Computational Intelligence in Fault Diagnosis PDF
Preview Computational Intelligence in Fault Diagnosis
Advanced Information and Knowledge Processing Series Editors Professor Lakhmi Jain [email protected] Professor Xindong Wu [email protected] Also in this series Gregoris Mentzas, Dimitris Apostolou, Andreas Abecker and Ron Young Knowledge Asset Management 1-85233-583-1 Michalis Vazirgiannis, Maria Halkidi and Dimitrios Gunopulos Uncertainty Handling and Quality Assessment in Data Mining 1-85233-655-2 Asunción Gómez-Pérez, Mariano Fernández-López and Oscar Corcho Ontological Engineering 1-85233-551-3 Arno Scharl (Ed.) Environmental Online Communication 1-85233-783-4 Shichao Zhang, Chengqi Zhang and Xindong Wu Knowledge Discovery in Multiple Databases 1-85233-703-6 Jason T.L. Wang, Mohammed J. Zaki, Hannu T.T. Toivonen and Dennis Shasha (Eds) Data Mining in Bioinformatics 1-85233-671-4 C.C. Ko, Ben M. Chen and Jianping Chen Creating Web-based Laboratories 1-85233-837-7 Manuel Graña, Richard Duro, Alicia d’Anjou and Paul P. Wang (Eds) Information Processing with Evolutionary Algorithms 1-85233-886-0 Colin Fyfe Hebbian Learning and Negative Feedback Networks 1-85233-883-0 Yun-Heh Chen-Burger and Dave Robertson Automating Business Modelling 1-85233-835-0 Dirk Husmeier, Richard Dybowski and Stephen Roberts (Eds) Probabilistic Modeling in Bioinformatics and Medical Informatics 1-85233-778-8 Ajith Abraham, Lakhmi Jain and Robert Goldberg (Eds) Evolutionary Multiobjective Optimization 1-85233-787-7 K.C. Tan, E.F. Khor and T.H. Lee Multiobjective Evolutionary Algorithms and Applications 1-85233-836-9 Nikhil R. Pal and Lakhmi Jain (Eds) Advanced Techniques in Knowledge Discovery and Data Mining 1-85233-867-9 Amit Konar and Lakhmi Jain Cognitive Engineering 1-85233-975-6 Miroslav Kárny´ (Ed.) Optimized Bayesian Dynamic Advising 1-85233-928-4 Yannis Manolopoulos, Alexandros Nanopoulos, Apostolos N. Papadopoulos and Yannis Theodoridis R-trees: Theory and Applications 1-85233-977-2 Sanghamitra Bandyopadhyay, Ujjwal Maulik, Lawrence B. Holder and Diane J. Cook (Eds) Advanced Methods for Knowledge Discovery from Complex Data 1-85233-989-6 Marcus A. Maloof (Ed.) Machine Learning and Data Mining for Computer Security 1-84628-029-X Sifeng Liu and Yi Lin Grey Information 1-85233-995-0 Vasile Palade, Cosmin Danut Bocaniala and Lakhmi Jain (Eds) Computational Intelligence in Fault Diagnosis With 154 Figures Vasile Palade,PhD Lakhmi Jain,PhD Oxford University Computing Laboratory KES Center Oxford University of South Australia UK Australia Cosmin Danut Bocaniala,Phd Department of Communication Systems Lancaster University Lancaster UK British Library Cataloguing in Publication Data A catalogue record for this book is available from the British Library Library ofCongress Control Number:2006922573 Advanced Information and Knowledge Processing ISSN 1610-3947 ISBN-10:1-84628-343-4 Printed on acid-free paper ISBN-13:978-1-84628-343-7 © Springer-Verlag London Limited 2006 Apart from any fair dealing for the purposes ofresearch 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 oflicences issued by the Copyright 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. Printed in the United States ofAmerica (MVY) 9 8 7 6 5 4 3 2 1 Springer Science+Business Media springer.com Contributors Viorel Ariton “Danubius” University of Galati Lunca Siretului no. 3, 800416 Galati, Romania Email: [email protected] Cosmin Danut Bocaniala Computer Science and Engineering Department “Dunarea de Jos” University of Galati Domneasca 47, Galati, Romania Email: [email protected] João Calado IDMEC/ISEL, Polytechnic Institute of Lisbon Mechanical Engineering Studies Centre Rua Conselheiro Emídio Navarro, 1950-062 Lisbon, Portugal Email: [email protected] Kok Yeng Chen School of Electrical and Electronic Engineering University of Science Malaysia Engineering Campus, 14300 Nibong Tebal, Penang, Malaysia Florin Ionescu Department of Mechatronics University of Applied Sciences in Konstanz Brauneggerstraße 55, 78462 – Konstanz, Germany Email: [email protected] Weng Kin Lai MIMOS Berhad Technology Park Malaysia 57000 Kuala Lumpur, Malaysia vi V Palade, CD Bocaniala and L Jain (Eds.) Chee Peng Lim School of Electrical and Electronic Engineering University of Science Malaysia Engineering Campus, 14300 Nibong Tebal, Penang, Malaysia Email: [email protected] Ar(cid:460)nas Lipnickas Kaunas University of Technology Department of Control Technology Student(cid:464) 48-317, Kaunas LT-51367, Lithuania Email: [email protected] Luca Marinai Department of Power, Propulsion & Aerospace Engineering Cranfield University Beds. MK43 OAL, United Kingdom Email: [email protected] Luis Mendonça Technical University of Lisbon Dept. of Mechanical Engineering, GCAR/IDMEC Pav. Eng. Mecânica III, Av. Rovisco Pais, 1049-001 Lisbon, Portugal Email: [email protected] Stephen Ogaji Department of Power, Propulsion and Aerospace Engineering School of Engineering Cranfield University Beds. MK43 OAL, United Kingdom E-mail: [email protected] Vasile Palade Oxford University Computing Laboratory Wolfson Building, Parks Road Oxford, OX1 3QD, United Kingdom Email: [email protected] José Sá da Costa Technical University of Lisbon Department of Mechanical Engineering, GCAR/IDMEC Pav. Eng. Mecânica III, Av. Rovisco Pais, 1049-001 Lisbon, Portugal Email: [email protected] Computational Intelligence in Fault Diagnosis vii Riti Singh Department of Power, Propulsion and Aerospace Engineering School of Engineering Cranfield University Beds. MK43 OAL, United Kingdom João Sousa Technical University of Lisbon Dept. of Mechanical Engineering, GCAR/IDMEC Pav. Eng. Mecânica III, Av. Rovisco Pais, 1049-001 Lisbon, Portugal Email: [email protected] Dan Stefanoiu Department of Automatic Control and Computer Science “Politehnica” University of Bucharest 313 Splaiul Independen(cid:288)ei, 060042–Bucharest, Romania Email: [email protected] Foreword With the increased complexity of industrial machines and processes, the task of fault diagnosis is becoming increasingly difficult and its complexity almost unmanageable using conventional techniques. Therefore, in the past decade, intense research was dedicated to find alternative solutions using methods that mirror human reasoning as well as involve complex problem solving techniques inspired from nature, to cope with the need for adaptation of the diagnostic methodology to the inherent changes occurring in the diagnosed process. The automatic diagnosis requires the ability to identify the symptoms automatically and map them to their causes as well as, eventually, to prescribe solutions for repairing/restoring the good functionality of the device, machine or plant. Some methods can prove suitable for certain systems while being totally inappropriate for others. Computational intelligence attempts to emulate human and biological reasoning, decision-making, learning and optimization via a series of techniques that mirror the adaptive evolutionary nature of living beings. Such techniques can be either used individually or combined into more complex hybrid methodologies, resulting in systems with enhanced capabilities, e.g., the same system can benefit from the decision-making under uncertainty enabled by fuzzy logic as well as from learning and adaptation that neural networks provide, or from the evolutionary optimization inherent in genetic algorithms. Since the early 1990s, attempts to apply various computational intelligence methods to fault diagnosis, sometimes used to augment traditional methods, were made mainly in research laboratories. Given their success, these are now moving into industrial settings. Big companies such as Siemens and ABB have embraced such novel technologies very early. Most successful attempts proved that fault diagnosis can greatly benefit from computational intelligence techniques. Neural networks can ease fault identification through model matching and learning of new symptoms. Fuzzy logic can improve the diagnostic decision-making under the uncertainty inherent in the diagnostic information: vague symptoms, ambiguous mapping of symptoms to their causes as well as capturing the gradual degradation of systems and processes in appropriate (fuzzy) models. Genetic algorithms are capable of optimizing the diagnostic models as well as the diagnostic process itself by tracking the (sometimes gradual) changes occurring in the diagnosed system in various ways. We welcome this new book for offering us a very good overview of the state of the art in the development of computational intelligence techniques pertaining to fault diagnosis. Covering all computational intelligence techniques both in theory as well as illustrating how they work by clear examples and/or x V Palade, CD Bocaniala and L Jain (Eds.) practical applications on a relatively broad range of problems, the book gradually exposes the reader to these various methods in its eleven chapters. Structurally, the book is a comprehensive collection of works arranged in a progressive manner, to ease the gradual grasping of concepts. Starting with a very good overview of computational intelligence and its suitability to the difficult task of fault diagnosis, in Chapter 1, it continues (in Chapters 2 to 5) with four applications involving fuzzy logic to solve various real-world diagnosis problems, then Chapters 6 and 7 illustrate successful neural network-based diagnostic models, to progress in Chapter 8 to a generic computational intelligence approach. Hybrid neuro-fuzzy diagnostic approaches are further illustrated in Chapters 9 and 10. The last chapter presents a novel distributed causal model for diagnosing complex systems. Overall, I salute this work for marking the progress made in this significant area of fault diagnosis, which can be very useful to a broad audience, ranging from industrial users to graduate students. Enabling the use of these techniques in industrial applications as well as for training and teaching purposes, the book can be regarded as both a repository of knowledge for practitioners and a basis for a course on computational intelligence in diagnosis. Professor Mihaela Ulieru, Canada Research Chair Preface In one of his recent commentaries, called “Integration automation”, Mark Venables, editor of the IEE Manufacturing Engineer Journal, predicts that “there are five technologies that will drive the future of industrial automation. These are control and diagnosis, communication, software, electronics, and materials – with the former trio being the most important” (http://www.iee.org/oncomms/ sector/computing/commentary.cfm). Indeed, one of the main current trends in solving problems in manufacturing industry is developing fault-tolerant control schemes. Fault-tolerant control is concerned with making the controlled system able to maintain control objectives, despite the occurrence of a fault. Hence, fault diagnosis represents the main ingredient of a fault-tolerant control system. Diagnosing the faults that occurred in a system permits triggering control mechanisms to keep a plant working sufficiently well until the necessary maintenance may be performed. In practice, this feature results in a significant improvement in industrial plant safety, productivity and time in service. There are two main categories of fault diagnosis techniques currently in use and each has its own basic support theory. The first class of methodologies used for fault diagnosis-related problems were based on mathematical models of the monitored plant. The differences between the plant model and its actual behaviour are called residuals and form the basis for deciding if a fault did or did not occur; and if a fault has occurred, deciding which particular fault occurred. Unfortunately, these techniques provide satisfactory results only when plants exhibit linear behaviour or when the modelling errors can be kept within acceptable limits. Accurate mathematical models can be obtained only for plants with low behavioural complexity. Recent research efforts have concentrated on finding suitable techniques to model plants with high nonlinear behaviour, noise and uncertainty. These three characteristics have been successfully mastered by using computational intelligence methodologies. These solutions are based on models such as fuzzy systems, neural networks, and genetic algorithms, to name only the most important of them. The above methods are commonly combined to give the desired result. Besides using residuals for diagnosis purposes, the computational intelligence methods may also be used to directly map the sensor measurements to the faults’ space. These methods allow an understanding of plant behaviour using rules obtained directly from sensor measurements. However, even if these techniques can solve the difficult problems posed by nonlinearity, noise and uncertainty, if the complexity of the plant behaviour is very high, the computational load becomes too large for practical purposes.
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