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Fault Detection and Diagnosis in Industrial Systems 是工业系统的故障检测与诊断的英文原版书籍,主要讲解了工业系统的故障检测与诊断
L H. Chiang, E L. Russell andr.D Braatz Fault detection and diagnosis in Industrial Systems With 81 Figures Springer eo H. Chiang, MS Richard d. braatz. phD Department of Chemical Engineering, University of Ilinois at Urbana-Champaign, 600 S Mathews Avenue. Urbana, Illinois 61801-3792 USA Evan l. russell, phD Exxon Mobil Upstream Reasearch Company, URC Building, Room C-312, PO Box 2189, Houston, TX 77252, USA ISBN978-1-85233-327-0 British Library Cataloguing in Publication Data Chiang, Leo H. Fault detection and diagnosis in industrial systems. (Advanced textbooks in control and signal processing) 1. Fault location(Engineering) 2. Process control I. Title II. Russell, Evan, 1972- III. Braatz, Richard D. 1966- 670.42 ISBN978-1-85233-327-0 Library of Congress Cataloging-in-Publication Data Chiang, Leo h, 1975 Fault detection and diagnosis in industrial systems / Leo H. Chiang, Evan L. Russell nd richard d braatz m-(Advanced textbooks in control and signal processing) Includes bibliographical references and index. IsBN978-1-85233-327-0SBN97814471-0347-9(eook DoI10.1007/978-1444710347-9 1. Chemical process control 2. Fault location(Engineering) I Russell, Evan, 1972-II Braatz, Richard D, 1966- III Title. IV Series. TP15575,C4652000 6602815dc21 00045601 Apart from any fair dealing for the purposes of research or private study, or criticism or review, as per mitted 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 pub lishers, or in the case of reprographic reproduction in accordance with the terms of licences issued by the Copyright Licensing Agency. Enquiries concerning reproduction outside those terms should be sent to the publishers. G Springer-Verlag London 2001 Originally published by Springer-Verlag London Limited in 2002 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 there- fore free for general use. The publisher makes no representation, express or implied, with regard to the accuracy of the infor mation contained in this book and cannot accept any legal responsibility or liability for any errors or omissions that may be made Typesetting: Camera ready by authors 69/3830-543210 Printed on acid-free paper SPiN 10768812 Series editors'Foreword The topics of control engineering and signal processing continue to flourish and develop. In common with general scientific investigation, new ideas, concepts and interpretations emerge quite spontaneously and these are then discussed, used discarded or subsumed into the prevailing subject paradigm. Sometimes these innovative concepts coalesce into a new sub-discipline within the broad subject tapestry of control and signal processing. This preliminary battle between old and new usually takes place at conferences, through the Internet and in the journals of the discipline. After a little more maturity has been acquired by the new concepts then archival publication as a scientific or engineering monograph may occur. a new concept in control and signal processing is known to have arrived wh en sufficient material has developed for the topic to be taught as a specialised tutorial workshop or as a course to undergraduates, graduates or industrial engineers. The Advanced Textbooks in Control and Signal Processing Series is designed as a vehicle for the systematic presentation of course material for both popular and innovative topics in the discipline. It is hoped that prospective authors will welcome the opportunity to publish a structured presentation of either existing subject areas or some of the newer emerging control and signal processing technologies Fault detection and process monitoring is one of the new growth areas in process control. The reason for this development is not hard to find. New instrumentation and communications technologies have created a wealth of real- time data from processes in both new and existing manufacturing plant installations. Process operators are therefore keen to use this data to minimise plant downtime and optimise plant operations. The traditional routes to fault detection were model based and to use them the process has to be well understood an alternative group of methods has emerged which do not require the use of an explicit model. This is the key basic construct for the data-driven paradigm Model-free and non-parametric methods for fault detection, process optimisation and control design are currently at a particularly exciting stage of development This new advanced textbook by Chiang, Russell and Braatz primarily tackles the data-driven routes to Fault Detection and Diagnosis. It is an outgrowth of a prior Advances in Industrial Control monograph; Russell, Chiang and braatz Data-driven Techniques for Fault Detection and Diagnosis in Chemical Processes, 2000, IsBN 1-85233-258-1. The new textbook expands the material of the monograph and gives a fuller presentation of some of the alternative model based methods, the analytical methods, and of the knowledge-based techniques ⅵ Series editors’ Foreword This allows the reader to compare and contrast the different approaches to the problem of fault detection and diagnosis. Thus the text is suitable for advanced courses for process, chemical and control engineers M.J. Grimble and m.a. johnson Industrial Control Centre Glasgow, Scotland, U.K October 2000 Preface Modern manufacturing facilities are large scale, highly complex, and oper- ate with a large number of variables under closed-loop control. Early and accurate fault detection and diagnosis for these plants can minimize down time, increase the safety of plant operations, and reduce manufacturing costs Plants are becoming more heavily instrumented, resulting in more data be coming available for use in detecting and diagnosing faults. Univariate control charts(e. g, Shewhart charts) have a limited ability to detect and diagnose faults in such multivariable processes. This has led to a surge of academic and industrial effort concentrated on developing more effective process moni- toring methods. A large number of these methods are being regularly applied to real industrial systems which makes these techniques suitable for coverage n undergraduate and graduate courses This textbook presents the theoretical background and practical tech niques for process monitoring The intended audience is engineering students and practicing engineers. The book is appropriate for a first-year graduate or advanced undergraduate course in process monitoring. Numerous simple ex amples and a simulator for a large-scale industrial plant are used to illustrate the methods. As the most effective method for learning the techniques is b applying them, the Tennessee Eastman plant simulator has been made avail- ableathttp://brahms.scs.uiuc.edu.Readersareencouragedtocollect process data from the simulator, and then apply a range of process moni- toring techniques to detect, isolate, and diagnose various faults. The process monitoring techniques can be implemented using commercial software pack- ages such as the MATLAB PLS Toolbox and ADAPTx What were the goals in writing this textbook? Although much effort has been devoted to process monitoring by both academics and industrially em- ployed engineers, books on the subject are still rather limited in coverage These books usually focus entirely on one type of approach such as statistical quality control(Montgomery(1991), Park and Vining(2000) or analytical methods(Chen and Patton(1999), Gertler(1998), Patton, Frank, and Clark (1989)). Some books treat both statistical and analytical methods(Himmel- blau(1978), Basseville and Nikiforov(1993)), but do not cover knowledge- based methods. Wang(1999 )covers both statistical and knowledge-based methods, but does not cover analytical methods. Many process monitoring methods of practical importance, such as those based on canonical variate analysis and Fisher discriminant analysis, are described in hardly any books on process monitoring(an exception is Russell, Chiang, and Braatz(2000)) While many of these books do an excellent job covering their intended top icS, it was our opinion that there was a need for a single textbook that covers data-driven, analytical, and knowledge-based process monitoring methods Part of the motivation for this is that many engineering curricula do not have sufficient space for courses on each of these topics in isolation. But all of these methods are becoming increasingly important in practice, and should be studied by engineering students who plan to work in industry. These include mechanical, electrical, industrial, chemical, nuclear, manufacturing, control, aerospace, quality, and reliability engineers, as well as applied statisticians The proportion of coverage given to each topic is based on our own experi- ence(all three authors have applied process monitoring methods to industrial systems with hundreds of measured variables), as well as on the industrial experience of other engineers as described in person or in publications. The first chapter gives an overview of process monitoring procedures and methods Chapter 2 provides background in multivariate statistics, including univari ate control charts and a discussion of data requirements Chapter 3 discusses pattern classification, including discriminant analysis and feature extraction which are fundamental to fault diagnosis techniques Chapters 4-7 cover data-driven process monitoring methods. Principal component analysis(PCA)and partial least squares(PLs)are multi variate statistical methods that generalize the univariate control charts that have been applied for decades. Fisher discriminant analysis(FDa)is a fault diagnosis method based on the pattern classification literature. Canon ical variate analysis(CvA)is a subspace identification method that has been used in process monitoring in a similar manner to PCA and PLS. These four methods represent the state of the art in data-driven process monitor- ing methods, which are the methods most heavily used in many chemical and manufacturing industries. One reason for the popularity of data-driven methods is that they do not require first-principles models, the development of which is usually costly or time-consuming. For this reason, these meth- ods are also the predominant methods that have been applied to large-scale systems. In Chapters 8-10 the methods are compared through application to a large-scale system, the Tennessee Eastman plant simulator. This gives the readers an understanding of the strengths and weaknesses of various ap- proaches, as well as some realistic homework problems Chapter 11 describes analytical methods including parameter estimation, state estimation, and parity relations. While not as pervasive as data-driven methods in many industries, in some cases a first-principles model is available and analytical methods are suited to using these models for process monitor ing. Also, in most engineering curricula it is the analytical approach that is most closely related to topics covered in other control courses. Chapter 12 de- scribes knowledge-based methods, including causal analysis, expert systems and pattern recognition. Knowledge-based methods are especially suited to systems in which there are inadequate data to apply a data-driven method but qualitative or semi-qualitative models can be derived from causal mod eling of the system, expert knowledge, or fault-symptom examples. Each of the data-driven, analytical, and knowledge-based approaches have strengths and limitations. Incorporating several techniques for process monitoring can be beneficial in many applications. Chapter 12 also discusses various combi- nations of process monitoring techniques The authors thank International Paper, dupont, and the National Center for Supercomputing Applications for funding over the past three years this textbook was being written. L.H.C. EL.R. R.D. B Urbana. illinois Contents Part I Introduction 1。 Introduction.... 1.1 Process Monitoring P rocedures........ 1.2 Process Monitoring Measures 5 1.3 Process Monitoring Methods 7 1.4 Book Organization ,,,.10 Part II. Background 2. Multivariate statistics 2.1 Introduction 2.2 Data P, 16 2.3 Univariate Statistical Monitoring .17 2.4 1 Statistic 21 2.5 Thresholds for the T2 Statistic 22 26 Data requirements·……… 24 2.7 Homework Problems 25 3. Pattern Classification 27 3.1 Introduction ·垂垂 .27 3.2 Discriminant Analysis 28 3.3 Feature Extraction 3.4 Homework Problems ,31 Part III Data-driven methods 4. Principal Component Analysis 35 4.1 Introduction ......,35 4.2 Principal Component analysis 36 4.3 Reduction Order ,41 4.4 Fault detection 4.5 Fault Identification∴∴ ∴.......45

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