Richard J. Fickelscherer, Daniel L. Chester

Optimal Automated Process Fault Analysis

• Gebundenes Buch

Produktbeschreibung

Automated fault analysis is not widely used within chemical processing industries due to problems of cost and performance as well as the difficulty of modeling process behavior at needed levels of detail. In response, this book presents the method of minimal evidence (MOME), a model-based diagnostic strategy that facilitates the development and implementation of optimal automated process fault analyzers. With this book as their guide, readers have a powerful new tool for ensuring the safety and reliability of any chemical processing system.

---

Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.

Produktdetails

• Verlag: Wiley & Sons
• 1. Auflage
• Seitenzahl: 224
• Erscheinungstermin: 4. Januar 2013
• Englisch
• Abmessung: 236mm x 160mm x 20mm
• Gewicht: 590g
• ISBN-13: 9781118372319
• ISBN-10: 111837231X
• Artikelnr.: 36265671

Autorenporträt

Dr. Richard J. Fickelscherer is currently a licensed Professional Engineer and is a principal owner of FALCONEER Technologies, LLC. He has developed and implemented programs which provide various supervisory control functions for DuPont, Exxon-Mobile, Merck Pharmaceuticals, Koch Industries, the FMC Corporation and many other client companies. Dr. Daniel L. Chester joined the Department of Computer and Information Sciences at the University of Delaware in 1980, where he soon became one of the principal investigators on the FALCON project. He is currently Associate Chair in the computer science department at the University of Delaware. He has been involved in the creation and development of three companies, one of which is FALCONEER Technologies, LLC. He is also co-inventor in five U.S. patents.

Inhaltsangabe

Foreword xiii
Preface xv
Acknowledgments xix
1 Motivations for Automating Process Fault Analysis 1
1.1 Introduction 1
1.2 CPI Trends to Date 1
1.3 The Changing Role of Process Operators in Plant Operations 3
1.4 Methods Currently Used to Perform Process Fault Management 5
1.5 Limitations of Human Operators in Performing Process Fault
Management 10
1.6 The Role of Automated Process Fault Analysis 12
1.7 Anticipated Future CPI Trends 13
1.8 Process Fault Analysis Concept Terminology 14
References 16
2 Method of Minimal Evidence: Model-Based Reasoning 21
2.1 Overview 21
2.2 Introduction 22
2.3 Method of Minimal Evidence Overview 23
2.3.1 Process Model and Modeling Assumption Variable Classifications 28
2.3.2 Example of a MOME Primary Model 31
2.3.3 Example of MOME Secondary Models 36
2.3.4 Primary Model Residuals' Normal Distributions 39
2.3.5 Minimum Assumption Variable Deviations 41
2.3.6 Primary Model Derivation Issues 44
2.3.7 Method for Improving the Diagnostic Sensitivity of the Resulting
Fault Analyzer 47
2.3.8 Intermediate Assumption Deviations, Process Noise, and Process
Transients 48
2.4 Verifying the Validity and Accuracy of the Various Primary Models 49
2.5 Summary 51
References 52
3 Method of Minimal Evidence: Diagnostic Strategy Details 55
3.1 Overview 55
3.2 Introduction 56
3.3 MOME Diagnostic Strategy 57
3.3.1 Example of MOME SV&PFA Diagnostic Rules' Logic 57
3.3.2 Example of Key Performance Indicator Validation 67
3.3.3 Example of MOME SV&PFA Diagnostic Rules with Measurement
Redundancy 71
3.3.4 Example of MOME SV&PFA Diagnostic Rules for Interactive
Multiple-Faults 74
3.4 General Procedure for Developing and Verifying Competent Model-Based
Process Fault Analyzers 79
3.5 MOME SV&PFA Diagnostic Rules' Logic Compiler Motivations 80
3.6 MOME Diagnostic Strategy Summary  83 References 84
4 Method of Minimal Evidence: Fuzzy Logic Algorithm 87
4.1 Overview 87
4.2 Introduction 88
4.3 Fuzzy Logic Overview 90
4.4 MOME Fuzzy Logic Algorithm 91
4.4.1 Single-Fault Fuzzy Logic Diagnostic Rule 93
4.4.2 Multiple-Fault Fuzzy Logic Diagnostic Rule 97
4.5 Certainty Factor Calculation Review 102
4.6 MOME Fuzzy Logic Algorithm Summary 104
References 105
5 Method of Minimal Evidence: Criteria for Shrewdly Distributing Fault
Analyzers and Strategic Process Sensor Placement 109
5.1 Overview 109
5.2 Criteria for Shrewdly Distributing Process Fault Analyzers 109
5.2.1 Introduction 110
5.2.2 Practical Limitations on Target Process System Size 110
5.2.3 Distributed Fault Analyzers 112
5.3 Criteria for Strategic Process Sensor Placement 113
References 114
6 Virtual SPC Analysis and Its Routine Use in FALCONEERTM IV 117
6.1 Overview 117
6.2 Introduction 118
6.3 EWMA Calculations and Specific Virtual SPC Analysis Configurations 118
6.3.1 Controlled Variables 119
6.3.2 Uncontrolled Variables and Performance Equation Variables 120
6.4 Virtual SPC Alarm Trigger Summary 123
6.5 Virtual SPC Analysis Conclusions 124
References 124
7 Process State Transition Logic and Its Routine Use in FALCONEERTM IV 125
7.1 Temporal Reasoning Philosophy 125
7.2 Introduction 126
7.3 State Identification Analysis Currently Used in FALCONEERTM IV 128
7.4 State Identification Analysis Summary 131
References 131
8 Conclusions 133
8.1 Overview 133
8.2 Summary of the MOME Diagnostic Strategy 133
8.3 FALCON, FALCONEER, and FALCONEERTM IV Actual KBS Application
Performance Results 134
8.4 FALCONEERTM IV KBS Application Project Procedure 136
8.5 Optimal Automated Process Fault Analysis Conclusions 138
References 139
Appendix A Various Diagnostic Strategies for Automating Process Fault
Analysis 141
A.1 Introduction 141
A.2 Fault Tree Analysis 142
A.3 Alarm Analysis 143
A.4 Decision Tables 143
A.5 Sign-Directed Graphs 144
A.6 Diagnostic Strategies Based on Qualitative Models 145
A.7 Diagnostic Strategies Based on Quantitative Models 145
A.8 Artificial Neural Network Strategies 147
A.9 Knowledge-Based System Strategies 147
A.10 Methodology Choice Conclusions 148
References 149
Appendix B The FALCON Project 163
B.1 Introduction 163
B.2 Overview 164
B.3 The Diagnostic Philosophy Underlying the FALCON System 164
B.4 Target Process System 165
B.5 The FALCON System 167
B.5.1 The Inference Engine 168
B.5.2 The Human-Machine Inference 169
B.5.3 The Dynamic Simulation Model 169
B.5.4 The Diagnostic Knowledge Base 172
B.6 Derivation of the FALCON Diagnostic Knowledge Base 173
B.6.1 First Rapid Prototype of the FALCON System KBS 173
B.6.2 FALCON System Development 173
B.6.3 The FALCON System's Performance Results 182
B.7 The Ideal FALCON System 183
B.8 Use of the Knowledge-Based System Paradigm in Problem Solving 184
References 185
Appendix C Process State Transition Logic Used by the Original FALCONEER
KBS 187
C.1 Introduction 187
C.2 Possible Process Operating States 187
C.3 Significance of Process State Identification and Transition
Detection 189
C.4 Methodology for Determining Process State Identification 189
C.4.1 Present-Value States of All Key Sensor Data 189
C.4.2 Predicted Next-Value States of All Key Sensor Data 190
C.5 Process State Identification and Transition Logic Pseudocode 191
C.5.1 Attributes of the Current Data Vector 191
C.5.2 Method Applied to Each Data Vector 192
C.6 Summary 196
Appendix D FALCONEERTM IV Real-Time Suite Process Performance Solutions
Demos 197
D.1 FALCONEERTM IV Demos Overview 197
D.2 FALCONEERTM IV Demos 197
D.2.1 Wastewater Treatment Process Demo 197
D.2.2 Pulp and Paper Stock Chest Demo 199
Index 203