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There is an increasing demand for dynamic systems to become safer and more reliable. This requirement extends beyond the normally accepted safety-critical systems such as nuclear reactors and aircraft, where safety is of paramount importance, to systems such as autonomous vehicles and process control systems where the system availability is vital. It is clear that fault diagnosis is becoming an important subject in modern control theory and practice. Robust Model-Based Fault Diagnosis for Dynamic Systems presents the subject of model-based fault diagnosis in a unified framework. It contains many important topics and methods; however, total coverage and completeness is not the primary concern. The book focuses on fundamental issues such as basic definitions, residual generation methods and the importance of robustness in model-based fault diagnosis approaches. In this book, fault diagnosis concepts and methods are illustrated by either simple academic examples or practical applications. The first two chapters are of tutorial value and provide a starting point for newcomers to this field. The rest of the book presents the state of the art in model-based fault diagnosis by discussing many important robust approaches and their applications. This will certainly appeal to experts in this field. Robust Model-Based Fault Diagnosis for Dynamic Systems targets both newcomers who want to get into this subject, and experts who are concerned with fundamental issues and are also looking for inspiration for future research. The book is useful for both researchers in academia and professional engineers in industry because both theory and applications are discussed. Although this is a research monograph, it will be an important text for postgraduate research students world-wide. The largest market, however, will be academics, libraries and practicing engineers and scientists throughout the world.
Show moreThere is an increasing demand for dynamic systems to become safer and more reliable. This requirement extends beyond the normally accepted safety-critical systems such as nuclear reactors and aircraft, where safety is of paramount importance, to systems such as autonomous vehicles and process control systems where the system availability is vital. It is clear that fault diagnosis is becoming an important subject in modern control theory and practice. Robust Model-Based Fault Diagnosis for Dynamic Systems presents the subject of model-based fault diagnosis in a unified framework. It contains many important topics and methods; however, total coverage and completeness is not the primary concern. The book focuses on fundamental issues such as basic definitions, residual generation methods and the importance of robustness in model-based fault diagnosis approaches. In this book, fault diagnosis concepts and methods are illustrated by either simple academic examples or practical applications. The first two chapters are of tutorial value and provide a starting point for newcomers to this field. The rest of the book presents the state of the art in model-based fault diagnosis by discussing many important robust approaches and their applications. This will certainly appeal to experts in this field. Robust Model-Based Fault Diagnosis for Dynamic Systems targets both newcomers who want to get into this subject, and experts who are concerned with fundamental issues and are also looking for inspiration for future research. The book is useful for both researchers in academia and professional engineers in industry because both theory and applications are discussed. Although this is a research monograph, it will be an important text for postgraduate research students world-wide. The largest market, however, will be academics, libraries and practicing engineers and scientists throughout the world.
Show more1. 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.
Springer Book Archives
`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)
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