Fault diagnosis of hybrid dynamic and complex systems / Moamar Sayed-Mouchaweh, editor.
Online fault diagnosis is crucial to ensure safe operation of complex dynamic systems in spite of faults affecting the system behaviors. Consequences of the occurrence of faults can be severe and result in human casualties, environmentally harmful emissions, high repair costs, and economical losses...
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| Format: | eBook |
| Language: | English |
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Springer International Publishing,
2018.
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Table of Contents:
- Intro; Preface; Contents; 1 Prologue; 1.1 Hybrid Dynamic Systems: Definition, Classes, and Modeling Tools; 1.2 Fault Diagnosis of Hybrid Dynamic Systems: Problem Formulation, Methods, and Challenges; 1.3 Contents of the Book; 1.3.1 Chapter 2; 1.3.2 Chapter 3; 1.3.3 Chapter 4; 1.3.4 Chapter 5; 1.3.5 Chapter 6; 1.3.6 Chapter 7; 1.3.7 Chapter 8; 1.3.8 Chapter 9; 1.3.9 Chapter 10; References; 2 Motor Fault Detection and Diagnosis Based on a Meta-cognitive Random Vector Functional Link Network; 2.1 Introduction; 2.1.1 Induction Motor; 2.1.2 Hybrid Dynamic System; 2.1.3 Our Approach.
- 2.2 Fault Detection and Diagnosis in Induction Motors2.2.1 Fault Detection and Diagnosis Features in an Induction Motor; 2.2.2 Fault Detection Methods from Single and Multiple Sources; 2.3 eT2RVFLN Architecture; 2.3.1 Cognitive Architecture of an eT2RVFLN; 2.3.2 Meta-cognitive Learning Policy of the eT2RVFLN; 2.3.2.1 What to Learn; 2.3.2.2 How to Learn; 2.3.2.3 When to Learn; 2.4 Experimental Design; 2.5 Numerical Results; 2.6 Conclusion; References; 3 Optimal Adaptive Threshold and Mode Fault Detection for Model-Based Fault Diagnosis of Hybrid Dynamical Systems; 3.1 Introduction.
- 3.2 Bond Graph3.2.1 Hybrid Bond Graph (HBG) Model; 3.3 Diagnostic HBG Model for Uncertain System; 3.3.1 Modelling Parameter Uncertainty; 3.3.2 Modelling Measurement Uncertainty; 3.3.3 ARR/GARR and Adaptive Threshold; 3.3.4 Fault Signature Matrix and Coherence Vector; 3.3.5 Proposed Method for Optimal Threshold and Mode Fault Detection; 3.4 Case Study: Bench Mark Hybrid Two-Tank System; 3.4.1 ARRs/GARRs for Hybrid Two-Tank System; 3.4.2 Optimum Adaptive Threshold for Hybrid Two-Tank System; 3.4.3 FDI Study for Hybrid Two-Tank System Using Proposed Technique; 3.5 Conclusions; References.
- 4 Diagnosing Hybrid Dynamical Systems Using Max-Plus Algebraic Methods4.1 Introduction; 4.2 Problem Statement; 4.2.1 Hybrid Systems Model; 4.2.2 Objective; 4.2.3 System Architecture; 4.3 Related Work; 4.3.1 Algebraic Descriptions of Hybrid Systems; 4.3.2 Petri Net Models; 4.3.3 Diagnosing Hybrid Systems; 4.4 Behaviour Modeling: Switching Max-Plus Linear Systems; 4.4.1 Max-Plus Algebra; 4.4.2 Continuous Dynamics: Max-Plus Linear Systems; 4.4.3 Switching Max-Plus Linear Systems; 4.4.4 Stochastic SMPL Systems; 4.4.5 Generality of Approach; 4.5 Running Example; 4.5.1 Nominal Model.
- 4.5.2 Fault Model4.5.3 Max-Plus Model; 4.6 Diagnosing Hybrid Systems Using SMPL Automata; 4.6.1 Observers; 4.6.2 Isolating Faults; 4.7 Computational Complexity; 4.7.1 Fault Detection; 4.7.2 Fault Isolation; 4.7.3 Approximation Algorithm; 4.8 Diagnosis Scenarios; 4.8.1 Scenario 1: T3 Leak; 4.8.2 Scenario 2: V3 Blockage; 4.8.3 Scenario 3: V2 Blockage; 4.9 Types of Hybrid Systems Covered; 4.10 Summary; References; 5 Monitoring of Hybrid Dynamic Systems: Application to Chemical Process; 5.1 Introduction; 5.2 Residual Generation by the Extended Kalman Filter.