advance in information to improve planning, thereby reducing unexpected downtime and operating costs.

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3.2.1.5 Proactive Maintenance Proactive maintenance (PaM) is a new maintenance concept that is emerging along with the development of business globalization. It encompasses any tasks that seek to realize the seamless integration of diagnosis and prognosis information and maintenance decision making via a wireless internet or satellite communication network. Machine health information should represent a trend, not just a status, so that a company’s productivity can be focused on asset-level utilization, not just production rates. Moreover, through integrated life-cycle management, such degradation information can be used to make improvements in every aspect of a product’s life-cycle. Intelligent maintenance systems (IMS) presented by Lee (1996) is a PaM representative. Specifically, it has three main working directions as follows: • •

•

Develop intertwined embedded informatics and electronic intelligence in a networked and tether-free environment and enable products and systems to intelligently monitor, predict, and optimize their performance. Change “failure reactive” to “failure proactive” by avoiding the underlying conditions that lead to machine faults and degradation. Focus on analyzing the root cause, not just the symptoms. That is, seek to prevent or to fix failure from its source. Feed the maintenance information back to the product, process and machine design, and ultimately make improvements in every aspect of product lifecycle.

3.2.1.6 Self-maintenance Self-maintenance is a new design and system methodology. Self-maintenance machines are expected to be able to monitor, diagnose, and repair themselves in order to increase their uptime. One system approach to enabling self-maintenance is based on the concept of functional maintenance (Umeda et al. 1995). Functional maintenance aims to recover the required function of a degrading machine by trading off functions, whereas traditional repair (physical maintenance) aims to recover the initial physical state by replacing faulty components, cleaning, etc. The way to fulfil the self-maintenance function is by adding intelligence to the machine, making it clever enough for functional maintenance, so that the machine can monitor and diagnose itself, and it can still maintain its functionality for a while if any kind of failure or degradation occurs. In other words, self-maintainability would be appended to an existing machine as an additional embedded reasoning system. The required capabilities of a self-maintenance machine (SMM) are defined as follows (Labib 2006): • •

Monitoring capability: SMM must have the ability of on-line condition monitoring using sensor fusion. The sensors send the raw data of machine condition to a processing unit. Fault judging capability: from the sensory data, the SMM can judge whether the machine condition is at normal or abnormal state. By judging the condition of the machines, we can know the current condition and time left to failure of the machines.

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• •

• •

Diagnosing capability: if the machine condition is at abnormal state, the causes of faults must be diagnosed and identified to allow repair planning action to be carried out. Repair planning capability: the machine is able to propose repair actions based on the result of diagnosis and functional maintenance. The repair planning action is performed using knowledge from the experts which is stored in the data base system. There may be more than one repair action proposed; however, the optimized one will be selected to be implemented. Repair executing capability: the maintenance is carried out by the machine itself without any human intervention. This can be achieved through computer control system and actuators in the machines. Self-learning and improvement: when faced with unfamiliar problems, the machine is able to repair itself and it is expected that if such problems occur again, the machine will take a shorter time for repairing itself and the outcome of maintenance will be more effective and efficient.

Efforts towards realizing self-maintenance have been mainly in the form of intelligent adaptive control, where investigation of control was achieved using fuzzy logic control. In order to realize self-maintenance, one needs to develop and implement an adaptive artificial neuron-fuzzy inference system which allows the fuzzy logic controller to learn from the data it is modeling and automatically produce appropriate membership functions and the required rules. Such a controller must be able to cater for sensor degradation and this leads to self-learning and improvement capabilities. Another system approach to enabling self-maintenance is to add the self-service trigger function to a machine. The machine self-monitors, self-prognoses and selftriggers a service request before a failure actually occurs. The maintenance task may still be conducted by a maintenance crew, but the no gap integration of machine, maintenance schedule, dispatch system and inventory management system will minimize maintenance costs and raise customer satisfaction. 3.2.2 Prognostics Approaches for Condition Based Maintenance Condition based maintenance (CBM) was presented as a maintenance scheme to provide sufficient warning of an impending failure on a particular piece of equipment, allowing that equipment is to be maintained only when there is objective evidence of an impending failure. CBM methods and practices have been continuously improved in recent decades. Sensor fusion techniques are now commonly in use due to the inherent superiority in taking advantage of mutual information from multiple sensors (Hansen et al. 1994; Reichard et al. 2000; Roemer et al. 2001). A variety of techniques in vibration, temperature, acoustic emissions, ultrasonic, oil debris, lubricant condition, chip detectors, and time/stress analyses has received considerable attention. For example, vibration signature analysis, oil analysis and acoustic emissions, because of their excellent capability for describing machine performance, have been successfully employed for prognostics for a long time (Kemerait 1987; Wilson et al. 1999; Goodenow et al. 2002). Current prognostic approaches can be classified into three basic groups: model-based

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approach, data-driven approach, and hybrid approach. The model-based approach requires detailed knowledge of the physical relationships between, and characteristics of, all related components in a system. It is a quantitative model used to identify and evaluate the difference between the actual operating state determined from measurements, and the expected operating state derived from the values of the characteristics obtained from the physical model. Bunday (1991) presented the theory and methodology of obtaining reliability indices from historical data. In direct implementation in maintenance, the reliability of the system is kept at a defined level, and whenever the reliability falls below the defined level, maintenance actions should take place to restore it back to its proper level. However, it is usually prohibitive to use the model-based approach since relationships and characteristics of all related components in a system and its environment are often too complicated to build a model with a reasonable amount of accuracy. In some cases, values of some process parameters/factors are not readily available. A poor model leads to poor judgment. The data-driven approach requires a large amount of history data representing both normal and “faulty” operations. It uses no a priori knowledge of the process but, instead, derives behavioral models only from measurement data from the process itself. Pattern recognition techniques are widely used in this approach. General knowledge of the process can be used to interpret data analysis results, based on which qualitative methods such as fuzzy logic, and artificial intelligence methods can be used for decision making to realize fault prevention. The hybrid approach fuses the model-based information and sensor-based information and takes advantage of both model-driven and datadriven approaches through which more reliable and accurate prognostic results can be generated (Hansen et al. 1994). Garga et al. (2001) introduced a hybrid reasoning method for prognostics, which integrated explicit domain knowledge and machinery data. In this approach, a feed-forward neural network was trained using explicit domain knowledge to get a parsimonious representation of the explicit domain knowledge. However, a major breakthrough has not been made since. Existing prognostic methods are application or equipment specific. For instance, the development of neural networks has added new dimensions to solving existing problems in conducting prognostics of a centrifugal pump case (Liang et al. 1988). A comparison of the results using the signal identification technique shows various merits of employing neural nets including the ability to handle multivariate wear parameters in a much shorter time. A polynomial neural network was conducted in fault detection, isolation, and estimation for a helicopter transmission prognostic application (Parker et al. 1993). Ray and Tangirala (1996) built a stochastic model of fatigue crack dynamics in mechanical structures to predict remaining service time. Fuzzy logic-based neural networks have been used to predict paper web breakage in a paper mill (Bonissone 1995) and the failure of a tensioned steel band with seeded crack growth (Swanson 2001). Yet another prognostic application presented an integrated system in which a dynamically linked ellipsoidal basis function neural network was coupled with an automated rule extractor to develop a tree-structured rule set which closely approximates the classification of the neural network (Brotherton et al. 2000). That method allowed assessment of trending from the nominal class to each of the identified fault classes, which means quantitative

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prognostics were built into the network functionality. Vachtsevanos and Wang (2001) gave an overview of different CBM algorithms and suggested a method to compare their performance for a specific application. Prognostic information, obtained through intelligence embedded into the manufacturing process or equipment, can also be used to improve manufacturing and maintenance operations in order to increase process reliability and improve product quality. For instance, the ability to increase reliability of manufacturing facilities using the awareness of the deterioration levels of manufacturing equipment has been demonstrated through an example of improving robot reliability (Yamada and Takata 2002). Moreover, a life cycle unit (LCU) (Seliger et al. 2002) was proposed to collect usage information about key product components, enabling one to assess product reusability and facilitating the reuse of products that have significant remaining useful life. In spite of the progresses in CBM, many fundamental issues still remain. For example: 1. Most research is conducted at the single equipment level, and no infrastructure exists for employing a real-time remote machinery diagnosis and prognosis system for maintenance. 2. Most of the developed prognostics approaches are application or equipment specific. A generic and scalable prognostic methodology or toolbox doesn’t exist. 3. Currently, methods are focused on solving the failure prediction problem. The need for tools for system performance assessment and degradation prediction has not been well addressed. 4. The maintenance world of tomorrow is an information world for featurebased monitoring. Features used for prognostics need to be further developed. 5. Many developed prediction algorithms have been demonstrated in a laboratory environment, but are still without industry validation. To address the afore-mentioned unmet needs, Watchdog Agent®-based intelligent maintenance systems (IMS) has been presented by the IMS Center with a vision to develop a systematic approach in advanced prognostics to enable products and systems to achieve near-zero breakdown reliability and performance.

3.3 Watchdog Agent®-based Intelligent Maintenance Systems Today most state-of-the-art manufacturing, mining, farming, and service machines (e.g., elevators) are actually quite “smart” in themselves. Many sophisticated sensors and computerized components are capable of delivering data concerning a machine’s status and performance. The problem is that little or no practical use is made of most of this data. We have the devices, but we do not have a continuous and seamless flow of information throughout entire processes. Sometimes this is because the available data is not rendered in a useable, or instantly understandable,

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form. More often, no infrastructure exists for delivering the data over a network, or for managing and analyzing the data, even if the devices were networked. Watchdog Agent®-based real-time remote machinery prognostics and health management (R2M-PHM) system has been recently developed by the IMS Center. It focuses on developing innovative prognostics algorithms and tools, as well as remote and embedded predictive maintenance technologies to predict and prevent machine failures, as illustrated in Figure 3.2.

Figure 3.2. Key focus and elements of the Intelligent Maintenance Systems

The rest of the section is organized as follows. Section 3.1 deals with the platform of Watchdog Agent®-based real-time remote machinery prognostics and health management (R2M-PHM) system. Section 3.2 presents a generic and scalable prognostic methodology or toolbox, i.e., the Watchdog Agent® toolbox; and Section 3.3 illustrates the effectiveness and potentials of this new development using several real industry case studies. 3.3.1 Watchdog Agent®-based R2M-PHM Platform A generic and scalable prognostics framework was presented by Su et al. (1999) to integrate with embedded diagnostics to provide “total health management” capability. A reconfigurable and scalable Watchdog Agent®-based R2M-PHM platform is being developed by the IMS Center, which expands the well known open system architecture for condition-based maintenance (OSA-CBM) standard (Thurston and Lebold 2001) by including real-time remote machinery diagnosis and prognosis systems and embedded Watchdog Agent® technology. As illustrated in Figure 3.3, the Watchdog Agent® (hardware and software) is embedded onto machines to convert multi-sensory data to machine health information. The extracted information is managed and transferred through wireless internet or a satellite communication network, and service is automatically triggered.

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Figure 3.3. Illustration of IMS real-time remote machinery diagnosis and prognosis system

3.3.1.1 System Architecture The system architecture of the Watchdog Agent®-based R2M-PHM platform is shown in Figure 3.4. In most products or systems, different sensors measure different aspects of the same physical phenomena. For example, sensor signals, such as vibrations, temperature, pressure, etc. are collected. A “digital doctor” inspired by biological perceptual systems and machine psychology theory, the Watchdog Agent® consists of embedded computational prognostic algorithms and a software toolbox for predicting degradation of devices and systems. It is being built to be extensible and adaptable to most real-world machine situations. The health related information is saved to the database. The diagnostic and prognostic outputs of the Watchdog Agent®, which is mounted on all the machinery of interest, can then be fed into the decision support tools. Decision support tools help the operation personnel balance and optimize their resources, when one or more machines are likely to fail, by constantly looking ahead. For example, if a production line has three processes A, B and C, such that A has one machine, B has three machines, and C has one machine, what would we do if we could anticipate that one of the machines at station B is not behaving normally. Perhaps we would arrange a staging area for output from A, or perhaps we would ramp up production on the other two machines at station B. Whatever the case, we would be making our decision before experiencing the impending breakdown. These tools are critical to maintenance and process personnel, enabling them to stay ahead of the game, balancing limited resources with constant change in demand. Decision support tools also help minimize losses in productivity caused by downtime, and help production and logistics managers optimize their maintenance schedule to minimize downtime costs. The lean and necessary information for maintenance can then be determined and published to the internet through an embedded web server.

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Embedded software

Sensor signals Vibration Temperature Pressure

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Watchdog Agent® toolbox

Database

Decision support tools

Web server

Client software

Current Voltage On/Off …

Embedded operating system I/O cards

Remote computer

Embedded computer

Figure 3.4. System architecture of a reconfigurable Watchdog Agent®

The rapid development of web-enabled and cyber-infrastructure technologies is important in providing enablers for remote monitoring and prognostics. One of the major barriers is that most manufacturers adopt proprietary communication protocols which lead to difficulties in connecting diverse machines and products. Currently, the IMS Center is developing a web-enabled remote monitoring Deviceto-Business (D2B)™ platform for remote monitoring and prognostics of diversified products and systems. A system methodology and infotronics platform has been developed that enables the transformation of product condition data into more a useful health information format for remote and network-enabled prognostics applications. The MIMOSA (maintenance information management open system architecture) organization has adopted the IMS infotronic platform as one of its standard platforms and will use an IMS testbed to demonstrate MIMOSA standards in its future activities. As shown in Figure 3.5, the IMS infotronics platform includes the Watchdog Agent® toolbox (which contains adaptive algorithms for different situations and applications), decision support tools, data storage, and D2BTM (device-to-business) system level connectivity. The Watchdog Agent® toolbox includes signal processing, feature extraction, performance assessment, autonomous learning, prediction and prognostics functions. The lean and necessary information for maintenance from decision support tools can then be determined and sent out through D2BTM system level connectivity to remote workstations or computers.

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Figure 3.5.

Integrated infotronics platform

3.3.1.2 Hardware Requirements For a certain industry application, the selection of Watchdog Agent® hardware depends on characteristics of the input/output signals (for example, what type of input/output signal and how many channels needed), which tools or algorithms are selected (for example, different algorithms require different hardware computation and storage capacities), and the hardware’s working environment (for example, which decides the hardware’s storage type, temperature range, etc.). The hardware prototype currently used in the IMS Center is based on PC104 architecture, as shown in Figure 3.6a. PC104 architecture enables the hardware to be easily expanded to a multi-board system, which includes multiple CPUs and a large amount of input channels. It has a powerful VIA Eden 400MHz CPU and 128MB

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of memory since all of the tools are embedded into the hardware. It has 16 high speed analog input channels to deal with highly dynamic signals. It also has various peripherals that can acquire non-analog sensor signals such as RS232/485/432, parallel and USB. The prototype uses a compact flash card for storage, so it can be placed on top of machine tools and is suitable for withstanding vibrations in a working environment. Once a certain set of tools/algorithms is determined for a certain industry application, commercially available hardware, such as Advantech and National Instruments (NI) as illustrated in Figure 3.6b and c, respectively, will be further evaluated for customized Watchdog Agent® applications.

Figure 3.6a–c. Options of hardware prototypes for Watchdog Agent® application

3.3.1.3 Software Development The software system of the Watchdog Agent®-based IMS platform consists of two parts: the embedded side software and the remote side software, as shown in Figure 3.7. The embedded side software is the software running on the Watchdog Agent® hardware, which includes a communication module, a command analysis module, a task module, an algorithm module, a function module, and a DAQ module. The communication module is responsible for communicating with the remote side via TCP/IP protocol. The command analysis module is used to analyze different commands coming from the remote side. The task module includes multithread scheduling and management. The algorithm module contains specific watchdog agent tools. The function module has several auxiliary functions such as channel configuration, security configuration, and email list and so on. The DAQ module performs A/D conversion using either interrupt or software trigger to get data from different sensors. The remote side software is the software running on the remote computers. It is implemented by ActiveX control technology and can be used as a component of the Internet Explorer Browser. The remote side software is mainly composed of a communication module and a user interface module. The communication module is used for communicating with the embedded site via TCP/IP protocol. The user interface has a health information display, an ATC status display, and a discrete event display. It also possess an algorithm module, as well as error log database and data format interface.

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Figure 3.7. Software structure of Watchdog Agent®

3.3.1.4 Remote Monitoring Architecture and Human Machine Interface Standards A four-layer infrastructure for remote monitoring and human machine interface standards is illustrated in Figure 3.8. The data acquisition layer consists of multiple sensors which obtain raw data from the components of a machine or machines in different locations. The Network layer will use either traditional Ethernet connections, or wireless connections for communication between the Watchdog Agent®s, or for sending short messages (SM) to an engineer’s mobile phone via GPRS services. The Application layer functions as a control server to save related information and control the behavior of the Watchdog Agent®s in the network. The Enterprise layer offers a user-friendly interface for maintenance-related engineers to access information either via an Internet browser or a mobile phone.

Figure 3.8. Illustration of Watchdog Agent®-based remote monitoring architecture

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3.3.2 Watchdog Agent® Toolbox for Multi-sensor Performance Assessment and Prognostics The Watchdog Agent® toolbox, with autonomic computing capabilities, is able to convert critical performance degradation data into health features and quantitatively assess their confidence value to predict further trends so that proactive actions can be taken before potential failures occur. Figure 3.9 illustrates one of the developed enabling prognostics tools that can assess and predict the performance degradation of products, machines and complex systems.

Figure 3.9. MS innovation in advanced prognostics

The Watchdog Agent® toolbox enables one to assess and predict quantitatively performance degradation levels of key product components, and to determine the root causes of failure (Casoetto et al. 2003; Djurdjanovic et al. 2000; Lee 1995, 1996), thus making it possible to realize physically closed-loop product life cycle monitoring and management. The Watchdog Agent® consists of embedded computational prognostic algorithms and a software toolbox for predicting degradation of devices and systems. Degradation assessment is conducted after the critical properties of a process or machine are identified and measured by sensors. It is expected that the degradation process will alter the sensor readings that are being fed into the Watchdog Agent®, and thus enable it to assess and quantify the degradation by quantitatively describing the corresponding change in sensor signatures. In addition, a model of the process or piece of equipment that is being considered, or available application specific knowledge can be used to aid the degradation process description, provided that such a model and/or such knowledge exist. The prognostic function is realized through trending and statistical modeling of the observed process performance signatures and/or model parameters. In order to facilitate the use of Watchdog Agent® in a wide variety of applications (with various requirements and limitations regarding the character of signals, available processing power, memory and storage capabilities, limited space, power consumption, the user’s preference etc.) the performance assessment module of the

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Watchdog Agent® has been realized in the form of a modular, open architecture toolbox. The toolbox consists of different prognostics tools, including neural network-based, time-series based, wavelet-based and hybrid joint time-frequency methods, etc., for predicting the degradation or performance loss on devices, process, and systems. The open architecture of the toolbox allows one easily to add new solutions to the performance assessment modules as well as to easily interchange different tools, depending on the application needs. To enable rapid deployment, a quality function deployment (QFD) based selection method had been developed to provide a general suggestion to aid in tool selection; this is especially critical for those industry users who have little knowledge about these algorithms. The current tools employed in the signal processing and feature extraction, performance assessment, diagnostics and prognostics modules of Watchdog Agent® functionality are summarized in Figure 3.10. Each of these modules is realized in several different ways to facilitate the use of the Watchdog Agent® in a wide variety of products and applications.

Figure 3.10. Watchdog Agent® prognostics toolbox

3.3.2.1 Signal Processing and Feature Extraction Module The signal processing module transforms multiple sensor signals into domains that are the most informative of a product’s performance. Time-series analysis (Pandit and Wu 1993) or frequency domain analysis (Marple 1987) can be used to process stationary signals (signals with time invariant frequency content), while wavelet (Burrus et al. 1998; Yen and Lin 2000), or joint time-frequency analysis (Cohen 1995; Djurdjanovic et al. 2002) could be used to describe non-stationary signals (signals with time-varying frequency content). Most real life signals, such as speech, music, machine tool vibration, acoustic emission etc. are non-stationary

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signals, which place a strong emphasis on the need for development and utilization of non-stationary signal analysis techniques, such as wavelets, or joint timefrequency analysis. The feature extraction module extracts features most relevant to describing a product’s performance. Those features are extracted from the time domain into which the sensory processing module transforms sensory signals, using expert knowledge about the application, or automatic feature selection methods such as roots of the autoregressive time-series model, or time-frequency moments and singular value decomposition. Currently the following signal processing and feature extraction tools are used in the Watchdog Agent® toolbox: •

•

• •

•

The Fourier transformation method has been widely used in de-noising and feature extraction. Noise component in the signal can be distinguished after it is transformed, and feature components can be identified after the removal of noise. However, Fourier transformation is applicable to nonstationary signals only since frequency-band energies for applications are characterized by time-invariant frequency content. The autoregressive modeling method calculates frequency peak locations and intensities using autoregressive oscillation modes of sensor readings and bares significant information about the process (usually, mechanical systems are well described by the modes of oscillations). The wavelet/wavelet packet decomposition method enables the rapid calculation of non-stationary signal energy distribution at the expense of loosing some of the desirable mathematical properties. The time-frequency analysis method provides both temporal and spectral information with good resolution, and is applicable to highly non-stationary signals (e.g. impacts or transient behaviors). However, it is not applicable if a large amount of data has to be considered and calculation speed is a concern. The application specific features extraction method is applicable in cases when one can directly extract performance-relevant features out of the time-series of sensor readings.

3.3.2.2 Performance Assessment Module The performance assessment module evaluates the overlap between the most recently observed signatures and those observed during normal product operation. This overlap is expressed through the so-called confidence value (CV), ranging between zero and one, with higher CVs signifying a high overlap, and hence performance closer to normal (Lee 1995, 1996). In case data associated with some failure mode exist, most recent performance signatures obtained through the signal processing and feature extraction module can be matched against signatures extracted from faulty behavior data as well. The areas of overlap between the most recent behavior and the nominal behavior, as well as the faulty behavior, are continuously transformed into CV over time for evaluating the deviation of the recent behavior from nominal to faulty. Realization of the performance evaluation module depends on the character of the application and extracted performance signatures. If significant application

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expert knowledge exists, simple but rapid performance assessment based on the feature-level fused multi-sensor information can be made using the relative number of activated cells in the neural network, or by using the logistic regression approach. For products with open-control architecture, the match between the current and nominal control inputs and the performance criteria can also be utilized to assess the product’s performance. For more sophisticated applications with intricate and complicated signals and performance signatures, statistical pattern recognition methods, or the feature map based approach can be employed. The following performance assessment tools are currently being used in the Watchdog Agent® toolbox: •

• •

•

•

The logistic regression method allows one to predict a discrete outcome, such as group membership, from a set of variables that may be continuous, discrete, dichotomous, or a mix of any of these. It can quantitatively represent the proximity of current operating conditions to the region of desirable or undesirable behavior. However, it is applicable when a good feature domain description of unacceptable behavior is available. The feature map method assesses the overlap between the normal and most recent process behavior, and is applicable in cases when the Gaussianness of extracted features cannot be guaranteed. The statistical pattern recognition method calculates overlap of feature distributions based on the assumption of Gaussian distribution of the features, and is applicable to a repeatable and stable process. However, it is not applicable to the highly dynamic systems in which feature distribution cannot be approximated as Gaussian The hidden Markov model method is applicable to highly dynamic phenomena when a sequence of process observations rather than a single observation is needed to describe adequately the behavior of process signatures. The particle filters performance assessment is able to describe quantitatively process performance, and is applicable in cases of complex systems that display multiple regimes of operation (both normal and faulty). In this case a hybrid description of the system is needed, incorporating both discrete and continuous states.

3.3.2.3 Diagnostics Module The diagnostics module tells not only the level of behavior degradation (the extent to which the newly arrived signatures belong to the set of signatures describing normal system behavior), but also how close the system behavior is to any of the previously observed faults (overlap between signatures describing the most recent system behavior with those characterizing each of the previously observed faults). This matching allows the Watchdog Agent® to recognize and forecast a specific fault behavior, once a high match with the failure associated signatures is assessed for the current process signatures, or forecasted based on the current and past product’s performance. Figure 3.11 illustrates this signature matching process for performance evaluation.

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Figure 3.11. Performance evaluation using Confidence Value (CV)

•

• •

•

The support vector machine method establishes a non-linear maximum margin classifier that infers the machine condition from a new set of measurements. It works by using a non-linear kernel to transform the input vector space (which is a set of measurements believed to be correlated with machine condition) to a much higher dimension feature space, and drawing a linear hyper-plane classifier there. It is especially applicable to the situation when Gaussianity of the performance related features cannot be guaranteed and when a process may display multiple normal and faulty modes of behavior (multiple regimes of operation and/or multiple possible faults in the process). The main drawback to using this method is that the choice of a kernel in real applications is usually based on experience or trial-and-error test. The hidden Markov model method is especially applicable to a situation in which multiple signals exist and the system may have multiple failure modes. It is applicable to both stationary and non-stationary signals. The Bayesian belief network is a compact representation of cause-and-effect for a complex system, and is especially applicable to situations where there are multiple faults with multiple symptoms. The main drawback of this method is that no standard procedure exists to determine network structure and expert knowledge is needed to identify the node state. Condition diagnosis based on analytically calculated overlaps of Gaussians that describe the signatures corresponding to the current process behavior and the signatures corresponding to various modes of normal or faulty equipment behavior, is applicable to the cases in which performance related features approximately behave as Gaussians.

3.3.2.4 Prediction and Prognostics Module The prediction and prognostics module is aimed at extrapolating the behavior of process signatures over time and predicting their behavior in the future. autoregressive moving average (ARMA) (Pandit and Wu 1993) modeling and match matrix (Liu et al. 2004) methods are used to forecast the performance behavior. Currently, autoregressive moving-average (ARMA) modeling and match matrix methods are used to forecast the performance behavior. Over time, as new

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failure modes occur, performance signatures related to each specific failure mode can be collected and used to teach the Watchdog Agent® to recognize and diagnose those failure modes in the future. Thus, the Watchdog Agent® is envisioned as an intelligent device that utilizes its experience and human supervisory inputs over time to build its own expandable and adjustable world model. Performance assessment, prediction and prognostics can be enhanced through feature-level or decision-level sensor fusion, as defined by Hall and Llinas (2000) (Chapter 2). Feature-level sensor fusion is accomplished through concatenation of features extracted from different sensors, and the joint consideration of the concatenated feature vector in the performance assessment and prediction modules. Decision-level sensor fusion is based on separately assessing and predicting process performance from individual sensor readings and then merging these individual sensor inferences into a multi-sensor assessment and prediction through some averaging technique. In summary, the following performance forecasting tools are currently used in the Watchdog Agent®: •

•

•

•

The autoregressive moving average (ARMA) method is applicable to linear time-invariant systems whose performance features display stationary behavior. ARMA utilizes a small amount of historic data and can provide good short term predictions. The compound match matrix/ARMA prediction method is applicable to cases when abundant records of multiple maintenance cycles exist for nonlinear processes. It excels at dealing with high dimension data and can provide good long term prediction by converting vector-based feature prediction to scalar-based prediction. The fuzzy logic prediction method is applicable to complex systems whose behavior is unknown and no model, function or numerical technique to describe the system is readily available. It utilizes linguistic vagueness or form and allows imprecision, to some extent, in formulating approximations. Fuzzy logic can give fast approximate solutions. The Elman recurrent neural network (ERNN) prediction method is applicable to non-linear systems and can give long term predictions when given a large amount of training data. However, no standard methodology exists to determine ERNN structure, and trial-and-error is usually used in the modeling process.

New tools will be continuously developed and added to the modular, open architecture Watchdog Agent® toolbox based on the development procedure as shown in Figure 3.12.

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Problem definition & constraints

Tool selection

Parameter & tool selection

Prototyping & testing No

Accepted

Program development No

Evaluation

Yes Yes Deployment

Figure 3.12. Flowchart for developing Watchdog Agent® tools

3.3.3 Case Studies Several Watchdog Agent® tools for on-line performance assessment and prediction have already been implemented as stand alone applications in a number of industrial and service facilities. Listed below are several examples to illustrate the developed tools. 3.3.3.1 Example 1: Prognostics of an AS/RS Materials Handling Systems A time-frequency based method (Cohen 1995) has been implemented for performance assessment of a gearbox in an AS/RS material handling system shown in Figure 3.13. Four vibration sensor readings have been fused to evaluate autonomously its performance while it is on-line. The vibration signals were processed into joint time-frequency energy distributions (Cohen 1995) and a set of time-shift invariant time-frequency moments (Zalubas et al. 1996; Djurdjanovic et al. 2000; Tacer and Loughlin 1996) were extracted. Since those moments asymptotically follow a Gaussian distribution (Zalubas et al. 1996), statistical reasoning was utilized to evaluate the overlap between signatures describing normal process behavior (used for training) and those describing the most recent process behavior. Figure 3.14 shows a screenshot of the software application housing this time-frequency based Watchdog Agent® used for performance assessment of a material handling system. The CV was generated by fusing multiple signal features for performance assessment.

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Figure 3.13. Material handling system for mail staging

Figure 3.14. Screenshot of the time-frequency based Watchdog Agent ®

3.3.3.2 Example 2: Roller Bearing Prognostics Testbed Most bearing diagnostics research involves studying the defective bearings recovered from the field or from laboratory experiements where the bearings exhibit mature faults. Experiments using defective bearings have a lower capability for discovering natural defect propagation in its early stages. In order truly to reflect real defect propagation processes, bearing run-to-failure tests were performed under normal load conditions on a specially designed test rig sponsored by Rexnord Technical Service. The bearing test rig hosts four test bearings on one shaft. Shaft rotation speed was kept constant at 2000rpm. A radial load of 6000lbs was added to the shaft and bearing by a spring mechanism. A magnetic plug installed in the oil feedback pipe collected debris from the oil as evidence of bearing degradation. The test stopped when the accumulated debris that adhered to the magnetic plug exceeds a certain level. Four double row bearings were installed on one shaft as shown in Figure 3.15. A high sensitivity accelerometer was installed on each bearing house. Four thermocouples were attached to the outer race of each bearing to record bearing temperature (that is relevant to bearing lubrication condition). Several sets of tests ending with various failure modes were carried out. The time domain feature shows that most of the bearing fatigue time is consumed during the period of material accumulative damage, while the period of crack propagation and development is relatively short. This means that if the traditional threshold-based condition monitoring approach is used, the response time available for the maintenance crew to respond prior to catastrophic failure after a defect is detected in such bearings is very short. A prognostic approach that can detect the defect at an early stage is demanded so that enough buffer time is available for maintenance and logistical scheduling.

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Figure 3.15. The bearing test rig sponsored by Rexnord Technical Service

Figure 3.16 presents the vibration waveform collected from bearing 4 at the last stage of the bearing test. The signal exhibits strong impulses periodicity because of the impacts generated by a mature outer race defect. However, when examining the historical data and observing the vibration signal three days before the bearing failed, there is no sign of periodic impulses as shown in Figure 3.17a. The periodic impulse feature is completely masked by the noise.

Figure 3.16. The vibration signal waveform of a faulty bearing

An adaptive wavelet filter is designed to de-noise the raw signal and enhance degradation detection. The adaptive wavelet filter is yielded in two steps. First the optimal wavelet shape factor is found by the minimal entropy method. Then an optimal scale is identified by maximizing the signal periodicity. By applying the designed wavelet filter to the noisy raw signal, the de-noised signal can be obtained as shown in Figure 3.17b. The periodic impulse feature can then be clearly discovered, which serves as strong evidence of bearing outer race degradation. The wavelet filter-based de-noising method successfully enhanced the signal feature and provided potent evidence for prognostic decision-making.

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a Raw Signal

b De-noised signal using the wavelet filter

Figure 3.17a, b. The vibration waveform with early stage defect

3.3.3.3 Example 3: Bearing Risk of Failure and Remaining Useful Life Prediction An important issue in prognostic technology is the estimation of the risk of failure, and of the remaining useful life of a component, given the component’s age and its past and current operating condition. In numerous cases, failures were attributed to many correlated degradation processes, which could be reflected by multiple degradation features extracted from sensor signals. These features are the major information regarding the health of the component under monitoring; however, the failure boundary is hard to define using these features. In reality, the same feature vector could be attributed to totally different combinations of the underlying degradation processes and their severity levels. There is only a probabilistic relationship between the component failure and the certain level of degradation features. A typical example can be found during bearing operation. Two bearings of the same type could fail at different levels of RMS and Kurtosis of vibration signal. To capture the probabilistic relationship between the multiple degradation features and the component failure as well as to predict the risk of failure and the remaining useful life, IMS has developed a Proportional Hazards (PH) approach (Liao et al. 2005) based on the PH model proposed by Cox (1972). The PH model involving multiple degradation features is given as

λ (t ; Z ) = λ0 (t ) exp( β ' Z )

(3.1)

where λ (t ; Z ) is the hazard rate of the component given the current age t and the degradation feature vector Z ; λ0 (t ) is called the baseline hazard rate function; β is the model parameter vector. This formulation relates the working age and multiple degradation feature to the hazard rate of the component. To estimate the parameters, the maximum likelihood approach could be utilized using offline data, including the degradation features over time of many components and their failure times. Afterwards, the established model can be used for predicting the risk of failure for the component by plugging in the working age and the degradation features extracted from the on-line sensor signals. In addition, the remaining useful life L(tcurrent ) given the current working age and the history of degradation features can be estimated as

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L(tcurrent ) ≈ ∫

∞ t current

⎛ τ exp ⎜ − ∫ ⎝ t

current

⎞ λ (v; zˆ (v)) dv ⎟ dτ ⎠

73

(3.2)

where zˆ (v) is the predicted feature vector. Consider the vibration data obtained from the test rig in Example 2. To facilitate on-line implementation, root-mean-square (RMS) and Kurtosis are calculated and used as degradation features. Figure 3.18 shows the predicted hazard rate over time based on these degradation features. This quantity can be utilized to trigger maintenance when the risk level crosses a predetermined threshold level. Table 3.1 provides the remaining useful life predictions given the current bearing age and the feature observations. The predictions are in accordance with the actual life of the studied bearing ( ≈ 32 days) with minor prediction errors as the degradation progresses.

Figure 3.18. Hazard rate prediction of bearing 3 in Test 1

Table 3.1. Estimates of expected remaining useful life – Test 1, Bearing 3 (unit: day) Time

26

29

31

Estimated expected remaining useful life

3.5549

3.3965

1.5295

True remaining useful life

6.5278

3.5278

1.5278

Error

2.9729

0.1313

0.0017

3.4 Conclusions and Future Research This chapter addresses the paradigm shift in modern maintenance systems from the traditional “fail and fix” practices to a “predict and prevent” methodology. A reconfigurable and scalable Watchdog Agent®-based intelligent maintenance system

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has been developed, which serves as a baseline system for researchers and companies to develop next-generation e-maintenance systems. It enables machine makers and users to predict machine health degradation conditions, diagnose fault sources, and suggest maintenance decisions before a fault actually occurs. The Watchdog Agent®-based R2M-PHM platform expands the OSA-CBM architecture topology by including real-time remote machinery diagnosis and prognosis systems and embedded Watchdog Agent® technology. The Watchdog Agent® is an embedded algorithm toolbox which converts multi-sensory data to machine health information. Innovative sensory processing and autonomous feature extraction methods are developed to facilitate the plug-and-play approach in which the Watchdog Agent® can be setup and run without any need for expert knowledge or intervention. Future work will be the further development of the Watchdog Agent®-based IMS platform. Smart software and NetWare will be further developed for proactive maintenance capabilities such as performance degradation measurement, fault recovery, self-maintenance and remote diagnostics. For the embedded Watchdog Agent® application, we need to harvest the developed technologies and tools and to accelerate their deployment in real-world applications through close collaboration between industrial and academic researchers. Specifically, future work will include the following aspects: (i) evaluate the existing Watchdog Agent® tools and identify the application needs from the smart machine testbed; (ii) develop a configurable prognostics tools platform for rotary machinery elements such as bearings, motors, and gears, etc., so that several of most frequently used prognostics tools can be pretested and deposited into a ready-to-use tool library; (iii) develop a user interface system for tool selection, which allows users to use the right tools effectively for the right applications and achieve “the first tool correct” accuracy; (iv) validate the reconfiguration of these tools to a variety of similar applications (to be defined by the company participants); and (v) explore research in a ‘‘peer-to-peer’’ (P2P) paradigm in which Watchdog Agent®s embedded on identical products operating under similar conditions could exchange information and thus assist each other in machine health diagnosis and prognosis. To predict, prioritize, and plan precision maintenance actions to achieve an “every action correct” objective, the IMS Center is creating advanced maintenance simulation software for maintenance schedule planning and service logistics cost optimization for transparent decision making. At the same time, the Center is exploring the integration of decision support tool and optimization techniques for proactive maintenance; this integration will facilitate the functionalities of the Watchdog Agent®-based R2M-PHM in which an intelligent maintenance systems can operate as a near-zero down-time, self-sustainable and self-aware artificially intelligent system that learns from its own operation and experience. Embedding is crucial for creating an enabling technology that can facilitate proactive maintenance and life cycle assessment for mobile systems, transportation devices and other products for which cost-effective realization of predictive performance assessment capabilities cannot be implemented on general purpose personal computers. The main research challenge will be to accomplish sophisticated performance evaluation and prediction capabilities under the severe power consumption, processing power and data storage limitations imposed by embedding. The Center

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will develop a wireless sensor network made of self-powered wireless motes for machine health monitoring and embedded prognostics. These networked smart motes can be easily installed in products and machines with ad hoc communications. In addition, the Center is investigating the feasibility of harvesting energy by using vibration in an environment equipped with wireless motes for remote monitoring of equipment and machinery. In conjunction with that investigation, the Center is looking at ways of developing communication protocols that require less energy for communication. Power converter circuitry has been designed by using vibration signals in order to convert vibration energy into useful electric energy. These technologies are very critical for monitoring equipment or systems in a complex environment where the availability of power is the major constraint. In the area of collaborative product life cycle design and management, the Watchdog Agent® can serve as an infotronics agent to store product usage and endof-life (EOL) service data and to send feedback to designers and life cycle management systems. Currently, an international intelligent manufacturing systems consortium on product embedded information systems for service and EOL has been proposed. The goal is to integrate Watchdog Agent® capabilities into products and systems for closed-loop design and life cycle management, as illustrated in Figure 3.19.

Figure 3.19. Embedded and tether-free product life cycle monitoring

The Center will continue advancing its research to develop technologies and tools for closed-loop life cycle design for product reliability and serviceability, as well as explore research in new frontier areas such as embedded and networked agents for self-maintenance and self-healing, and self-recovery of products and systems. These new frontier efforts will lead to a fundamental understanding of reconfigurability and allow the closed-loop design of autonomously reconfigurable engineered systems that integrate physical, information, and knowledge domains. These autonomously reconfigurable engineered systems will be able to sense, perform self-prognosis, self-

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diagnose, and reconfigure the system to function uninterruptedly when subject to unplanned failure events, as illustrated in Figure 3.20.

Near Near“0” “0” Downtime

Closed-Loop Life LifeCycle Cycle Design Design Design for Reliability and Serviceability

Product Center

Health Monitoring Product or System Sensors & Embedded In Use Intelligence

Product Redesign

Smart Design

Enhanced Six-Sigma Design

Degradation Watchdog Agent®

Self-Maintenance

Communications

•Redundancy •Active •Passive

•Tether-Free (Bluetooth) • Internet •TCP/IP

Service

• Web-enabled Monitoring & Prognostics • Decision Support Tools for Optimized Maintenance Condition-based

Maintenance • Business and Service Synchronization (CBM) • Asset Optimization

Web-enabled D2B™ Platform (XML-based)

Watchdog Agent and Device-to-Business (D2B) are Trademarks of IMS Center

Figure 3.20. Intelligent maintenance systems and its key elements

3.5 References Badia, F.G., Berrade, M.D. and Campos, C.A., (2002) Optimal Inspection and Preventive Maintenance of Units with Revealed and Unrevealed Failures. Reliability Engineering and System Safety 78: 157–163. Barbera, F., Schneider, H. and Kelle, P., (1996) A Condition Based Maintenance Model with Exponential Failures and Fixed Inspection Interval. Journal of the Operational Research Society 47(8): 1037–1045. Bonissone, G., (1995) Soft computing applications in equipment maintenance and service in: ISIE ’95, Proceedings of the IEEE International Symposium, 2: 10–14. Brotherton, T., Jahns, G., Jacobs, J. and Wroblewski, D., (2000) Prognosis of faults in gas turbine engines, in: Aerospace Conference Proceedings, (2000) IEEE, 6: 18–25. Bruns, P., (2002) Optimal Maintenance Strategies for Systems with Partial Repair Options and without Assuming Bounded Costs. European Journal of Operational Research 139: 146–165. Bunday, B.D., (1991) Statistical Methods in Reliability Theory and Practice, Ellis Horwood. Burrus, C., Gopinath, R. and Haitao, G., (1998) Introduction to wavelets and wavelet transforms – a primer. NJ: Prentice Hall. Casoetto, N., Djurdjanovic, D., Mayor, R., Lee, J. and Ni, J., (2003) Multisensor process performance assessment through the use of autoregressive modeling and feature maps. Trans. of SME/NAMRI, 31:483–490.

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Cavory, G., Dupas, R. and Goncalves, R., (2001) A Genetic Approach to the Scheduling of Preventive Maintenance Tasks on a Single Product Manufacturing Production Line, International Journal of Production Economics, 74: 135–146. Chen, C.T., Chen, Y.W. and Yuan, J., (2003) On a Dynamic Preventive Maintenance Policy for a System under Inspection. Reliability Engineering and System Safety 80: 41–47. Chen, D. and Trivedi, K., (2002) Closed-Form Analytical Results for Condition-Based Maintenance. Reliability Engineering and System Safety 76: 43–51. Cohen, L., (1995) Time-frequency analysis. NJ: Prentice Hall. Cox, D., (1972) Regression models and life tables (with discussion). Journal of the Royal Statistical Society, Series B 34:187–220. Djurdjanovic, D., Widmalm, S.E., William, W.J., et al., (2000) Computerized classification of temporomandibular joint sounds. IEEE Transactions on Biomedical Engineering 47:977–984. Djurdjanovic, D., Ni, J. and Lee, J., (2002) Time-frequency based sensor fusion in the assessment and monitoring of machine performance degradation. Proceedings of 2002 ASME Int. Mechanical Eng. Congress and Exposition, paper number IMECE2002-32032. Garga, A., McClintic, K.T., Campbell, R.L., et al., (2001) Hybrid reasoning for prognostic learning in CBM systems, in: Aerospace Conference, 10–17 March, 2001, IEEE Proceedings, 6: 2957–2969. Goodenow, T., Hardman, W., Karchnak, M., (2000) Acoustic emissions in broadband vibration as an indicator of bearing stress. Proceedings of IEEE Aerospace Conference, 2000; 6: 95–122.L.D. Hall, L.D. and Llinas, J., (Eds.), (2000) Handbook of Sensor Fusion, CRC Press. Hall, L.D., (1992) Mathematical techniques in Multi-Sensor Data Fusion, Artech House Inc. Hansen, R., Hall, D., Kurtz, S., (1994) New approach to the challenge of machinery prognostics. Proceedings of the International Gas Turbine and Aeroengine Congress and Exposition, American Society of Mechanical Engineers, June 13–16 1994: 1–8. IMS, NSF I/UCRC Center for Intelligent Maintenance Systems, www.imscenter.net; 2004. Kemerait, R., (1987) New cepstral approach for prognostic maintenance of cyclic machinery. IEEE SOUTHEASTCON, 1987: 256–262. Kleinbaum, D., (1994) Logistic regression. New York: Springer-Verlag. Labib, A.W., (2006) Next generation maintenance systems: Towards the design of a selfmaintenance machine. 2006 IEEE International Conference on Industrial Informatics, Integrating Manufacturing and Services Systems, 16–18 August, Singapore Lee, J., (1995) Machine performance monitoring and proactive maintenance in computerintegrated manufacturing: review and perspective. International Journal of Computer Integrated Manufacturing 8:370–380. Lee, J., (1996) Measurement of machine performance degradation using a neural network model. Computers in Industry 30:193–209. Lee, J., Ni, J., (2002) Infotronics agent for tether-free prognostics. Proceeding of AAAI Spring Symposium on Information Refinement and Revision for Decision Making: Modeling for Diagnostics, Prognostics, and Prediction. Stanford Univ., Palo Alto, CA, March 25–27. Liang, E., Rodriguez, R., Husseiny, A., (1988) Prognostics/diagnostics of mechanical equipment by neural network, Neural Networks 1 (1) 33. Liao, H., Lin, D., Qiu, H., Banjevic, D., Jardine, A., Lee, J., (2005) A predictive tool for remaining useful life estimation of rotating machinery components. ASME International 20th Biennial Conference on Mechanical Vibration and Noise, Long Beach, CA, 24–28 September, 2005. Liu, J., Djurdjanovic, D., Ni, J., Lee, J., (2004) Performance similarity based method for enhanced prediction of manufacturing process performance. Proceedings of the 2004 ASME International Mechanical Engineering Congress and Exposition (IMECE), 2004.

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Marple, S., (1987) Digital spectral analysis. NJ: Prentice Hall. Marseguerra, M., Zio, E. and Podofillini, L. (2002) Condition-Based Maintenance Optimization by Means of Genetic Algorithm and Monte Carlo Simulation. Reliability Engineering and System Safety 77: 151–166. Mijailovic, V. (2003) Probabilistic Method for Planning of Maintenance Activities of Substation Component. Electric Power System Research 64: 53–58. Pandit, S., Wu, S-M., (1993) Time series and system analysis with application. FL: Krieger Publishing Co. Parker, B.E., Jr., Nigro, T.M., Carley, M.P., et al., (1993) Helicopter gearbox diagnostics and prognostics using vibration signature analysis, in: Proceedings of the SPIE — The International Society for Optical Engineering: 531–542. Pham, H., Suprasad, A. and Misra, R.B. (1997) Availability and Mean Life Time Prediction of Multistage Degraded System with Partial Repairs. Reliability Engineering and System Safety 56: 169–173 Radjou, N., (2002) The collaborative product life-cycle. Forrester Research, May 2002. Ray, A. and Tangirala, S., (1996) Stochastic Modeling of Fatigue Crack Dynamic for OnLine Failure Prognostics, IEEE Transactions on Control Systems Technology, 4(4): 443– 449. Reichard, K., Van Dyke, M. and Maynard, K. (2000) Application of sensor fusion and signal classification techniques in a distributed machinery condition monitoring system. Proceedings of SPIE – The International Society for Optical Engineering 4051:329–336. Roemer, M., Kacprzynski, G. and Orsagh, R., (2001) Assessment of data and knowledge fusion strategies for prognostics and health management. Proceedings of IEEE Aerospace Conference, 2001; 6:62979–62988. Seliger, G., Basdere, B., Keil, T., et al. (2002) Innovative processes and tools for disassembly. Annals of CIRP 51:37–41. Su, L., Nolan M, DeMare G, Carey D. (1999) Prognostics framework ‘for weapon systems health monitoring’. Proceedings of IEEE Systems Readiness Technology Conference, IEEE AUTOTESTCON '99, 30 August–2 September 1999: 661–672. Swanson, D.C., (2001) A General Prognostics tracking algorithm for predictive maintenance, Proc. of the IEEE Aerospace Conference, 2001, 6: 2971–2977. Tacer, B., Loughlin, P., (1996) Time-frequency based classification. SPIE Proceedings 42:2697–2705. Thurston, M. and Lebold, M., (2001) Open Standards for Condition Based Maintenance and Prognostic Systems, Pennsylvania State University, Applied Research Laboratory. Umeda, Y., Tomiyama, T. and Yoshikawa, H., (1995) A design methodology for selfmaintenance machines, ASME journal of mechanical design, 117, September Vachtsevanos, G. and Wang, P., (2001) Fault prognosis using dynamic wavelet neural networks, Proceedings of the IEEE International Symposium on Intelligent Control 2001 (ISIC '01): 79–84. Wang, H.Z., (2002) A Survey of Maintenance Policies of Deteriorating Systems, European Journal of Operationa Research, 139: 469–489. Wilson, B.W., Hansen, N.H., Shepard, C.L., et al. (1999) Development of a modular in-situ oil analysis prognostic system. International Society of Logistics (SOLE) 1999 Symposium, Nevada, Las Vegas, 30 August –2 September. Yamada, A., Takata, S., (2002) Reliability improvement of industrial robots by optimizing operation plans based on deterioration evaluation. Annals of the CIRP 51:319–322. Yen, G., Lin, K., (2000) Wavelet packet feature extraction for vibration monitoring. IEEE Trans. on Industrial Electronics 2000; 47:650–667. Zalubas, E.J., O’Neill, J.C., Williams, W.J., et al., (1996) Shift and scale invariant detection. Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing, 1996; 5:3637–3640.

4 Reliability Centred Maintenance Marvin Rausand and Jørn Vatn

4.1 Introduction Reliability centred maintenance (RCM) is a method for maintenance planning that was developed within the aircraft industry and later adapted to several other industries and military branches. A high number of standards and guidelines have been issued where the RCM methodology is tailored to different application areas, e.g., IEC 60300-3-11, MIL-STD-217, NAVAIR 00-25-403 (NAVAIR 2005), SAE JA 1012 (SAE 2002), USACERL TR 99/41 (USACERL 1999), ABS (2003, 2004), NASA (2000) and DEF-STD 02-45 (DEF 2000). On a generic level, IEC 60300-3-11 (IEC 1999) defines RCM as a “systematic approach for identifying effective and efficient preventive maintenance tasks for items in accordance with a specific set of procedures and for establishing intervals between maintenance tasks.” A major advantage of the RCM analysis process is a structured, and traceable approach to determine the optimal type of preventive maintenance (PM). This is achieved through a detailed analysis of failure modes and failure causes. Although the main objective of RCM is to determine the preventive maintenance, the results from the analysis may also be used in relation to corrective maintenance strategies, spare part optimization, and logistic consideration. In addition, RCM also has an important role in overall system safety management. An RCM analysis process, when properly conducted, should answer the following seven questions: 1. 2. 3. 4. 5. 6. 7.

What are the system functions and the associated performance standards? How can the system fail to fulfil these functions? What can cause a functional failure? What happens when a failure occurs? What might the consequence be when the failure occurs? What can be done to detect and prevent the failure? What should be done when a suitable preventive task cannot be found?

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The main objectives of an RCM analysis process are to: • • •

Identify effective maintenance tasks Evaluate these tasks by some cost–benefit analysis Prepare a plan for carrying out the identified maintenance tasks at optimal intervals

The RCM analysis process is carried out as a sequence of activities. Some of these activities, or steps, overlap in time. The structuring of the RCM process is slightly different in the various standards, guidelines, and textbooks. In this chapter we split the RCM analysis process into the following 12 steps: 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12.

Study preparation System selection and definition Functional failure analysis (FFA) Critical item selection Data collection and analysis Failure modes, effects, and criticality analysis (FMECA) Selection of maintenance actions Determination of maintenance intervals Preventive maintenance comparison analysis Treatment of non-critical items Implementation In-service data collection and updating

The rest of the chapter is structured as follows: In Section 4.2 we describe and discuss the 12 steps of the RCM process. The concepts of generic and local RCM analysis are introduced in Section 4.3. These concepts have been used in a novel RCM approach to improve and speed up the analyses in a railway application. Models and methods for optimization of maintenance intervals are discussed in Section 4.4. Some main features of a new computer tool, OptiRCM, are briefly introduced. Concluding remarks are given in Section 4.5. The RCM analysis approach that is described in this chapter is mainly in accordance with accepted standards, but also contains some novel issues, especially related to steps 6 and 8 and the approach chosen in OptiRCM. The RCM approach is illustrated with examples from railway applications. Simple examples from the offshore oil and gas industry are also mentioned.

4.2 Main Steps of the RCM Analysis Process 4.2.1 Step 1: Study Preparation Before the actual RCM analysis process is initiated, an RCM project group must be established. The group should include at least one person from the maintenance function and one from the operations function, in addition to an RCM specialist. In Step 1 the RCM project group should define and clarify the objectives and the scope of the analysis. Requirements, policies, and acceptance criteria with

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respect to safety and environmental protection should be made visible as boundary conditions for the RCM analysis. The part of the plant to be analyzed is selected in Step 2. The type of consequences to be considered should, however, be discussed and settled on a general basis in Step 1. Possible consequences to be evaluated may comprise: • • • • • •

Human injuries and/or fatalities Negative health effects Environmental damage Loss of system effectiveness (e.g. delays, production loss) Material loss or equipment damage Loss of market shares

All consequence classes cannot usually be measured in a common unit. It is therefore necessary to prioritize between means affecting the various consequence classes. Such a prioritization is not an easy task and will not be discussed in this chapter. The trade-off problems can to some extent be solved within a decision theoretical framework (Vatn et al. 1996). RCM analyses have traditionally concentrated on PM strategies. It is, however, possible to extend the scope of the analysis to cover topics like corrective maintenance strategies, spare part inventories, logistic support problems, and input to safety management. The RCM project group must decide what should be part of the scope and what should be outside. The resources that are available for the analysis are usually limited. The RCM project group should therefore be realistic with respect to what to look into, realizing that analysis cost should not dominate potential benefits. In many RCM applications the plant already has effective maintenance programs. The RCM project will therefore be an upgrade project to identify and select the most effective PM tasks, to recommend new tasks or revisions, and to eliminate ineffective tasks. Further to apply those changes within the existing programs in a way that will allow the most efficient allocation of resources. When applying RCM to an existing PM program, it is best to utilize, to the greatest extent possible, established plant administrative and control procedures in order to maintain the structure and format of the current program. This approach provides at least three additional benefits: • • •

It preserves the effectiveness and successfulness of the current program It facilitates acceptance and implementation of the project’s recommendations when they are processed It allows incorporation of improvements as soon as they are discovered, without the necessity of waiting for major changes to the PM program or analysis of every system

4.2.2 Step 2: System Selection and Definition Before a decision to perform an RCM analysis is taken, two questions should be considered:

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• •

To which systems is an RCM analysis beneficial compared with more traditional maintenance planning? At what level of assembly (plant, system, subsystem) should the analysis be conducted?

All systems may in principle benefit from an RCM analysis. With limited resources we must, however, set priorities, at least when introducing RCM in a new plant. We should start with the systems we assume will benefit most from the analysis. The following criteria may be used to prioritize systems for an RCM analysis: • • •

The failure effects of potential system failures must be significant in terms of safety, environmental consequences, production loss, or maintenance costs The system complexity must be above average Reliability data or operating experience from the actual system, or similar systems, should be available

Most operating plants have developed an assembly hierarchy, i.e. an organization of the system hardware elements into a structure that looks like the root system of a tree. In the offshore oil and gas industry this hierarchy is usually referred to as the tag number system. Several other names are also used. Moubray (1997) refers to the assembly hierarchy as the plant register. In railway infrastructure maintenance it is common to use the disciplinary areas as the next highest level in the plant register. These are typically: • • • • • •

Superstructure Substructure Signalling Telecommunications Power supply (overhead line with supporting systems) Low voltage systems

In this chapter, the following terms are used for the levels of the assembly hierarchy: Plant: A logical grouping of systems that function together to provide an output or product by processing and manipulating various input raw materials and feed stock. An offshore gas production platform may, e.g., be considered as a plant. For railway application a plant might be a maintenance area, where the main function of that “plant” is to ensure satisfactory infrastructure functionality in that area. Moubray (1997) refers to the plant as a cost centre. In railway application a plant corresponds to a train set (rolling stock), or a line (infrastructure). System: A logical grouping of subsystems that will perform a series of key functions, which often can be summarized as one main function, that is required of a plant (e.g., feed water, steam supply, and water injection). The compression system on an offshore gas production platform may, e.g., be considered as a system. Note that the compression system may consist of several compressors with a high degree of redundancy. Redundant units performing the same main function should be included in the same system. It is usually easy to identify the systems in a plant, since they are used as logical building blocks in the design process.

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The system level is usually recommended as the starting point for the RCM process. This is further discussed and justified, e.g., by Smith (1993) and in MILSTD 2173 (MIL-STD 1986). This means that on an offshore oil/gas platform the starting point of the analysis should be the compression system, the water injection system or the fire water system, and not the whole platform. In railway application the systems were defined above as the next highest level in the plant hierarchy. The systems may be further broken down into subsystems, and sub-subsystems, and so on. For the purpose of the RCM analysis process the lowest level of the hierarchy should be what we will call an RCM analysis item. RCM analysis item: A grouping or collection of components, which together form some identifiable package that will perform at least one significant function as a stand-alone item (e.g., pumps, valves, and electric motors). For brevity, an RCM analysis item will in the following be called an analysis item. By this definition, a shutdown valve, e.g., is classified as an analysis item, while the valve actuator is not. The actuator is supporting equipment to the shutdown valve, and only has a function as a part of the valve. The importance of distinguishing the analysis items from their supporting equipment is clearly seen in the FMECA in Step 6. If an analysis item is found to have no significant failure modes, then none of the failure modes or causes of the supporting equipment are important, and therefore do not need to be addressed. Similarly, if an analysis item has only one significant failure mode, then the supporting equipment only needs to be analyzed to determine if there are failure causes that can affect that particular failure mode (Paglia et al. 1991). Therefore, only the failure modes and effects of the analysis items need to be analyzed in the FMECA in Step 6. An analysis item is usually repairable, meaning that it can be repaired without replacing the whole item. In the offshore reliability database OREDA (2002) the analysis item is called an equipment unit. The various analysis items of a system may be at different levels of assembly. On an offshore platform, for example, a huge pump may be defined as an analysis item in the same way as a small gas detector. If we have redundant items, e.g., two parallel pumps; each of them should be classified as analysis items. When in Step 6 we identify causes of analysis item failures, we often find it suitable to attribute this failure causes to failures of items on an even lower level of indenture. The lowest level is usually referred to as components. Component: The lowest level at which equipment can be disassembled without damage or destruction to the items involved. Smith (2005) refers to this lowest level as least replaceable assembly, while OREDA (2002) uses the term maintainable item. It is very important that the analysis items are selected and defined in a clear and unambiguous way in this initial phase of the RCM analysis process, since the following analysis will be based on these analysis items. If the OREDA database is to be used in later phases of the RCM process, it is recommended as far as possible to define the analysis items in compliance with the “equipment units” in OREDA.

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4.2.3 Step 3: Functional Failure Analysis (FFA) The objectives of this step are to: 1. 2. 3.

Identify and describe the systems’ required functions Describe input interfaces required for the system to operate Identify the ways in which the system might fail to function

4.2.3.1 Step 3(i): Identification of System Functions The objective of this step is to identify and describe all the required functions of the system. According to ABS (2004) “each function should be documented as a function statement that contains a verb describing the function, an object on which the function acts, and performance standard(s)”. A function of a shutdown valve may therefore be “close flow of oil within 5 s”. A complex system will usually have a high number of different functions. It is often difficult to identify all these functions without a checklist. The checklist or classification scheme of the various functions presented below may help the analyst in identifying the functions. The same scheme may be used in Step 6 to identify functions of analysis items. The term item is therefore used in the classification scheme to denote either a system or an analysis item: 1.

2.

3.

4. 5.

6.

Essential functions: These are the functions required to fulfil the intended purpose of the item. The essential functions are simply the reasons for installing the item. Often an essential function is reflected in the name of the item. An essential function of a pump is, e.g., to pump a fluid. Auxiliary functions: These are the functions that are required to support the essential functions. The auxiliary functions are usually less obvious than the essential functions, but may in many cases be as important as the essential functions. Failure of an auxiliary function may in many cases be more critical than a failure of an essential function. An auxiliary function of a pump is, e.g., to “contain fluid.” Protective functions: The functions intended to protect people, equipment, and the environment from damage and injury. The protective functions may be classified according to what they protect, as: (i) safety functions, (ii) environment functions, and (iii) hygiene functions. An example of a protective function is the protection provided by a rupture disk on a pressure vessel. Information functions: These functions comprize condition monitoring, various gauges and alarms, and so on. Interface functions: These functions apply to the interfaces between the item in question and other items. The interfaces may be active or passive. A passive interface is, e.g., present when an item is a support or a base for another item. Superfluous functions: According to Moubray (1997) “Items or components are sometimes encountered which are completely superfluous. This usually happens when equipment has been modified frequently over a period of years, or when new equipment has been over-specified”. Superfluous functions are

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sometimes present when the item has been designed for an operational context that is different from the actual operational context. In some cases failures of a superfluous function may cause failure of other functions. For analysis purposes the various functions of an item may also be classified as: •

•

On-line functions: These are functions operated either continuously or so often that the user has current knowledge about their state. The termination of an on-line function is called an evident (or detectable) failure. In relation to safety instrumented systems, on-line functions correspond to high demand systems; see IEC 61508 (IEC 1997). Off-line functions: These are functions that are used intermittently or so infrequently that their availability is not known by the user without some special check or test. The protective functions are very often off-line functions. An example of an off-line function is the essential function of an emergency shutdown (ESD) system on an oil platform. The termination of an off-line function is called a hidden (or undetectable) failure. In the IEC 61508 setting, off-line functions correspond to low demand systems.

Note that this classification of functions should only be used as a checklist to ensure that all relevant functions are revealed. Discussions about whether to classify a function as, e.g., “essential” or “auxiliary” should be avoided. The item may in general have several operational modes (e.g., running, and standby), and several functions related to each operating state. 4.2.3.2 Step 3(ii): Functional Block Diagrams Various types of functional diagrams may represent the system functions identified in Step 3(i). The most common diagram is the so-called functional block diagram. A simple functional block diagram of a diesel engine is shown in Figure 4.1. It is generally not required to establish functional block diagrams for all the system functions. The diagrams are, however, efficient tools to illustrate the input interfaces to a function. In some cases we may want to split system functions into sub-functions on an increasing level of detail, down to functions of analysis items. The functional block diagrams may be used to establish this functional hierarchy in a pictorial manner, illustrating series-parallel relationships, possible feedbacks, and functional interfaces (e.g., see Blanchard and Fabrycky 1998; Rausand and Høyland 2004). Alternatives to the functional block diagram are reliability block diagrams and fault trees. Functional block diagrams are also useful as a basis for the FMECA in Step 6 in the RCM analysis process. 4.2.3.3 Step 3(iii): Functional Failures The next step of the FFA is to identify and describe how the various system functions may fail. A system function may be subject to a set of performance standards (or functional requirements) that may be grouped as physical properties, operational performance properties including output tolerances, and time requirements such as continuous operation or required availability. An unacceptable deviation from one or more of these performance standards is called a functional failure.

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Figure 4.1. Functional block diagram for a diesel engine

The term functional failure is mainly used in the RCM literature, and has the same meaning as the more common term failure mode. In RCM we talk about functional failures on equipment level, and use the term failure mode related to the parts of the equipment. The failure modes will therefore be causes of a functional failure. It is important to realize that a functional failure (and a failure mode) is a manifestation of the failure as seen from the outside, i.e., a deviation from performance standards. Functional failures and failure modes may be classified in three main groups related to the function of the item: • • •

Total loss of function: In this case the function is not achieved at all, or the quality of the function is far beyond what is considered as acceptable. Partial loss of function: This group may be very wide, and may range from the nuisance category almost to the total loss of function. Erroneous function: This means that the item performs an action that was not intended, often the opposite of the intended function.

A variety of classifications schemes for functional failures (failure modes) have been published. Some of these schemes, e.g., Blache and Shrivastava (1994), may be used in combination with the function classification scheme in Step 3(ii) to ensure that all relevant functional failures are identified. The system functional failures may be recorded on a specially designed FFAworksheet that is rather similar to a standard FMECA worksheet. An example of an FFA-worksheet is presented in Figure 4.2 In the first column of Figure 4.2 the various operational modes of the system are recorded. For each operational mode, all the relevant functions of the system are recorded in column 2.

Reliability Centred Maintenance System: Ref. drawing no.: Operational mode

Function

Date: Function requirements

Performed by: Functional failure

87

Page: of: Frequency

Criticality S

E

A

C

Figure 4.2. Example of an FFA-worksheet

The performance requirements to the functions, like target values and acceptable deviations, are listed in column 3. For each function (in column 2) all the relevant functional failures are listed in column 4. In column 5 the frequency/probability of the functional failure is listed. A criticality ranking of each functional failure in that particular operational mode is given is given in column 6. The reason for including the criticality ranking is to be able to limit the extent of the further analysis by disregarding insignificant functional failures. For complex systems such a screening is often very important in order not to waste time and money. The criticality ranking depends on both the frequency/probability of the occurrence of the functional failure, and the severity of the failure. The severity must be judged at plant level. The severity ranking should be given in the four consequence classes: (S) safety of personnel, (E) environmental impact, (A) production availability, and (C) economic losses. For each of these consequence classes the severity should be ranked as for example (H) high, (M) medium, or (L) low. How we should define the borderlines between these classes will depend on the specific application. If at least one of the four entries are (M) medium or (H) high, the severity of the functional should be classified as significant, and the functional failure should be subject to further analysis. The frequency of the functional failure may also be classified in the same three classes. (H) high may, e.g., be defined as more than once per 5 years, and (L) low less than once per 50 years. As above, the specific borderlines will depend on the application. The frequency classes may be used to prioritize between the significant system failure modes. If all the four severity entries of a system failure mode are (L) low, and the frequency is also (L) low, the criticality is classified as insignificant, and the functional failure is disregarded in the further analysis. If, however, the frequency is (M) medium or (H) high the functional failure should be included in the further analysis even if all the severity ranks are (L) low, but with a lower priority than the significant functional failures. The FFA may be rather time-consuming because, for all functional failures, we have to list all the maintenance significant items (MSIs) (see Step 4). The MSI lists will hence have to be repeated several times. To reduce the workload we often conduct a simpler FFA where for each main function we list all functional failures in one column, and all the related MSIs in another column. This is illustrated in Figure 4.3 for a railway application.

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The function name reflects the functions to be carried out on a relatively high level in the system. In principle, we should explicitly formulate the function(s) to be carried out. Instead we often specify the equipment class performing the function. For example, “departure light signal” is specified rather than the more correct formulation “ensure correct departure light signal”. We observe that the last functional failure in Figure 4.3 is not a failure mode for the “correct” functional description (Ensure correct departure light signal), but is related to another function of the “departure light signal”. Thus, if we use an equipment class description rather than an explicit functional statement, the list of failure modes should cover all (implicit) functions of the equipment class. At the functional failure level, it is also convenient to specify whether the failure mode is evident or hidden; see Figure 4.3 where we have introduced an “EF/HF” column. For each function we also list the relevant items that are required to perform the function. These items will form “rows” in the FMECA worksheets; see Step 5. 4.2.4 Step 4: Critical Item Selection The objective of this step is to identify the analysis items that are potentially critical with respect to the functional failures identified in Step 3(iii). These analysis items are denoted functional significant items (FSI). For simple systems the FSIs may be identified without any formal analysis. In many cases it is obvious which analysis items that have influence on the functional failures. For complex systems with an ample degree of redundancy or with buffers, we may need a formal approach to identify the FSIs. If failure rates and other necessary input data are available for the various analysis items, it is usually a straightforward task to calculate the relative importance of the various analysis items based on a fault tree model or a reliability block diagram. A number of importance measures are discussed by Rausand and Høyland (2004). In addition to the FSIs, we should also identify items with high failure rate, high repair costs, low maintainability, long lead-time for spare parts, or items requiring external maintenance personnel. These analysis items are denoted maintenance cost significant items (MCSI). The sum of the functional significant items and the maintenance cost significant items are denoted maintenance significant items (MSI). In an RCM project for the Norwegian Railway Administration the use of generic RCM analyses (see Section 4.3) made it possible to analyze all identified MSIs. In this case this step could be omitted. 4.2.5 Step 5: Data Collection and Analysis The purpose of this step is to establish a basis for both the qualitative analysis (relevant failure modes and failure causes), and the quantitative analysis (reliability parameters such as MTTF, PF-intervals, and so on). The data necessary for the RCM analysis may be categorized into the following three groups:

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Function: _______ Function: “Home signal” Function: “Departure light signal” Description: “Five lamp signals, with three main signals and two pre-signals” Functional failure

- Wrong signal picture - Missing signal picture - Unclear signal picture - Does not prevent contact hazard in case of earth fault - etc.

EF / HF HF HF HF HF

MSI

- Signal mast - Brands - Background shade - Earth conductor - Lamp - Lens - Transformer - etc.

Figure 4.3. Structure of functional failure analysis

Design data: (i) System definition: a description of the system boundaries including all subsystems and equipment to fulfil the main functions of the system, (ii) system breakdown: the assembly hierarchy as described in Step 2, (iii) a technical description of each subsystem, such as the structure of the subsystem, capacity and functions (e.g., input and output), (iv) system performance requirements, e.g., desired system availability, environmental requirements, (v) requirements related to maintenance/testing, e.g., according to rules and regulations. 2. Operational and failure data: (i) Performance requirements, (ii) operating profile (continuous or intermittent operation), (iii) control philosophy (remote/local and automatic/manual), (iv) environmental conditions, (v) maintainability, (vi) calendar- and accumulated operating time for overhauls, (vii) maintenance and downtime costs, (viii) recommended maintenance for each analysis item based on manufacturer specification, general guidelines or standards, or in-house recommended practice, and (ix) failure information, what happens when a failure occurs. 3. Reliability data: Reliability data may be derived from the operational data by statistical analysis. The reliability data is used to decide the criticality, to describe the failure process mathematically and to optimize the time between PM tasks.

1.

During the initial phase of the RCM analysis process it often becomes evident that the format and quality of the operational data are not sufficient to estimate the relevant reliability parameters. Some of the main problems encountered are: • •

The failure data is on a too high level in the assembly hierachy, i.e., data is not reported on the RCM analysis item level (MSI). Failure mode and failure causes are not reported, or the recorded information does not correspond to definitions and code lists used in the FMECA of Step 6.

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• •

For systems being monitored by measurements or visual inspection, the state information is often not reported, making it impossible to establish models for the failure progression. For multiple copies of a component the failure reporting do not link each failure report to a physical unit, but only states that “one of the components has failed and has been replaced.”

When such problems are encountered, it is important to start a process to improve the reporting of operational and failure data. However, there will always be a cost associated with improved reporting due to: (i) the maintenance personnel need to spend more time on reporting, (ii) the maintenance personnel need to be trained in failure reporting, and get insight into the structured FMECA thinking, and (iii) the reporting systems (maintenance management systems) have to be restructured to allow reporting in a format in accordance with the logical structure of the FMECA worksheets. Our experience is that improved reporting quality is unattainable unless maintenance personnel executing the maintenance also participate in the RCM process. This would give ownership to the process, but it is no guarantee that reporting will improve. 4.2.6 Step 6: FMECA The objective of this step is to identify the dominant failure modes of the MSIs identified in Step 4. The information entered into the FMECA worksheet should be sufficient both with respect to maintenance task selection in Step 7, and interval optimization in Step 8. Our FMECA worksheet has more fields than the FMECAs found in most RCM standards. The reason for this is that we use the FMECA as the main database for the RCM analysis. Other RCM approaches often use a rather simple FMECA worksheet, but then have to add an additional FMECA-like worksheet with the data required for optimization of maintenance intervals. TOP

Events Experience has shown that we can significantly reduce the workload of the FMECA by introducing so-called TOP events as a basis for the analysis. The idea is that for each failure mode in the FMECA, a so-called TOP event is specified as consequence of the failure mode. A number of failure modes will typically lead to the same TOP event. A consequence analysis is then carried out for each TOP event to identify the end consequences of that particular TOP event, covering all consequence classes (e.g., safety, availability/punctuality, environmental aspects). For many plants, risk analyses (or safety cases) have been carried out as part of the design process. These may sometimes be used as a basis for the consequence analysis. Figure 4.4 shows a conceptual model of this approach for a railway application where the left part relatively to the TOP event is treated in the FMECA, and the right part is treated as generic, i.e., only once for each TOP event.

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Initiating event

C3

TOP event

“Red bulb failure”

“Train collision”

C4 C5 C6

Failure cause: - Burn-out bulb

Maintenance barrier: Other barriers: - Preventive replacement

- Directional setting “block” - Automatic train protection - Train control centre

Consequence reducing barriers: - Rescue team - Train construction - Fire protection

Figure 4.4. Barrier model for safety

In the rectangle (dashed line) in the left-hand side of Figure 4.4 an “initiating event” and a “barrier” are illustrated. To analyze this “rectangle” we need reliability parameters, such as MTTF, aging parameter, and PF interval, that are included in the FMECA worksheet (e.g., see Rausand and Høyland 2004). Three situations are considered: 1.

There is a failure or a fault situation that is not related to the component we are analyzing with respect to maintenance. If, for example, we are analyzing the automatic train protection (ATP) on the train, the initiating event may be “locomotive driver does not comply with signaling”, and thus the ATP is a barrier against this initiating event. In this situation the function of the ATP is typically a hidden function. 2. There is a potential failure in the component that is being analyzed, and maintenance is a barrier against this failure. An example is a crack that has been initiated in the rail, or in an axle (initiating event); and ultrasonic inspection is a maintenance activity to reveal the crack, and prevent a serious incident. 3. The initiating event is a component aging failure, and preventive maintenance is carried out to reduce the likelihood of this failure. In this situation the initiating event and the first barrier in Figure 4.4 merges to one element. An example is aging failure of a light bulb. The likelihood of such a failure will, however, be reduced if the light bulb is periodically replaced with a new one before the aging effect becomes dominant.

“Other barriers” in Figure 4.4 can prevent the component failure from developing into a critical TOP event. “Track circuit detection” may be a barrier against rail breakage, because the track circuit can detect a broken rail. Typical examples of TOP events in railway application are: • • •

Train derailment Collision train-train Collision train-object

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• • • •

Fire Persons injured or killed in or at the track Persons injured or killed at level crossings Passengers injured or killed at platforms

Several consequence-reducing barriers may also be available. Guide rails may, e.g., be installed to mitigate the consequences in case of derailment. In Figure 4.4 we have indicated that the outcome of the TOP event may be one out of six (end) consequence classes: C1: C2: C3: C4: C5: C6:

Minor injury Medical treatment Permanent injury 1 fatality 2–10 fatalities >10 fatalities

Note that the consequence reducing barriers and the end consequences are not analyzed explicitly during the FMECA, but treated as generic for each TOP event. In the railway situation this means only six analyses of the safety consequences related to human injuries/fatalities. In the following, a list of fields (columns) for the FMECA worksheets is proposed. The structure of the FMECA is hierarchical, but the information is usually presented in a tabular worksheet. The starting point in the FMECA is the functional failures from the FFA in Step 3. Each maintainable item is analyzed with respect to any impact on the various functional failures. In the following we describe the various columns: • • • • • • • •

Failure mode (equipment class level). The first column in the FMECA worksheet is the failure mode at the equipment class level identified in the FFA in Step 3. Maintenance significant item (MSI). The relevant MSI were identified in the FFA. MSI function. For each MSI, the functions of the MSI related to the current equipment class failure mode are identified. Failure mode (MSI level). For the MSI functions we also identify the failure modes at the MSI level. Detection method. The detection method column describes how the MSI failure mode may be detected, e.g., by visual inspection, condition monitoring, or by the central train control system (for railway applications). Hidden or evident. Specify whether the MSI function is hidden or evident. Demand rate for hidden function, fD. For MSI functions that are hidden, the rate of demand of this function should be specified. Failure cause. For each failure mode there is/are one or more failure causes. A failure mode will typically be caused by one or more component failures at a lower level. Note that supporting equipment to the component is considered for the first time at this step. In this context a failure cause may therefore be a failure mode of supporting equipment.

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•

• •

• • •

•

•

• •

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Failure mechanism. For each failure cause, there is one or several failure mechanisms. Examples of failure mechanisms are fatigue, corrosion, and wear. To simplify the analysis, the columns for failure cause and failure mechanism are often merged into one column. Mean time to failure (MTTF). The MTTF when no maintenance is performed should be specified. The MTTF is specified for one component if it is a “point” object, and for a standardized distance if it is a “line” object such as rails, sleepers, and so on. TOP event safety. The TOP event in this context is the accidental event that might be the result of the failure mode. The TOP event is chosen from a predefined list established in the generic analysis Barrier against TOP event safety. This field is used to list barriers that are designed to prevent a failure mode from resulting in the safety TOP event. For example, brands on the signalling pole would help the locomotive driver to recognize the signal in case of a dark lamp. PTE-S. This field is used to assess the probability that the other barriers against the TOP event all fail; see Figure 4.4. PTE-S should count for all the barriers listed under “Barrier against TOP event safety”. TOP event availability/punctuality. Also for this dimension a predefined list of TOP events may be established in the generic analysis. Barrier against TOP event availability/punctuality. This field is used to list barriers that are designed to prevent a failure mode from resulting in an availability/punctuality TOP event. Since the fail safe principle is fundamental in railway operation, there are usually no barriers against the punctuality TOP event when a component fails. An example of a barrier is a two out of three voting system on some critical components within the system. PTE-P. This field is used to assess the probability that the other barriers against an availability/punctuality TOP event all fails. PTE-P should count for all the barriers listed under “Barrier against TOP event availability/ punctuality”. Due to the fail safe principle, PTE-P will often be equal to one. Other consequences. Other consequences may also be listed. Some of these are non-quantitative like noise effects, passenger comfort, and aesthetics. Material damage to rolling stock or components in the infrastructure may also be listed. Material damage may be categorized in terms of monetary value, but this is not pursued here. Mean downtime (MDT). The MDT is the time from a failure occurs until the failure has been corrected and any traffic restrictions have been removed. Criticality indexes. Based on already entered information, different criticality indexes can be calculated. These indexes are used to screen out nonsignificant MSIs.

If a failure mode is considered significant with respect to safety or availability/ punctuality (or other dimensions) a preventive maintenance task should be assigned. In order to do such an assignment, further information has to be specified. This additional information will be completed during Steps 7 and 8. The following fields are recommended:

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•

•

•

•

• •

Failure progression. For each failure cause the failure progression should be described in terms of one of the following categories: (i) gradual observable failure progression, (ii) non-observable and fast observable failure progression (PF model), (iii) non-observable failure progression but with aging effects, and (iv) shock type failures. Gradual failure information. If there is a gradual failure progression information about a what values of the measurable quantity represents a fault state. Further information about the expected time and standard deviation to reach this state should be recorded. PF-interval information. In case of observable failure progression the PF model is often applied (e.g., see Rausand and Høyland 2004, p. 394). The PF concept assumes that a potential failure (P) can be observed some time before the failure (F) occurs. This time interval is denoted the PF interval (e.g., see Rausand and Høyland 2004). We need information both on the expected value and the standard deviation of the PF interval. Aging parameter. For non-observable failure progression aging effects should be described. Relevant categories are strong, moderate or low aging effects. The aging parameter can alternatively be described by a numeric value, i.e., the shape parameter α in the Weibull distribution. Maintenance task. The maintenance task is determined by the RCM logic discussed in Step 7. Maintenance interval. Often we start by describing existing maintenance interval, but after the formalized process of interval optimalization in Step 8 we enter the optimized interval.

An example of an FMECA worksheet is shown in Table 4.1 for a departure light signal. 4.2.7 Step 7: Selection of Maintenance Actions This step is the most novel compared to other maintenance planning techniques. A decision logic is used to guide the analyst through a question–and–answer process. The input to the RCM decision logic is the dominant failure modes from the FMECA in Step 6. The main idea is for each dominant failure mode to decide whether a preventive maintenance task is suitable, or it will be best to let the item deliberately run to failure and afterwards carry out a corrective maintenance task. There are generally three reasons for doing a preventive maintenance task: • • •

Prevent a failure Detect the onset of a failure Reveal a hidden failure

Only the dominant failure modes are subjected to preventive maintenance. To obtain appropriate maintenance tasks, the failure causes or failure mechanisms should be considered.

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Table 4.1. Example of part of an FMECA worksheet System function: Functional failure:

Ensure correct departure light signal No signal Failure mode

event

Safety barriers

PTE-S

event

MSI

Function

Lamp

Give light

No light

Burnt-out filament

Train – Train

Directional block, ATP, TCC, “Black=red”

3 x 10

–4

Manual train operation

Lens

Protect lamp

Broken lens

Rock fall

Train – Train

Directional block, ATP, TCC, “Black=red”

2 x 10

–-5

None

Slip through light

No light slipping through

Fouling

Train – Train

Directional block, ATP, TCC, “Black=red”

2 x 10–4

Failure cause

TOP

TOP

None

The failure mechanisms behind each of the dominant failure modes should be entered into the RCM decision logic to decide which of the following basic maintenance tasks is most applicable: 1. 2. 3. 4. 5. 6.

Continuous on-condition task (CCT) Scheduled on-condition task (SCT) Scheduled overhaul (SOH) Scheduled replacement (SRP) Scheduled function test (SFT) Run to failure (RTF)

Continuous on-condition task (CCT) is a continuous monitoring of an item to find any potential failures. An on-condition task is applicable only if it is possible to detect reduced failure resistance for a specific failure mode from the measurement of some quantity. Scheduled on-condition task (SCT) is a scheduled inspection of an item at regular intervals to find any potential failures. There are three criteria that must be met for an on-condition task to be applicable: 1.

It must be possible to detect reduced failure resistance for a specific failure mode. 2. It must be possible to define a potential failure condition that can be detected by an explicit task. 3. There must be a reasonable consistent age interval between the time of potential failure and the time of failure.

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There are two disadvantage of a scheduled vs. a continuous on-condition task: • •

The man-hour cost of inspection is often larger than the cost of installing a sensor. Since the scheduled inspection is carried out at fixed points of time, one might “miss” situations where the degradation is faster than anticipated.

An advantage of a scheduled on-condition task is that the human operator is then able to “sense” information that a sensor will not be able to detect. This means that traditional “walk around checks” should not be totally skipped even if sensors are installed. Scheduled overhaul (SOH) is a scheduled overhaul of an item at or before some specified age limit, and is often called “hard time maintenance”. An overhaul task can be considered applicable to an item only if the following criteria are met: 1.

There must be an identifiable age at which the item shows a rapid increase in the item’s failure rate function. 2. A large proportion of the units must survive to that age. 3. It must be possible to restore the original failure resistance of the item by reworking it.

Scheduled replacement (SRP) is scheduled discard of an item (or one of its parts) at or before some specified age limit. A scheduled replacement task is applicable only under the following circumstances: 1. 2.

The item must be subject to a critical failure. Test data must show that no failures are expected to occur below the specified life limit. 3. The item must be subject to a failure that has major economic (but not safety) consequences. 4. There must be an identifiable age at which the item shows a rapid increase in the failure rate function. 5. A large proportion of the units must survive to that age.

Scheduled function test (SFT) is a scheduled inspection of a hidden function to identify any failure. A scheduled function test task is applicable to an item under the following conditions: 1.

The item must be subject to a functional failure that is not evident to the operating crew during the performance of normal duties. 2. The item must be one for which no other type of task is applicable and effective. Run to failure (RTF) is a deliberate decision to run to failure because the other tasks are not possible or the economics are less favourable.

Reliability Centred Maintenance

Continuous oncondition task (CCT)

Yes Does a failure alerting measurable indicator exist?

Yes

Is continuous monitoring feasible?

Scheduled oncondition task (SCT)

No

No

Yes Is aging parameter α>1?

Yes

Scheduled overhaul (SOH)

Is overhaul feasible? No

No

Is the function hidden?

97

Yes

Scheduled replacement (SRP)

Scheduled function test (SFT)

No No PM activity found (RTF)

Figure 4.5. Maintenance task assignment/decision logic

In many situations a maintenance task may prevent several failure mechanisms. Hence in some situations it is better to enter failure modes rather than failure mechanisms into the RCM decision logic. Note also that if a failure cause for a dominant failure mode corresponds to supporting equipment, the supporting equipment should be defined as the “item” to be entered into the RCM decision logic. The RCM decision logic is shown in Figure 4.5 Note that this logic is much simpler than that found in most RCM standards and guidelines. It should be emphasized that such logic can never cover all situations. For example, in the situation of a hidden function with aging failures, a combination of scheduled replacements and function tests is required.

4.2.8 Step 8: Determination of Maintenance Intervals Usually, formalized methods for optimization of maintenance interval are not a part of the RCM analysis. In order to optimize maintenance intervals we need to structure the analysis in such a way that it fits into the maintenance optimization models that exist. See Section 4.4 for a discussion of determination of maintenance intervals using optimization models. 4.2.9 Step 9: Preventive Maintenance-Comparison Analysis Two overriding criteria for selecting maintenance tasks are used in RCM. Each task selected must meet two requirements:

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• •

It must be applicable It must be effective

Applicability: Meaning that the task is applicable in relation to our reliability knowledge and in relation to the consequences of failure. If a task is found based on the preceding analysis, it should satisfy the applicability criterion. A PM task is applicable if it can eliminate a failure, or at least reduces the probability of occurrence to an acceptable level (Hoch 1990) — or reduces the impact of failures! Cost-effectiveness: Meaning that the task does not cost more than the failure(s) it is going to prevent. The PM task’s effectiveness is a measure of how well it accomplishes that purpose and if it is worth doing. Clearly, when evaluating the effectiveness of a task, we are balancing the cost of “performing the maintenance with the cost of not performing it. In this context, we may refer to the cost as follows (Hoch 1990): The cost of a PM task may include: • • • • • • •

The risk of maintenance personnel error, e.g., “maintenance introduced failures” The risk of increasing the effect of a failure of another component while one is out of service The use and cost of physical resources The unavailability of physical resources elsewhere while in use on this task Production unavailability during maintenance Unavailability of protective functions during maintenance of these “The more maintenance you do the more risk you expose your maintenance personnel to”

On the other hand, the cost of a failure may include: • • •

The consequences of the failure should it occur (i.e., loss of production, possible violation of laws or regulations, reduction in plant or personnel safety, or damage to other equipment) The consequences of not performing the PM task even if a failure does not occur (i.e., loss of warranty) Increased costs for emergency

4.2.10 Step 10: Treatment of Non-MSIs In Step 4 critical items (MSIs) were selected for further analysis. A remaining question is what to do with the items that are not analyzed. For plants already having a maintenance program it is reasonable to continue this program for the nonMSIs. If a maintenance program is not in effect, maintenance should be carried out according to vendor specifications if they exist, else no maintenance should be performed. See Paglia et al. (1991) for further discussion.

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4.2.11 Step 11: Implementation A necessary basis for implementing the result of the RCM analysis is that the organizational and technical maintenance support functions are available. A major issue is therefore to ensure the availability of the maintenance support functions. The maintenance actions are typically grouped into maintenance packages, each package describing what to do, and when to do it. Many accidents are related to maintenance work. When implementing a maintenance program it is therefore of vital importance to consider the risk associated with the execution of the maintenance work. Checklists may be used to identify potential risk involved with maintenance work: • • • •

Can maintenance people be injured during the maintenance work? Is work permit required for execution of the maintenance work? Are means taken to avoid problems related to re-routing, by-passing, etc.? Can failures be introduced during maintenance work?

Task analysis, e.g., see Kirwan and Ainsworth (1992), may be used to reveal the risk involved with each maintenance job. See Hoch (1990) for further discussion on implementing the RCM analysis results. 4.2.12 Step 12: In-service Data Collection and Updating The reliability data we have access to at the outset of the analysis may be scarce, or even almost none. In our opinion, one of the most significant advantages of RCM is that we systematically analyze and document the basis for our initial decisions and, hence, can better utilize operating experience to adjust that decision as operating experience data is collected. The full benefit of RCM is therefore only achieved when operation and maintenance experience is fed back into the analysis process. The updating process should be concentrated on three major time perspectives: 1. 2. 3.

Short term interval adjustments Medium term task evaluation Long term revision of the initial strategy

For each significant failure that occurs in the system, the failure characteristics should be compared with the FMECA. If the failure was not covered adequately in the FMECA, the relevant part of the RCM analysis should, if necessary, be revised. The short-term update can be considered as a revision of previous analysis results. The input to such an analysis is updated reliability figures either due to more data, or updated data because of reliability trends. This analysis should not require excessive resources, since the framework for the analysis is already established. Only Steps 5 and 8 in the RCM process will be affected by short-term updates. The medium term update will also review the basis for the selection of maintenance actions in Step 7. Analysis of maintenance experience may identify significant failure causes not considered in the initial analysis, requiring an updated FMECA in Step 6.

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The long-term revision will consider all steps in the analysis. It is not sufficient to consider only the system being analyzed; it is required to consider the entire plant with its relations to the outside world, e.g., contractual considerations, new laws regulating environmental protection, and so on.

4.3 Generic and Local RCM Analyses An RCM analysis should be conducted for physical units in a stated operational context. Assume that we are planning to carry out an RCM analysis of a specific railway point1 (turnout) at location X on line Y. For this railway point we identify all functions, failure modes, and so on. We then propose a set of maintenance tasks, and finally we choose the maintenance intervals based on reliability parameters for the railway point, punctuality parameters, and personnel risk. Now, there might be several hundreds of similar railway points, with slightly varying reliability performance and risk profiles that would require different maintenance intervals. To avoid repeating the entire RCM analysis for all these railway points, we propose to conduct a generic RCM analysis, and then make local adjustments with regard to reliability and risk parameters. The following steps are then required: 1.

2.

3.

4.

5.

1

Conduct a generic RCM analysis for selected components. In this analysis we use generic (average) values of reliability and risk parameters (regarding punctuality and personnel risk). Establish a generic RCM database. The results from generic RCM analyses of selected equipment types are stored in a generic RCM database. In a first phase we may restrict ourselves to consider broad classes of typical railway points. In a later phase, we may want to refine our analysis to cover specific types and brands of railway points (with different failure modes). Select local analysis objects. In the local analysis we work with a subset of the railway system. This can, for example, be a specific railway point, railway points in the main track of a specific line, and so on. Find an appropriate generic RCM template. For a local analysis object, we now recall the corresponding generic RCM analysis from the RCM database. We first verify that the generic RCM analysis object (template) is appropriate in terms of qualitative properties, with respect to functions, failure modes, and so. At this point it might be necessary to add more functions, failure modes, etc. In this case, we add the “new” RCM object to the generic RCM database in order to make the generic RCM database more comprehensive. Adjust parameters. At the local level we identify differences from the parameters used in the generic RCM database. A specific line may, for example, have very old railway points that may cause the MTTF to be smaller than the average MTTF. In this step of the procedure we have to

A railway point is a railway “switch” that allows a train to go from one track to another. A railway point is called a “turnout” in American English.

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consider all parameters that are involved in the optimization model (see Section 4.4. 6. Re-run the optimization procedure. Based on the new “local” parameters we next re-run the optimization procedure to adjust maintenance intervals taking local differences into account. To carry out this process we need a computerized tool to streamline the work. 7. Document the results. The results from the local analysis are stored in a local RCM database. This is a database where only the adjustment factors are documented, for example, for railway points A, B, C, and D on line Y the MTTF is 30 % higher than the average. Hence the maintenance interval is also reduced accordingly.

4.4 Modelling and Optimizing Maintenance Intervals A wide range of general models and methods for maintenance optimization have been proposed, e.g., see Rausand and Høyland (2004), Pierskalla and Voelker (1979), Valdez-Florez and Feldman (1989), Cho and Parlar (1991), Gertsbakh (2000) and Wang (2002). A high number of models and methods for specific applications have also been developed, e.g., see Vatn and Svee (2002), Chang (2005), Castanier and Rausand (2006), and Welte et al. (2006). In this section we present basic elements required to optimize maintenance interval (τ ) , and a standard procedure for setting up the cost function, C(τ ) , is proposed. A computerized tool called OptiRCM has been developed by the authors to support the RCM procedure presented in this chapter. OptiRCM is currently being used by the Norwegian National Railway (NSB). The Norwegian National Rail Administration (JBV) has also adopted the same procedure. OptiRCM imports the FMECA results generated by Steps 6 and 7 of the RCM analysis process. Cost information is usually not available in the FMECA; hence information about preventive and corrective maintenance costs must be provided separately. A screen presenting the information on the MSI level is shown in Figure 4.6. OptiRCM uses a procedure with three steps to optimize maintenance intervals: (i) the component performance is established (left-hand part of Figure 4.6), (ii) the system model is established (centre part of Figure 4.6), and (iii) the total cost if calculated (right-hand part of Figure 4.6). 4.4.1 Component Model The aim of the component model is to establish the effective failure rate with respect to a specific failure mode, λE (τ ) , as a function of the maintenance interval τ . The effective failure rate is the unconditional expected number of failures per time unit for a given maintenance level. Typically, the effective failure rate is an increasing function of τ . A large number of models for determining the effective failure rate as a function of the maintenance strategies, the degradation models, and so on, have been proposed in the literature.

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Figure 4.6. OptiRCM input and analysis screen

The interpretation of the effective failure rate is not straightforward for hidden functions. For such functions we also need to specify the rate at which the hidden function is demanded. In this situation we may approximate the effective failure rate by the product of the demand rate and the probability of failure on demand (PFD) for the hidden function. In the following we indicate models that may be used for modelling the effective failure rate, and we refer to the literature for details. The aim of OptiRCM has been: • • •

To cover the standard situations, both with respect to evident/hidden failures, but also with respect to the type of failure progression. Provide formulae that do not require too many reliability parameters to be specified. Limit the number of probabilistic models as a basis for the optimization.

Only the Weibull distribution is used to model aging failures in OptiRCM. There may, of course, be situations where another distribution would be more realistic, but our experience is that the user of such a tool rarely has data or insight that helps him to do better than applying the Weibull model. 4.4.1.1 Effective Failure Rate in the Situation of Aging A standard block replacement policy is considered where an aging component is periodically replaced after intervals of length τ . Upon a failure in one interval, the component is replaced without affecting the next planned replacement. The effective failure rate, i.e., the average number of failures per time unit is then given by λE (τ ) = W (τ ) / τ , where W (τ ) is the renewal function (e.g., see Rausand and Høyland 2004). Approximation formulas for the effective failure rate exist if we assume Weibull distributed failure times (e.g., see Chang et al. 2006). OptiRCM

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uses the renewal equation to establish an iterative scheme for the effective failure rate based on an initial approximation. 4.4.1.2 Effective Failure Rate in the Situation of Gradual Observable Failure Progression The assumptions behind this situation is that the failure progression, say Y (t) , can be observed as a function of time. In the simplest situation Y (t) is onedimensional, whereas in more complex situations Y (t) may be multidimensional. We may also have situations where Y (t) denotes some kind of a signal where, for example, the fast Fourier transform of the signal is available. In OptiRCM a very simple situation is considered, where Y (t) is monotonically increasing. As Y (t) increases, the probability of failure also increases, and at a predefined level (maintenance limit), say l , the component is replaced, or overhauled. The effective failure rate, λE (τ, l) , is now a function of both the inspection interval, and the maintenance limit. In OptiRCM a Markov chain model is used to model the failure progression (e.g., see Welte et al. 2006 for details of the Markov chain modelling, and also an extension where it is possible to reduce the inspection intervals as we approach the maintenance limit). In the Markov chain model it is easy to treat the situation where Y (t) is a nonlinear function of time. If we restrict ourselves to linear failure progression, continuous models as the Wiener and gamma processes may also be used. 4.4.1.3 Effective Failure Rate in the PF Model The assumption behind the PF model is that failure progression is not observable for a rather long time, and then at some point of time we have a rather fast failure progression. This is the typical situation for cracks (potential failures) that can be initiated after a large number of load cycles. The cracks may develop rather fast, and it is important to detect the cracks before they develop into breakages. The time from a crack is observable until a failure (breakage) occurs is denoted the PF-interval. The important reliability parameters are the rate of potential failures, the mean and standard deviation of the PF-interval, and the coverage of the inspection method. The model implemented in OptiRCM for the PF situation is described in Vatn and Svee (2002). See also Castanier and Rausand (2006) for a similar approach, and the more general application of delay time models (Christer and Waller 1984). 4.4.2 System Model Figure 4.4 shows a simplified model of the risk picture related to the component failure being analyzed. In order to quantify the risk related to safety, we need the following input data: • • •

The effective failure rate, λE (τ ) The probability that the other barriers against the TOP event with respect to safety all fail, PTE−S The probability that the TOP event results in consequence C j is PC j for j running through the number of consequence classes.

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PLLj = PLL-contribution

SCj = Cost (Euro)

C1: Minor injury

0.01

2,000

C2: Medical treatment

0.05

30,000

C3: Permanent injury

0.1

300,000

C4: 1 fatality

0.7

1,600,000

C5: 2–10 fatalities

4.5

13,000,000

C6: >10 fatalities

30

160,000,000

The frequency of the consequence class C j is given by Fj = λE (τ ) ⋅ PTE−S ⋅ PC j

(4.1)

where PCj is the probability that the TOP event results in consequence class C j . We will later indicate how we can model Equation 4.1 as a function of the maintenance interval, τ . In some situations we also assign a cost, and/or a PLL (potential loss of life) contribution to the various cost elements. PLL denotes the annual, statistically expected number of fatalities in a specified population. Proposed values adopted by the Norwegian National Rail Administration are given in Table 4.2. Please see discussion by Vatn (1998) regarding what it means to assign monetary values to safety. The total PLL contribution related to the component failure being analyzed is then PLL = PTE−S ⋅ ∑ j=1 (PC j ⋅ PLL j ) ⋅ λE (τ ) 6

(4.2)

and the total cost contribution related to the component is CS = PTE−S ⋅ ∑ j=1 (PC j ⋅ SC j ) ⋅ λE (τ ) 6

(4.3)

where SCj is safety cost of consequence class C j . Note that in the FMECA analysis we can have an automatic procedure that calculates both the PLL contribution and the safety cost contribution based on the reliability parameters, and the type of TOP event. In the same way as we have done for safety consequences, we proceed with punctuality or unavailability costs. Here we simplify, and assume that there exists a fixed (expected) cost for each TOP event for punctuality, say PC(TOP). The punctuality cost per time unit is then

Reliability Centred Maintenance

CP = PTE-P ⋅ PC (TOP) ⋅ λE (τ )

105

(4.4)

This procedure may, if required, be repeated for other dimensions like environment, material damage, and so on. 4.4.3 Total Cost and Interval Optimization The approach to interval optimization is based on minimizing the total cost related to safety, punctuality, availability, material damage, etc. Within an ALARP regime (e.g., see Vatn 1998) this requires that the risk is not unacceptable. Assuming that risk is acceptable, we proceed by calculating the total cost per time unit: C(τ ) = CS (τ ) + CP (τ ) + CPM (τ ) + CCM (τ )

(4.5)

where CS (τ ) and CP (τ ) are given by Equation 4.3 and 4.4, respectively. Further, CPM (τ ) = PM Cost / τ

(4.6)

where PM Cost is the cost per preventive maintenance activity. Note that for condition-based tasks we distinguish between the cost of monitoring the item, and the cost of physically improving the item by some restoration or renewal activity. This complicates Equation 4.6 slightly because we have to calculate the average number of renewals. Further, if CM Cost is the cost of a corrective maintenance activity, we have CCM (τ ) = CM Cost ⋅ λE (τ )

(4.7)

Table 4.3. Generic probabilities, PCj, of consequence class Ci for the different TOP events event

PC1

PC2

PC3

PC4

PC5

PC6

Derailment

0.1

0.1

0.1

0.1

0.05

0.01

Collision train-train

0.02

0.03

0.05

0.5

0.3

0.1

Collision train-object

0.1

0.2

0.3

0.15

0.01

0.001

Fire

0.1

0.2

0.2

0.1

0.02

0.005

Passengers injured or killed at platforms

0.3

0.3

0.2

0.05

0.01

0.001

Persons injured or killed at level crossings

0.1

0.2

0.3

0.3

0.09

0.01

Persons injured or killed in or at the track

0.2

0.2

0.2

0.3

0.1

0.0001

TOP

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To find the optimum maintenance interval we can now calculate C(τ ) in Equation 4.5 for various values of the maintenance interval, τ , and then choose the τ value that minimizes C(τ ) . As a numerical example we consider a pump used for oil cooling of the main high voltage transformer in a locomotive. The relevant figures in the example are assessed by experts in the Norwegian National Railway. Upon failure of the oil pump, the TOP event for punctuality will most likely be a FULL STOP with a probability, PTE−P = 0.75 for this punctuality consequence. It is considered that a full stop gives an average delay of 15 min, and the cost of 1 min delay is set to 150 Euros. The potential TOP event for safety is a FIRE, but the likelihood is very small, i.e. PTE−S = 0.0005 . The reliability parameters of the pump are for the aging parameter α = 3.5 , and for the mean time to failure without any preventive maintenance we set to MTTF = 10 million km. To calculate the safety cost we find ∑ j (PC j ⋅ SC j ) = 1.286 million Euros by combining Table 4.2 and 4.3. Equation 4.3 thus reads CS (τ ) = 643 ⋅ λE (τ ) . Punctuality cost in Equation 4.4 is similarly given by CS (τ ) = 2250 ⋅ λE (τ ) . For PM and CM cost we have PM Cost = 3100 Euros, and CM Cost = 4400 Euros, respectively. Chang et al. (2006) argue that a good approximation for the effective failure rate is α

2 (0.09α − 0.2)τ ⎤ ⎛ Γ(1 + 1/ α ) ⎞ α −1 ⎡ 0.1ατ λE (τ ) = ⎜ + ⎥ ⎟ τ ⎢1 − 2 MTTF ⎝ MTTF ⎠ ⎣ MTTF ⎦

(4.8)

The total cost C(τ ) in Equation 4.5 can now be found as a function of τ ; see Figure 4.7 for a graphical illustration. The optimum interval is found to be 7.5 million km. The maintenance action is scheduled replacement of the pump; see Figure 4.5.

Figure 4.7. Cost elements as a function of the maintenance interval

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4.5 Conclusions The main parts of the RCM approach that we have described in this chapter are compatible with common practice and with most of the RCM standards. We are, however, using a more complex FMECA where we also record data that are necessary during maintenance interval optimization. The novel parts of our approach are related to the use of so-called generic RCM analysis and to maintenance interval optimization. The use of generic RCM analysis will significantly reduce the workload of a complete RCM analysis. Maintenance optimization is, generally, a very complex task, and only a brief introduction is presented in this chapter. For maintenance personnel to be able to use the proposed methods, they need to have access to simple computerized tools where the mathematically complex methods are hidden. This was our objective in developing the OptiRCM tool. Maintenance optimization modules are, more or less, non-existent in the standard RCM tool. OptiRCM is not a replacement for these tools, but rather a supplement. OptiRCM is still in the development stage, and we are currently trying to implement several new features into OptiRCM. Among these are additional methods related to maintenance strategies, and grouping of maintenance tasks.

4.6 References ABS, (2003) Guide for Survey Based on Reliability-Centered Maintenance. American Bureau of Shipping, Houston. ABS, (2004) Guidance Notes on Reliaility-Centered Maintenance. American Bureau of Shipping, Houston. Blanchard BS, Fabrychy WJ, (1998) Systems Engineering and Analysis, 3rd ed. Prentice Hall, Englewood Cliffs, NJ. Blanche KM, Shrivastava AB, (1994) Defining failure of manufacturing machinery and equipment. Proceedings from the Annual Reliability and Maintainability Symposium, pp. 69–75. Castanier B, Rausand M, (2006) Maintenance optimization for subsea oil pipelines. Pressure Vessels and Piping 83:236–243. Chang KP, (2005) Reliability-centered maintenance for LNG ships. ROSS report 200506, NTNU, Trondheim, Norway. Chang KP, Rausand M, Vatn J, (2006) Reliability Assessment of Reliquefaction Systems on LNG Carriers. Submitted for publication in Reliability Engineering and System Safety. Cho DI, Parlar M, (1991) A survey of maintenance models for multi-unit systems. European Journal of Operational Research 51:1–23. Christer AH, Waller WM, (1984) Delay time models of industrial inspection maintenance problems, Journal of the Operational Research Society 35:401–406. DEF-STD 02-45 (NES 45), (2000) Requirements for the application of reliability-centred maintenance technique to HM ships, submarines, Royal fleet auxiliaries and other naval aixiliary vessels. Defense Standard, U.K. Ministry of Defence, Bath, England. Gertsbakh I, (2000) Reliability Theory with Applications to Preventive Maintenance. Springer, New York. Hoch R, (1990) A practical application of reliability centered maintenance. the American Society of Mechanical Engineers, 90-JPGC/Pwr-51, Joint ASME/IEEE Power Generation Conference, Boston, MA, 21–25 October.

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IEC60300-3-11, (1999) Dependability management – Application guide – Reliability centered maintenance. International Electrotechnical Commission, Geneva. IEC61508, (1997) Functional safey of electrical/electronic/programmable electronic safetyrelated systems, Part 1–7. International Electrotechnical Commission, Geneva. Kirwan B, Ainsworth LK, (1992) A Guide to Task Analysis. Taylor and Francis, London. MIL-STD-2173 (AS), (1986) Reliability-Centered Maintenance. Requirements for Naval Aircraft, Weapon Systems and Support Equipment. U.S. Department of Defense, Washington, DC. Moubray J, (1997) Reliability-centered Maintenance II, 2nd ed. Industrial Press, New York. NASA, (2000) Reliability Centered Maintenance Guide for Facilities and Collateral Equipment. NASA Office of Safety and Mission Assurance, Washington DC. NAVAIR 00-25-403, (2005) Guidelines for the naval aviation reliability-centered maintenance process. Naval Air Systems Command, U.S.A. OREDA, (2002) Offshore Reliability Data, 4th ed. OREDA Participants. Available from Det Norske Veritas, NO-1322 Høvik, Norway. Paglia A, Barnard D, Sonnett D, (1991) A case study of the RCM project at V.C. Summer Nuclear Generating Station. 4th International Power Generation Exhibition and Conference, Tampa, Florida, USA. 5:1003–1013. Pierskalla WP, Voelker JA, (1979) A survey of maintenance models: The control and surveillance of deteriorating systems. Naval Research Logistics Quarterly 23:353–388. Rausand M, Høyland A, (2004) System Reliability Theory; Models, Statistical Methods, and Applications. Wiley, New York. SAE JA1012, (2002) A Guide to the Reliability-Centered Maintenance (RCM) Standard. The Egineering Society for Advancing Mobility Land, Sea, Air, and Space, Warrendale, PA. Smith AM, (1993) Reliability-Centered Maintenance. McGraw-Hill, New York. Smith DJ, (2005) Reliability, maintainability and Risk; Practical Methods for Engineers icluding Reliability Centred Maintenance and Safety-Related Systems. Elsevier, Butterworth Heinemann, Amsterdam. USACERL TR 99/41 (1999) Reliability centered maintenance (RCM) guide. Operating a more effective maintenance program. U.S. Army Corps of Engineers. Valdez-Flores C, Feldman RM, (1989) A survey of preventive maintenance models for stochastically deterioratingsingl-unit systems. Naval Research Logistics 36:419–446. Vatn J, Hokstad P, Bodsberg L, (1996) An overall model for maintenance optimization. Reliability Engineering and System Safety 51:241–257. Vatn J, (1998) A discussion of the acceptable risk problem. Reliability Engineering and System Safety 61:11–19. Vatn J, Svee H, (2002) A risk based approach to determine ultrasonic inspection frequencies in railway applications. ESReDA Conference, Madrid, 27–28 May. Wang H, (2002) A survey of maintenance policies of deteriorating systems. European Journal of Operational Research 139:469–489. Welte T, Vatn J, Heggset J, (2006) Markov state model for optimization of maintenance and renewal of hydro power components. 9th International Conference on Probabilistic Methods Applied to Power Systems, KTH, Stockholm, 11–15 June 2006.

Part C

Methods and Techniques

5 Condition-based Maintenance Modelling Wenbin Wang

5.1 Introduction The use of condition monitoring techniques in industry to direct maintenance actions has increased rapidly over recent years to the extent that it has marked the beginning of what is likely to prove a new generation in production and maintenance management practice. There are both economic and technological reasons for this development driven by tight profit margins, high outage costs and an increase in plant complexity and automation. Technical advances in condition monitoring techniques have provided a means to achieve high availability and to reduce scheduled and unscheduled production shutdowns. In all cases, the measured condition information does, in addition to potentially improving decision making, have a value added role for a manager in that there is now a more objective means of explaining actions if challenged. In November 1979, the consultants, Michael Neal & Associate Ltd published ‘A Guide to Condition Monitoring of Machinery’ for the UK Department of Trade and Industry; Neal et al. (1979). This groundbreaking report illustrated the difference in maintenance strategies (e.g., breakdown, planned, etc.) and suggested that condition based maintenance, using a range of techniques, would offer significant benefits to industry. By the late 1990s condition based maintenance had become widely accepted as one of the drivers to reduce maintenance costs and increase plant availability. With the advent of e-procurement, business to business (B2B), customer to business (C2B), business to customer (B2C) etc., industry is fast moving towards enterprise wide information systems associated with the internet. Today, plant asset management is the integration of computerised maintenance management systems and condition monitoring in order to fulfil the business objectives. This enables significant production benefits through objective maintenance prediction and scheduling. This positions the manufacturer to remain competitive in a dynamic market. Today there exists a large and growing variety of condition monitoring techniques for machine condition monitoring and fault diagnosis. A particularly popular

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one for rotating and reciprocal machinery is vibration analysis. However, irrespective of the particular condition monitoring technique used, the working principle of condition monitoring is the same, namely condition data become available which need to be interpreted and appropriate actions taken accordingly. There are generally two stages in condition based maintenance. The first stage is related to condition monitoring data acquisition and their technical interpretations. There have been numerous papers contributing to this stage, as evidenced by the proceedings of COMADEM over recent years. This stage is characterised by engineering skill, knowledge and experience. Much effort of the study at this stage has gone into determining the appropriate variables to monitor, Chen et al. (1994), the design of systems for condition monitoring data acquisition, Drake et al. (1995), signal processing, Wong et al. (2006), Samanta et al. (2006), Harrison (1995), Li and Li (1995), and how to implement computerised condition monitoring, Meher-Homji et al. (1994). These are just a few examples and no modelling is explicitly entered into the maintenance decision process based upon the results of condition monitoring. For detailed technical aspects of condition monitoring and fault diagnosis, see Collacott (1997). The second stage is maintenance decision making, namely what to do now given that condition information data and their interpretations are available. The decision at this stage can be complicated and entails consideration of cost, downtime, production demand, preventive maintenance shutdown windows, and most importantly, the likely survival time of the item monitored. Compared with the extensive literature on condition monitoring techniques and their applications, relatively little attention has been paid to the important problem of modelling appropriate decision making in condition based maintenance. This chapter focuses on the second stage of condition monitoring, namely condition based maintenance modeling as an aid to effective decision making. In particular, we will highlight a modelling technique used recently in condition based maintenance, e.g. residual life modelling via stochastic filtering (Wang and Christer 2000). This is a key element in modeling the decision making aspect of condition based maintenance. The chapter is organised as follows. Section 5.2 gives a brief introduction to condition monitoring techniques. Section 5.3 focuses on condition based maintenance modeling and discuss various modeling techniques used. Section 5.4 presents the modelling of the residual life conditional on observed monitoring information using stochastic filtering. Section 5.5 concludes the chapter with a discussion of topics for future research.

5.2 Condition Monitoring Techniques For many years condition monitoring has been defined as “The assessment on a continuous or periodic basis of the mechanical and electrical condition of machinery, equipment and systems from the observation and/or recordings of selected measurement parameters” (Collacott 1997). One of the obvious analogies is the temperature measurement of a human body where the observation is the temperature and the system is the human body. Just as doctors strongly recommend periodic checks of key health parameters such as blood pressure, pulse, weight and/or temperature for an early indication of potential health problems, for

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industrial equipment some measurements can be taken and the likely condition of the plant assessed. Today there exists a large and growing variety of forms of condition monitoring techniques for machine condition monitoring and fault diagnosis. Understanding the nature of each monitoring technique and the type information measured will certainly help us when establishing a decision model. Here we briefly introduce five main techniques and among them, vibration and oil analysis techniques are the two most popular. 5.2.1 Vibration Based Monitoring Vibration based monitoring is the main stream of current applications of condition monitoring in industry. Vibration based monitoring is an on (off) line technique used to detect system malfunction based on measured vibration signals. Generally speaking, vibration is the variation with time of the magnitude of a quantity that is descriptive of the motion or position of a mechanical system, when the magnitude is alternatively greater than and smaller than some average value or reference. Vibration monitoring consists essentially in identifying two quantities: • •

The magnitude (overall level) of the vibration The frequency content (and/or time waveform)

The magnitude is basically used for establishing the severity of the vibration and the frequency content for the cause or origin. Vibration velocity has been seen as the most meaningful magnitude criterion for assessing machine condition, though displacement or acceleration is also used. The magnitude of vibration is usually measured in root mean square (rms). If T denotes the period of vibration and V (t ) is the vibration (say, velocity) measured at time t, then Vrms =

1 T

∫

T 0

(V (t )) 2 dt ,

which is proportional to the energy of vibration (Reeves 1998). However, since vibration signals from machines are, in general, periodic in nature, a great deal of information is contained in its frequency spectrum form. The frequency spectrum is usually obtained digitally using a digital analyser or computer via a mathematical algorithm known as “fast fourier transform” (FFT). The spectrum analysis of vibration signals is commonly used in the fault diagnosis of rotating machines. Potentially, all machines can benefit from vibration monitoring except, perhaps, those running at very low speed (below about 20 rev/min), and those where isolation (or damping) occurs between the source and the sensor. From observed vibration signals we often see a typical two-stage process where the signals may stay flat over the normal operation period and then display some increasing trend when a defect has initiated (Wang 2002). Another factor coming into play when establishing a vibration based maintenance model is the casual relationship between the measured signals and the state of the plant. It is the defect

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which causes the abnormal signals, but not vice versa (Wang 2002). This factor plays an important role when selecting an appropriate model for describing such a relationship. 5.2.2 Oil Based Monitoring A detailed analysis of a sample of engine, transmission and hydraulic oils is a valuable preventive maintenance tool for machines. In many cases it enables the identification of potential problems before a major repair is necessary, has the potential to reduce the frequency of oil changes, and increase the resale value of used equipment. Oil based monitoring involves sampling and analyzing oil for various properties and materials to monitor wear and contamination in an engine, transmission or hydraulic system etc. Sampling and analyzing on a regular basis establishes a baseline of normal wear and can help indicate when abnormal wear or contamination is occurring. Oil analysis works as follows. Oil that has been inside any moving mechanical apparatus for a period of time reflects the possible condition of that assembly. Oil is in contact with engine or mechanical components as wear metallic trace particles enter the oil. These particles are so small they remain in suspension. M