Robust Model-Based Fault Diagnosis for Dynamic Systems

Robust Model-Based Fault Diagnosis for Dynamic Systems by Jie Chen, R.J. Patton.

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 s...

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Bibliographic Details
Main Authors: Jie Chen (Author), Patton, R.J (Author)
Corporate Author: SpringerLink (Online service)
Format: eBook
Language:English
Published: New York, NY : Springer US : Imprint: Springer, 1999.
Edition:1st ed. 1999.
Series:The International Series on Asian Studies in Computer and Information Science, 3
Springer eBook Collection.
Subjects:
Electrical engineering.
System theory.
Calculus of variations.
Microprocessors.
Automotive engineering.
Electronic resources (E-books)
Online Access:Click to view e-book
Holy Cross Note:Loaded electronically.
Electronic access restricted to members of the Holy Cross Community.
Table of Contents:
  • 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.

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