Diagnosis and Fault-tolerant Control, Volume 1: Data-driven and Model-based Fault Diagnosis Techniques

✍ Scribed by Vicenç Puig, Silvio Simani

• Publisher: Wiley-ISTE
• Year: 2022
• Tongue: English
• Leaves: 269
• Series: Systems and Industrial Engineering: Reliability, Diagnosis, Safety and Maintenance of Systems
• Category: Library

✦ Synopsis

This book presents recent advances in fault diagnosis strategies for complex dynamic systems. Its impetus derives from the need for an overview of the challenges of the fault diagnosis technique, especially for those demanding systems that require reliability, availability, maintainability and safety to ensure efficient operations. Moreover, the need for a high degree of tolerance with respect to possible faults represents a further key point, primarily for complex systems, as modeling and control are inherently challenging, and maintenance is both expensive and safety-critical.

Diagnosis and Fault-tolerant Control 1 also presents and compares different diagnosis schemes using established case studies that are widely used in related literature. The main features of this book regard the analysis, design and implementation of proper solutions for the problems of fault diagnosis in safety critical systems. The design of the considered solutions involves robust data-driven, model-based approaches.

✦ Table of Contents

Cover
Half-Title Page
Title Page
Copyright Page
Contents
Introduction
1. Mathematical Modeling and Fault Description
1.1. Introduction
1.2. Model-based FDI Techniques
1.3. Modeling of faulty systems
1.3.1. Fault modeling and description
1.3.2. Mathematical description
1.4. Residual generation
1.5. Residual generation techniques
1.5.1. Residual generation via parameter estimation
1.5.2. Observer-based approaches
1.5.3. Fault detection via parity equations
1.6. Change detection and symptom evaluation
1.7. Residual generation robustness problem
1.7.1. FDI H∞ approach
1.7.2. Active and passive disturbance de-coupling
1.8. Fault diagnosis technique integration
1.8.1. Fuzzy logic for residual generation
1.8.2. Neural networks for fault diagnosis
1.8.3. Neuro-fuzzy approaches to FDI
1.8.4. Fault detectability and isolability
1.8.5. NF model structure identification
1.8.6. NF residual generation for FDI
1.9. Conclusion
1.10. References
2. Structural Analysis
2.1. Introduction
2.2. Background
2.2.1. Structural models
2.2.2. Dulmage–Mendelsohn decomposition and matchings
2.2.3. Dulmage–Mendelsohn decomposition and simulation
2.3. Fault isolability analysis
2.3.1. Fault detectability analysis
2.3.2. Fault isolability analysis
2.3.3. Canonical isolability decomposition of the overdetermined part
2.4. Testable submodels
2.4.1. Basic definitions
2.4.2. MSO algorithm
2.4.3. Residual generation based on matching
2.5. Sensor placement
2.5.1. The basic sensor placement problem
2.5.2. A structural approach
2.6. Summary and discussion
2.7. References
3. Set-based Fault Detection and Isolation
3.1. Introduction
3.2. Notations, definitions and properties
3.3. Problem statement
3.3.1. Uncertain discrete-time linear systems
3.3.2. Set-based methods
3.3.3. FDI problem statement
3.4. Proposed techniques
3.4.1. Set-membership approach
3.4.2. Zonotopic observer
3.4.3. Relationship between set-based methods
3.5. Design methods
3.5.1. Robustness conditions
3.5.2. Fault sensitivity condition
3.6. Fault detection and isolation procedures
3.6.1. Fault detection
3.6.2. Fault isolation
3.7. Application example: quadruple-tank system
3.7.1. Results with robustness condition
3.7.2. Results with robustness and fault sensitivity conditions
3.8. Conclusion
3.9. References
4. Diagnosis of Stochastic Systems
4.1. Introduction
4.2. Stochastic diagnosis task
4.2.1. Notation
4.2.2. Problem formulation
4.2.3. Representing uncertainty
4.3. Inference methods for diagnosis task
4.3.1. Difference with other tasks
4.4. Model-based approach
4.4.1. Traditional FDD methods
4.4.2. Bayesian inversion/filtering
4.5. Data-driven approaches
4.5.1. ML methods
4.5.2. Statistical methods
4.6. Hybrid approaches: surrogate methods
4.6.1. Fitting surrogate models via sampling
4.7. Comparative analysis of approaches
4.8. Summary and conclusions
4.9. References
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
5.3.1. Data–driven method in ideal conditions
5.3.2. Data-driven methods in real scenarios
5.3.3. Algebraic Frisch scheme
5.3.4. Dynamic Frisch scheme
5.3.5. MIMO case Frisch scheme
5.4. Nonlinear dynamic system identification
5.4.1. Piecewise affine model
5.4.2. Hybrid model structure
5.4.3. Nonlinear system approximation
5.4.4. Model continuity and domain partitioning
5.4.5. Local affine model estimation
5.4.6. Multiple-model estimation
5.5. Fuzzy data-driven approach to fault diagnosis
5.5.1. Fuzzy model identification
5.5.2. Takagi–Sugeno prototypes
5.5.3. Data-driven Fuzzy modeling
5.5.4. Clustering methods
5.5.5. Fuzzy c-means clustering algorithms
5.5.6. Gustafson–Kessel clustering algorithm
5.5.7. Optimal number of clusters
5.6. Fuzzy model identification
5.6.1. Nonlinear model identification
5.6.2. Product space clustering identification
5.6.3. Fuzzy clustering model identification
5.6.4. Antecedent membership function estimation
5.6.5. Estimating consequent parameters
5.7. Conclusion
5.8. References
6. The Artificial Intelligence Approach to Model-based Diagnosis
6.1. Introduction
6.2. Case studies
6.3. Knowledge-based diagnosis systems
6.3.1. Diagnosis task and system model
6.3.2. Diagnosis of physical devices
6.3.3. Limits of KBS for diagnosis of physical devices
6.4. Model-based diagnosis
6.4.1. Formalization of consistency-based diagnosis and its first implementation, GDE
6.5. CBD for dynamic systems
6.5.1. Different approaches for CBD of dynamic systems
6.5.2. PCs for the three-tank system case study
6.6. Conclusion
6.7. References
List of Authors
Index
Summary of Volume 2