- Email: [email protected]
Fault Detection and Diagnosis in Industrial Systems
Journal of Process Control 12 (2002) 453–454 www.elsevier.com/locate/jprocont
Book review
Fault Detection and Diagnosis in Industrial Systems By L.H. Chiang, E.L. Russell, R.D. Braatz, ISBN 1 852 33327 8. 279 pages. $42, published by Springer, London This book treats the important and timely subject of process monitoring in industrial systems. In Chapter 1 the authors clearly define the four procedures associated with process monitoring: fault detection, fault identification, fault diagnosis, and process recovery. Running processes safely, whether they are chemical plants, aircraft, computer systems, transportation systems, etc., is extremely important in today’s society. One severe spill from a chemical plant can wipe out years of increased profitability from using advanced control. Detection, identification, and diagnosis of faults before they cause such losses are important aspects of ensuring the economic vitality of manufacturing systems. The area of fault detection is not only very broad, but it is evolving at a fast rate. The authors attempt the very ambitious task of covering essentially all the major approaches to the subject. There are chapters on data based approaches including multivariate statistical methods, discriminate analysis, and canonical variate analysis. In addition, analytical methods involving estimators and parity equations are covered as are knowledge-based approaches including expert systems, neural networks, and fuzzy methods. In a text that is less than 300 pages long it is difficult to go into depth in each of these areas. Indeed as pointed out by the authors themselves entire texts have been devoted to topics that are covered in one chapter in their book. What the authors do achieve is an excellent overview of the material that is covered. They provide an extensive set of 367 up to date references so that the interested reader can pursue subjects in further detail through them. Part I of the book gives an introduction to the subject. An overview of the techniques discussed in the book and its organization are given. This overview is clearly written and the nomenclature used in the remainder of the text is clearly defined. One aspect where the overview could be improved involves the issue of the type of data that is typically available in industrial practice. On page 8 the authors state that theoretical developments for Fisher discriminant analysis (FDA) indicate that it should be more effective than principal component analysis (PCA) for diagnosing
faults. It would be useful for readers new to the field to point out here that in many if not most cases in industry one does not have sufficient data on specific faults to take advantage of the potential benefits of FDA. Most industrial data would come from acceptable operation. Having a large database for specific faults would be the exception rather than the norm. Part II covers background material on multivariate statistics and classification. In this section basic statistical tests are reviewed and data requirements are discussed. In Chapter 2 the difference between common cause and special cause variations in process data is made. It is pointed out that standard process control strategies may be able to remove most of the special cause variations, but not the common cause variations. For someone new to the field, it would be useful to elaborate here. For example, process control cannot make effective use of information from a faulty sensor, but cascade control can overcome a sticky valve. On page 16 the authors discuss the need to remove variables and outliers from the database, but the discussion is very brief and in the case of removing variables no guidance is given. In this chapter terms are again clearly defined and there is a nice discussion on data requirements. Part II ends with Chapter 3, which discusses pattern classification. Part III covers data driven methods including principal component analysis (PCA), partial least squares (PLS), Fisher discriminant analysis (FDA), and canonical variate analysis (CVA). While PCA and PLS are discussed in a number of texts, the same cannot be said for FDA and CVA. The text does an excellent job of giving the basics of these methods and illustrating their key features. To compare and contrast PCA, PLS, and FDA a common data set, namely Fisher’s iris data, is used and this approach helps the reader to understand the various approaches, what they achieve, and how they differ. However, the iris data is not typical of most industrial data in that one has a significant number of data points in each of three distinct classes. The majority of industrial data would typically come from good operation, and the remainder from faulty operation where the specific fault involved may not be known. Chapter 4 discusses PCA, including dynamic PCA, which makes use of time, lagged data to develop an ARX type model. However, such models can require a
454
Book review / Journal of Process Control 12 (2002) 453–454
large number of parameters for accuracy. In the discussion on Fig. 4.4 it would have been useful to point out that the T2 statistic violations represent model extrapolation in which the model accuracy remains good, while the Q statistic violations represent cases where the model accuracy is suspect. Chapter 5 discusses FDA and Chapter 6 PLS. The use of PLS in fault diagnosis requires that a training database containing information on specific faults be available, as does FDA. In Chapters 4 and 6 it would have been useful to briefly discuss multiway PCA and PLS for batch processes, and multiblock PCA and PLS for plantwide applications. Chapter 7 on CVA gives an excellent summary of the method. The superiority of CVA, which yields a state space model equivalent to an ARMAX model, compared to dynamic PCA, is pointed out. The number of parameters in a CVA model is typically much smaller than those in an ARX model. CVA is superior to classical identification methods in that a model order does not have to be specified a priori, and convergence problems associated with parameter identification are avoided. The relationship between CVA, PCA, PLS and FDA is clearly presented in the chapter. Using CVA on normal operating data will work, but to detect small faults with CVA one may want to consider use of external forcing in order to develop a rich database. Part IV of the book consists of 3 chapters and it discusses the application of the methods treated to the Tennessee Eastman testbed process. I found the presentation in these 3 chapters to be a little chopped up. Since they all deal with the same application, the material could have been given in a single chapter or 2 chapters at most. The authors do present extensive results on the Eastman plant for the various data based techniques discussed in Part III. In Chapter 9 the
authors discuss how they developed their database for 21 faults that they use to demonstrate the various approaches in the text. It is here that they mention that such a rich database on specific faults may not be available in practice. Chapter 10 presents an extensive set of results that compares the various approaches covered in the text on the faults in the Tennessee Eastman process. It would have been useful to conclude this chapter with a summary and a set of recommendations and guidelines since so many cases and methods are examined. It should be noted that interested readers using instructions provided in the text can download the simulation model used by the authors. Part V of the book gives an overview of analytical and knowledge based methods. In the chapter on analytical methods an overview of process identification, observerbased methods, and the parity equation approach are given. The final chapter gives an overview of expert systems, neural networks, and fuzzy methods for fault detection and diagnosis. Given that the main goal of the authors is to present an overview of the field, they do achieve their goal. The book could easily serve as a text for a one-semester course on fault detection and diagnosis. It also would be a useful addition to the library of practicing engineers who want to see the big picture in terms of available methods. It is published in paperback and it is reasonably priced. Thus, it should be a valuable edition to the library of those interested in the field. Thomas McAvoy Department of Chemical Engineering University of Maryland College Park, Maryland MD 20742, USA E-mail address: [email protected]
0959-1524/02/$ - see front matter # 2001 Published by Elsevier Science Ltd. All rights reserved. PII: S0959-1524(01)00050-6
Book review
Fault Detection and Diagnosis in Industrial Systems By L.H. Chiang, E.L. Russell, R.D. Braatz, ISBN 1 852 33327 8. 279 pages. $42, published by Springer, London This book treats the important and timely subject of process monitoring in industrial systems. In Chapter 1 the authors clearly define the four procedures associated with process monitoring: fault detection, fault identification, fault diagnosis, and process recovery. Running processes safely, whether they are chemical plants, aircraft, computer systems, transportation systems, etc., is extremely important in today’s society. One severe spill from a chemical plant can wipe out years of increased profitability from using advanced control. Detection, identification, and diagnosis of faults before they cause such losses are important aspects of ensuring the economic vitality of manufacturing systems. The area of fault detection is not only very broad, but it is evolving at a fast rate. The authors attempt the very ambitious task of covering essentially all the major approaches to the subject. There are chapters on data based approaches including multivariate statistical methods, discriminate analysis, and canonical variate analysis. In addition, analytical methods involving estimators and parity equations are covered as are knowledge-based approaches including expert systems, neural networks, and fuzzy methods. In a text that is less than 300 pages long it is difficult to go into depth in each of these areas. Indeed as pointed out by the authors themselves entire texts have been devoted to topics that are covered in one chapter in their book. What the authors do achieve is an excellent overview of the material that is covered. They provide an extensive set of 367 up to date references so that the interested reader can pursue subjects in further detail through them. Part I of the book gives an introduction to the subject. An overview of the techniques discussed in the book and its organization are given. This overview is clearly written and the nomenclature used in the remainder of the text is clearly defined. One aspect where the overview could be improved involves the issue of the type of data that is typically available in industrial practice. On page 8 the authors state that theoretical developments for Fisher discriminant analysis (FDA) indicate that it should be more effective than principal component analysis (PCA) for diagnosing
faults. It would be useful for readers new to the field to point out here that in many if not most cases in industry one does not have sufficient data on specific faults to take advantage of the potential benefits of FDA. Most industrial data would come from acceptable operation. Having a large database for specific faults would be the exception rather than the norm. Part II covers background material on multivariate statistics and classification. In this section basic statistical tests are reviewed and data requirements are discussed. In Chapter 2 the difference between common cause and special cause variations in process data is made. It is pointed out that standard process control strategies may be able to remove most of the special cause variations, but not the common cause variations. For someone new to the field, it would be useful to elaborate here. For example, process control cannot make effective use of information from a faulty sensor, but cascade control can overcome a sticky valve. On page 16 the authors discuss the need to remove variables and outliers from the database, but the discussion is very brief and in the case of removing variables no guidance is given. In this chapter terms are again clearly defined and there is a nice discussion on data requirements. Part II ends with Chapter 3, which discusses pattern classification. Part III covers data driven methods including principal component analysis (PCA), partial least squares (PLS), Fisher discriminant analysis (FDA), and canonical variate analysis (CVA). While PCA and PLS are discussed in a number of texts, the same cannot be said for FDA and CVA. The text does an excellent job of giving the basics of these methods and illustrating their key features. To compare and contrast PCA, PLS, and FDA a common data set, namely Fisher’s iris data, is used and this approach helps the reader to understand the various approaches, what they achieve, and how they differ. However, the iris data is not typical of most industrial data in that one has a significant number of data points in each of three distinct classes. The majority of industrial data would typically come from good operation, and the remainder from faulty operation where the specific fault involved may not be known. Chapter 4 discusses PCA, including dynamic PCA, which makes use of time, lagged data to develop an ARX type model. However, such models can require a
454
Book review / Journal of Process Control 12 (2002) 453–454
large number of parameters for accuracy. In the discussion on Fig. 4.4 it would have been useful to point out that the T2 statistic violations represent model extrapolation in which the model accuracy remains good, while the Q statistic violations represent cases where the model accuracy is suspect. Chapter 5 discusses FDA and Chapter 6 PLS. The use of PLS in fault diagnosis requires that a training database containing information on specific faults be available, as does FDA. In Chapters 4 and 6 it would have been useful to briefly discuss multiway PCA and PLS for batch processes, and multiblock PCA and PLS for plantwide applications. Chapter 7 on CVA gives an excellent summary of the method. The superiority of CVA, which yields a state space model equivalent to an ARMAX model, compared to dynamic PCA, is pointed out. The number of parameters in a CVA model is typically much smaller than those in an ARX model. CVA is superior to classical identification methods in that a model order does not have to be specified a priori, and convergence problems associated with parameter identification are avoided. The relationship between CVA, PCA, PLS and FDA is clearly presented in the chapter. Using CVA on normal operating data will work, but to detect small faults with CVA one may want to consider use of external forcing in order to develop a rich database. Part IV of the book consists of 3 chapters and it discusses the application of the methods treated to the Tennessee Eastman testbed process. I found the presentation in these 3 chapters to be a little chopped up. Since they all deal with the same application, the material could have been given in a single chapter or 2 chapters at most. The authors do present extensive results on the Eastman plant for the various data based techniques discussed in Part III. In Chapter 9 the
authors discuss how they developed their database for 21 faults that they use to demonstrate the various approaches in the text. It is here that they mention that such a rich database on specific faults may not be available in practice. Chapter 10 presents an extensive set of results that compares the various approaches covered in the text on the faults in the Tennessee Eastman process. It would have been useful to conclude this chapter with a summary and a set of recommendations and guidelines since so many cases and methods are examined. It should be noted that interested readers using instructions provided in the text can download the simulation model used by the authors. Part V of the book gives an overview of analytical and knowledge based methods. In the chapter on analytical methods an overview of process identification, observerbased methods, and the parity equation approach are given. The final chapter gives an overview of expert systems, neural networks, and fuzzy methods for fault detection and diagnosis. Given that the main goal of the authors is to present an overview of the field, they do achieve their goal. The book could easily serve as a text for a one-semester course on fault detection and diagnosis. It also would be a useful addition to the library of practicing engineers who want to see the big picture in terms of available methods. It is published in paperback and it is reasonably priced. Thus, it should be a valuable edition to the library of those interested in the field. Thomas McAvoy Department of Chemical Engineering University of Maryland College Park, Maryland MD 20742, USA E-mail address: [email protected]
0959-1524/02/$ - see front matter # 2001 Published by Elsevier Science Ltd. All rights reserved. PII: S0959-1524(01)00050-6