The book provides fault detection and diagnosis approaches from the perspective of filtering analysis. In order to design fault detection filters, it uses set-membership principles to deal with the unknown but bounded noise term. Some regular geometric spaces are introduced, such as the ellipsoid, polyhedron, interval, to describe the feasible parameter sets of the given system. Both principles and engineering practice have been addressed, with more weight placed on engineering practice. Some typical application cases are studied for fault detection and diagnosis in detail, which are power converter, permanent magnet synchronous motor, pitch system of wind turbine. Given its scope, the book offers a valuable guide for students, teachers, engineers and researchers in the field of fault detection and diagnosis.
Author(s): Ziyun Wang, Yan Wang, Zhicheng Ji
Publisher: Springer
Year: 2021
Language: English
Pages: 197
City: Singapore
Preface I
Preface II
Advances in Fault Detection and Diagnosis Using Filtering Analysis
Contents
Symbol Description
List of Figures
List of Tables
1 Introduction
1.1 Fault Detection and Diagnosis Problem
1.2 Classification of Fault Detection and Diagnosis Methods
1.2.1 Analytical Model-Based Method
1.2.2 Knowledge-Based Method
1.2.3 Signal-Processing-Based Method
1.3 Fault Classification
1.4 An Overview of Fault Diagnosis Process
1.4.1 Fault Detection
1.4.2 Fault Isolation
1.4.3 Fault Identification
1.5 Summary of Filtering Methods
1.6 Motivation and Objective
1.7 Outlines
References
2 Design of State Space Based Fault Diagnosis Filter
2.1 Preliminaries and Problem Formulation
2.2 Fault Diagnosis Based on Inverse Kalman Filter
2.3 Application Study
2.4 Concluding Remarks
References
3 Design of Ellipsoid Set-Membership Based Fault Detection Filter
3.1 Preliminaries and Problem Formulation
3.2 Process of Ellipsoid Set-Membership Method
3.3 Finite Data Window Algorithm
3.4 Illustrative Simulations
3.5 Concluding Remarks
References
4 Design of Polyhedron Set-Membership Based Fault Detection Filter
4.1 Preliminaries and Problem Formulation
4.2 Polyhedral Cone and the Vertices
4.3 Multi-objective Linear Programming
4.4 Illustrative Simulations
4.5 Application Study
4.6 Concluding Remarks
References
5 Design of Interval Set-Membership Based Fault Detection Filter
5.1 Preliminaries and Problem Formulation
5.2 SIVIA Approach
5.2.1 Interval Analysis
5.2.2 Set Inversion
5.3 Vector Set Inversion Interval Filter
5.4 Illustrative Simulations
5.5 Application Study
5.6 Concluding Remarks
References
6 Design of Orthotopic Set-Membership Based Fault Diagnosis Filter
6.1 Preliminaries and Problem Formulation
6.2 Orthotope and Its Center
6.3 Linear Programming
6.4 Orthotopic Spatial Extension
6.5 Orthotopic-filtering-based Fault Diagnosis Algorithms
6.5.1 Fault Detection Criterion
6.5.2 Fault Isolation and Identification
6.6 Hierarchical Fault Diagnosis
6.6.1 Fault Detection
6.6.2 Fault Identification
6.7 Illustrative Simulations
6.8 Application Study
6.9 Concluding Remarks
References
7 Fault Diagnosis Method Based on Composite Set-Membership Filter
7.1 Preliminaries and Problem Formulation
7.2 Directional Expansion Based Fault Diagnosis Algorithm …
7.2.1 Fault Detection
7.2.2 Fault Isolation
7.2.3 Fault Identification
7.3 Orthotopic Double Filtering Based State Estimation Algorithm
7.3.1 Prediction Step
7.3.2 Update Step
7.4 Illustrative Simulation
7.5 Application Study
7.5.1 Application Case 1
7.5.2 Application Case 2
7.5.3 Application Case 3
7.5.4 Application Case 4
7.6 Concluding Remarks
References
8 Summary
