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Process Control System Fault Diagnosis: A Bayesian Approach Ruben T. Gonzalez, University of Alberta, Canada Fei Qi, Suncor Energy Inc., Canada Biao Huang, University of Alberta, Canada Data-driven Inferential Solutions for Control System Fault Diagnosis A typical modern process system consists of hundreds or even thousands of control loops, which are overwhelming for plant personnel to monitor. The main objectives of this book are to establish a new framework for control system fault diagnosis, to synthesize observations of different monitors with a prior knowledge, and to pinpoint possible…mehr
Process Control System Fault Diagnosis: A Bayesian Approach Ruben T. Gonzalez, University of Alberta, Canada Fei Qi, Suncor Energy Inc., Canada Biao Huang, University of Alberta, Canada Data-driven Inferential Solutions for Control System Fault Diagnosis A typical modern process system consists of hundreds or even thousands of control loops, which are overwhelming for plant personnel to monitor. The main objectives of this book are to establish a new framework for control system fault diagnosis, to synthesize observations of different monitors with a prior knowledge, and to pinpoint possible abnormal sources on the basis of Bayesian theory. Process Control System Fault Diagnosis: A Bayesian Approach consolidates results developed by the authors, along with the fundamentals, and presents them in a systematic way. The book provides a comprehensive coverage of various Bayesian methods for control system fault diagnosis, along with a detailed tutorial. The book is useful for graduate students and researchers as a monograph and as a reference for state-of-the-art techniques in control system performance monitoring and fault diagnosis. Since several self-contained practical examples are included in the book, it also provides a place for practicing engineers to look for solutions to their daily monitoring and diagnosis problems. Key features: A comprehensive coverage of Bayesian Inference for control system fault diagnosis. Theory and applications are self-contained. Provides detailed algorithms and sample Matlab codes. Theory is illustrated through benchmark simulation examples, pilot-scale experiments and industrial application. Process Control System Fault Diagnosis: A Bayesian Approach is a comprehensive guide for graduate students, practicing engineers, and researchers who are interests in applying theory to practice.
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Ruben Gonzalez completed his Bachelor's degree in chemical engineering in 2008 at the University of New Brunswick. Under the supervision of Dr. Biao Huang, he completed his Master's degree in 2010 and his Doctorate in 2014, both in chemical engineering, at the University of Alberta. His research interests include Bayesian diagnosis, fault detection and diagnosis, data reconciliation, and applied kernel density estimation. Fei Qi obtained his Ph.D. degree in Process Control from the University of Alberta, Canada, in 2011. He had his M.Sc. degree (2006) and B.Sc. degree (2003) in Automation from the University of Science and Technology of China. Fei Qi joined Suncor Energy Inc. in 2010 as an Advance Process Control Engineer. He has extensive experiences in applying system identification, model predictive control, and control performance monitoring in real industrial processes. His Ph.D. research was on applying Bayesian statistics to control loop diagnosis. His current research interests include model predictive control, soft sensor, fault detection, and process optimization. Biao Huang obtained his PhD degree in Process Control from the University of Alberta, Canada, in 1997. He is currently a Professor in the Department of Chemical and Materials Engineering, University of Alberta, NSERC Industrial Research Chair in Control of Oil Sands Processes and AITF Industry Chair in Process Control. He is a Fellow of the Canadian Academy of Engineering, Fellow of the Chemical Institute of Canada, and recipient of numerous awards including Germany's Alexander von Humboldt Research Fellowship, Bantrel Award in Design and Industrial Practice, APEGA Summit Award in Research Excellence, best paper award from Journal of Process Control etc. Biao Huang's main research interests include: Bayesian inference, control performance assessment, fault detection and isolation. Biao Huang has applied his expertise extensively in industrial practice. He also serves as the Deputy Editor-in-Chief for Control Engineering Practice, the Associate Editor for Canadian Journal of Chemical Engineering and the Associate Editor for Journal of Process Control.
Preface xiii Acknowledgements xvii List of Figures xix List of Tables xxiii Nomenclature xxv Part I FUNDAMENTALS 1 Introduction 3 1.1 Motivational Illustrations 3 1.2 Previous Work 4 1.2.1 Diagnosis Techniques 4 1.2.2 Monitoring Techniques 7 1.3 Book Outline 12 1.3.1 Problem Overview and Illustrative Example 12 1.3.2 Overview of Proposed Work 12 References 16 2 Prerequisite Fundamentals 19 2.1 Introduction 19 2.2 Bayesian Inference and Parameter Estimation 19 2.2.1 Tutorial on Bayesian Inference 24 2.2.2 Tutorial on Bayesian Inference with Time Dependency 27 2.2.3 Bayesian Inference vs. Direct Inference 32 2.2.4 Tutorial on Bayesian Parameter Estimation 33 2.3 The EM Algorithm 38 2.4 Techniques for Ambiguous Modes 44 2.4.1 Tutorial on Theta Parameters in the Presence of Ambiguous Modes 46 2.4.2 Tutorial on Probabilities Using Theta Parameters 47 2.4.3 Dempster-Shafer Theory 48 2.5 Kernel Density Estimation 51 2.5.1 From Histograms to Kernel Density Estimates 52 2.5.2 Bandwidth Selection 54 2.5.3 Kernel Density Estimation Tutorial 55 2.6 Bootstrapping 56 2.6.1 Bootstrapping Tutorial 57 2.6.2 Smoothed Bootstrapping Tutorial 57 2.7 Notes and References 60 References 61 3 Bayesian Diagnosis 62 3.1 Introduction 62 3.2 Bayesian Approach for Control Loop Diagnosis 62 3.2.1 Mode M 62 3.2.2 Evidence E 63 3.2.3 Historical Dataset D 64 3.3 Likelihood Estimation 65 3.4 Notes and References 67 References 67 4 Accounting for Autodependent Modes and Evidence 68 4.1 Introduction 68 4.2 Temporally Dependent Evidence 68 4.2.1 Evidence Dependence 68 4.2.2 Estimation of Evidence-transition Probability 70 4.2.3 Issues in Estimating Dependence in Evidence 74 4.3 Temporally Dependent Modes 75 4.3.1 Mode Dependence 75 4.3.2 Estimating Mode Transition Probabilities 77 4.4 Dependent Modes and Evidence 81 4.5 Notes and References 82 References 82 5 Accounting for Incomplete Discrete Evidence 83 5.1 Introduction 83 5.2 The Incomplete Evidence Problem 83 5.3 Diagnosis with Incomplete Evidence 85 5.3.1 Single Missing Pattern Problem 86 5.3.2 Multiple Missing Pattern Problem 92 5.3.3 Limitations of the Single and Multiple Missing Pattern Solutions 93 5.4 Notes and References 94 References 94 6 Accounting for Ambiguous Modes: A Bayesian Approach 96 6.1 Introduction 96 6.2 Parametrization of Likelihood Given Ambiguous Modes 96 6.2.1 Interpretation of Proportion Parameters 96 6.2.2 Parametrizing Likelihoods 97 6.2.3 Informed Estimates of Likelihoods 98 6.3 Fagin-Halpern Combination 99 6.4 Second-order Approximation 100 6.4.1 Consistency of Theta Parameters 101 6.4.2 Obtaining a Second-order Approximation 101 6.4.3 The Second-order Bayesian Combination Rule 103 6.5 Brief Comparison of Combination Methods 104 6.6 Applying the Second-order Rule Dynamically 105 6.6.1 Unambiguous Dynamic Solution 105 6.6.2 The Second-order Dynamic Solution 106 6.7 Making a Diagnosis 107 6.7.1 Simple Diagnosis 107 6.7.2 Ranged Diagnosis 107 6.7.3 Expected Value Diagnosis 107 6.8 Notes and References 111 References 111 7 Accounting for Ambiguous Modes: A Dempster-Shafer Approach 112 7.1 Introduction 112 7.2 Dempster-Shafer Theory 112 7.2.1 Basic Belief Assignments 112 7.2.2 Probability Boundaries 114 7.2.3 Dempster's Rule of Combination 114 7.2.4 Short-cut Combination for Unambiguous Priors 115 7.3 Generalizing Dempster-Shafer Theory 116 7.3.1 Motivation: Difficulties with BBAs 117 7.3.2 Generalizing the BBA 119 7.3.3 Generalizing Dempster's Rule 122 7.3.4 Short-cut Combination for Unambiguous Priors 123 7.4 Notes and References 124 References 125 8 Making Use of Continuous Evidence Through Kernel Density Estimation 126 8.1 Introduction 126 8.2 Performance: Continuous vs. Discrete Methods 127 8.2.1 Average False Negative Diagnosis Criterion 127 8.2.2 Performance of Discrete and Continuous Methods 129 8.3 Kernel Density Estimation 132 8.3.1 From Histograms to Kernel Density Estim
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