Robust Model-Based Fault Diagnosis for Dynamic Systems

There is an increasing demand for dynamic systems to become more safe and reliable. This requirement extends beyond the normally accepted safety-critical systems of nuclear reactors and aircraft where safety is paramount important, to systems such as autonomous vehicles and fast railways where the system availability is vital. It is clear that fault diagnosis (including fault detection and isolation, FDI) has been becoming an important subject in modern control theory and practice. For example, the number of papers on FDI presented in many control-related conferences has been increasing steadily. The subject of fault detection and isolation continues to mature to an established field of research in control engineering. A large amount of knowledge on model-based fault diagnosis has been ac cumulated through the literature since the beginning of the 1970s. However, publications are scattered over many papers and a few edited books. Up to the end of 1997, there is no any book which presents the subject in an unified framework. The consequence of this is the lack of "common language", dif ferent researchers use different terminology. This problem has obstructed the progress of model-based FDI techniques and has been causing great concern in research community. Many survey papers have been published to tackle this problem. However, a book which presents the materials in a unified format and provides a comprehensive foundation of model-based FDI is urgently needed.

Résumé
`From a research monograph point of view, this book is certainly a welcome addition to several recently published books in this important field of fault-tolerant control systems. The reviewer would like to congratulate both authors for the excellent job presented in this book.'
Automatica 38:1089-1094 (2002)

Contenu

1. Introduction.- 1.1 Background.- 1.2 Brief history of model-based fault diagnosis.- 1.3 Outline of the Book.- 2. Basic Principles of Model-Based FDI.- 2.1 Introduction.- 2.2 Model-based Fault Diagnosis Methods.- 2.3 On-line Fault Diagnosis.- 2.4 Modeling of Faulty Systems.- 2.5 A General Structure of Residual Generation in Model-based FDI.- 2.6 Fault Detectability.- 2.7 Fault Isolability.- 2.8 Residual Generation Techniques.- 2.9 Model-based FDI via Parameter Estimation.- 2.10 Fault Diagnosis for Stochastic Systems.- 2.11 Robust Residual Generation Problems.- 2.12 Adaptive Thresholds in Robust FDI.- 2.13 Applicability of Model-based FDI Methods.- 2.14 Integration of Fault Diagnosis Techniques.- 2.15 Summary.- 3. Robust Residual Generation Via Uios.- 3.1 Introduction.- 3.2 Theory and Design of Unknown Input Observers.- 3.3 Robust Fault Detection and Isolation Schemes based on UIOs.- 3.4 Robust Fault Detection Filters and Robust Directional Residuals.- 3.5 Filtering and Robust FDI of Uncertain Stochastic Systems.- 3.6 Summary.- 4. Robust FDI Via Eigenstructure Assignment.- 4.1 Introduction.- 4.2 Residual Generation and Responses.- 4.3 General Principle for Disturbance De-coupling Design.- 4.4 Disturbance De-coupling by Assigning Left Eigenvectors.- 4.5 Robust Design Via Parametric Eigenstructure Assignment.- 4.6 Disturbance De-coupling by Assigning Right Eigenvectors.- 4.7 Dead-Beat Design for Robust Residual Generation.- 4.8 Two Numerical Examples in Eigenstructure Assignment.- 4.9 Conclusion and Discussion.- 5. Disturbance Distribution Matrix Determination For FDI.- 5.1 Introduction.- 5.2 Direct Determination of Disturbance Distribution Matrix.- 5.3 Estimation of Disturbance and Disturbance Distribution Matrix.- 5.4 Optimal Distribution Matrix for Multiple Operating Points.- 5.5 Modeling and FDI for a Jet Engine System 153.- 5.6 Conclusion.- 6. Robust FDI Via Multi-Objective Optimization.- 6.1 Introduction.- 6.2 Residual Generation and Performance Indices.- 6.3 Parameterization In Observer Design.- 6.4 Multi-Objective Optimization and the Method of Inequalities.- 6.5 Optimization via Genetic Algorithms.- 6.6 Detection of Incipient Sensor Faults in Flight Control Systems.- 6.7 Conclusions.- 7. Robust Fdi Using Optimal Parity Relations.- 7.1 Introduction.- 7.2 Performance Indices for Optimal Parity Relation Design.- 7.3 Optimal Parity Relation Design via Multi-Objective Optimization.- 7.4 A Numerical Illustration Example.- 7.5 Discussion on Designing Optimal Parity Relations.- 7.6 Summary.- 8. Frequency Domain Design And H? Optimization For FDI.- 8.1 Introduction.- 8.2 Robust Fault Detection via Factorization Approach.- 8.3 Robust FDI Design via Standard H? Filtering Formulation.- 8.4 LMI Approach for Robust Residual Generation.- 8.5 Summary.- 9. Fault Diagnosis Of Non-Linear Dynamic Systems.- 9.1 Introduction.- 9.2 Linear and Non-linear Observer-based Approaches.- 9.3 Neural Networks in Fault Diagnosis of Non-linear Dynamic Systems.- 9.4 Fuzzy Observers for Non-linear Dynamic Systems Fault Diagnosis.- 9.5 A Neuro-Fuzzy Approach for Non-linear Systems FDI.- 9.6 Summary.- Appendices.- A- Terminology in Model-based Fault Diagnosis.- B- Inverted Pendulum Example.- C- Matrix Rank Decomposition.- D- Proof of Lemma 3.2.- E- Low Rank Matrix Approximation.- References.