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Table of Contents
1 Cover
5 1 Mathematical Modeling and Fault Description 1.1. Introduction 1.2. Model-based FDI techniques 1.3. Modeling of faulty systems 1.4. Residual generation 1.5. Residual generation techniques 1.6. Change detection and symptom evaluation 1.7. Residual generation robustness problem 1.8. Fault diagnosis technique integration 1.9. Conclusion 1.10. References
6 2 Structural Analysis 2.1. Introduction 2.2. Background 2.3. Fault isolability analysis 2.4. Testable submodels 2.5. Sensor placement 2.6. Summary and discussion 2.7. References
7 3 Set-based Fault Detection and Isolation 3.1. Introduction 3.2. Notations, definitions and properties 3.3. Problem statement 3.4. Proposed techniques 3.5. Design methods 3.6. Fault detection and isolation procedures 3.7. Application example: quadruple-tank system 3.8. Conclusion 3.9. References
8 4 Diagnosis of Stochastic Systems 4.1. Introduction 4.2. Stochastic diagnosis task 4.3. Inference methods for diagnosis task 4.4. Model-based approach 4.5. Data-driven approaches 4.6. Hybrid approaches: surrogate methods 4.7. Comparative analysis of approaches 4.8. Summary and conclusions 4.9. References
9 5 Data-Driven Methods for Fault Diagnosis 5.1. Introduction 5.2. Models for linear system fault diagnosis 5.3. Parameter estimation methods for fault diagnosis 5.4. Nonlinear dynamic system identification 5.5. Fuzzy data-driven approach to fault diagnosis 5.6. Fuzzy model identification 5.7. Conclusion 5.8. References
10 6 The Artificial Intelligence Approach to Model-based Diagnosis 6.1. Introduction 6.2. Case studies 6.3. Knowledge-based diagnosis systems 6.4. Model-based diagnosis 6.5. CBD for dynamic systems 6.6. Conclusion 6.7. References
12 Index
List of Illustrations
1 IntroductionFigure I.1. Comparison between hardware and analytical redundancy schemesFigure
