内容简介：　　

　This book systematically presents a comprehensive framework and effective techniques for in-depth analysis, clear design procedure, and efficient implementation of diagnosis and prognosis algorithms for hybrid systems. It offers an overview of the fundamentals of diagnosis\prognosis and hybrid bond graph modeling. This book also describes hybrid bond graph-based quantitative fault detection, isolation and estimation. Moreover, it also presents strategies to track the system mode and predict the remaining useful life under multiple fault condition. A real world complex hybrid system—a vehicle steering control system—is studied using the developed fault diagnosis methods to show practical significance.
  Readers of this book will benefit from easy-to-understand fundamentals of bond graph models, concepts of health monitoring, fault diagnosis and failure prognosis, as well as hybrid systems. The reader will gain knowledge of fault detection and isolation in complex systems including those with hybrid nature, and will learn state-of-the-art developments in theory and technologies of fault diagnosis and failure prognosis for complex systems.


英文目录：
1 Health Monitoring of Engineering Systems
　　1.1 Condition Based Maintenance
　　1.2 Fault Diagnosis Tasks and Methodologies
　　　　1.2.1 Fault Diagnosis Tasks
　　　　1.2.2 Fault Diagnosis Methodologies
　　1.3 Failure Prognosis Tasks and Methodologies
　　　　1.3.1 Failure Prognosis Tasks
　　　　1.3.2 Failure Prognosis Methodologies
　　1.4 Organization of the Book
　　References
2 Hybrid Systems and Hybrid Bond Graph Models
　　2.1 Hybrid Systems
　　2.2 Modeling Methods for Hybrid Systems
　　2.3 Basics of Bond Graph
　　　　2.3.1 Bonds, Power and Causality
　　　　2.3.2 Bond Graph Elements
　　　　2.3.3 Causality of Basic Bond Graph Elements
　　　　2.3.4 Sequential Causality Assignment Procedure
　　　　2.3.5 Example of a Quarter Car System Modeling
　　2.4 Hybrid Bond Graph
　　　　2.4.1 Causality Properties and Causality Assignment for HBG
　　　　2.4.2 Illustrative Examples
　　References
3 Quantitative Hybrid Bond Graph-Based Fault Detection and Isolation
　　3.1 Introduction
　　3.2 Bond Graph-Based Fault Diagnosis
　　　　3.2.1 Analytical Redundancy Relationships
　　　　3.2.2 Residual Evaluation and Fault Signature Matrix
　　　　3.2.3 Generation of ARRs
　　3.3 Hybrid Bond Graph-Based Fault Diagnosis
　　　　3.3.1 Causality Assignment from FDI Perspective
　　　　3.3.2 Global Analytical Redundancy Relationships
　　　　3.3.3 Fault Detectability and Isolability Analysis
　　　　3.3.4 Case Study
　　References
4 Fault Identification Techniques
　　4.1 Nonlinear Least Square Optimization for Fault Identification
　　　　4.1.1 Nonlinear Least Square Method
　　　　4.1.2 Example: A Nonlinear Hybrid Electrical System
　　4.2 Simultaneous Fault Parameter and Mode Change Identification
　　　　4.2.1 Parametrization of Mode Changes
　　　　4.2.2 Simultaneous Fault Parameter and Mode Switching Identification
　　　　4.2.3 Example I: An Electro-Hydraulic Suspension
　　　　4.2.4 Example II: A Hybrid Electrical System
　　References
5 Mode Tracking Techniques
　　5.1 Mode Tracking of Hybrid Systems in FDI Framework
　　　　5.1.1 Mode Change Signatures of a Hybrid System
　　　　5.1.2 ARR-Based Mode Change Identification
　　　　5.1.3 Illustrative Example
　　5.2 Mode Identification of Hybrid Systems in the Presence of Fault
　　　　5.2.1 Rule-Based Analysis of ARRs
　　　　5.2.2 Implementation Schemes and Algorithms
　　　　5.2.3 From Theory to Implementation
　　　　5.2.4 Experimental Study
　　References
6 Application of Real Time FDI and Fault Estimation to a Vehicle Steering System
　　6.1 Introduction
　　6.2 Description of the Vehicle Steering System
　　　　6.2.1 The Electro-Hydraulic Steering System
　　　　6.2.2 Faults Under Consideration
　　6.3 FDI Approach for the Front Steering System
　　　　6.3.1 DHBG Model of the Electro-Hydraulic Steering System
　　　　6.3.2 Development of GARRs
　　　　6.3.3 FDI Approach
　　6.4 Experiment Study
　　　　6.4.1 Experimental Hardware and Software
　　　　6.4.2 Result and Analysis
　　References
7 Multiple Failure Prognosis for Hybrid Systems
　　7.1 Prognosis of Multiple Incipient Faults
　　　　7.1.1 Augmented Global Analytical Redundancy Relations
　　　　7.1.2 Degradation Models
　　　　7.1.3 Particle Swarm Optimization for Prognosis
　　　　7.1.4 Illustrative Example
　　7.2 Prognosis with Mode-Dependent Degradation Behaviors
　　　　7.2.1 Dynamic Fault Isolation
　　　　7.2.2 Mode-Dependent Degradation Behaviors
　　　　7.2.3 Sequential Prognosis
　　　　7.2.4 Experiment Result
　　References