目录

●Chapter 1 Introduction
1.1 Motivation of the thesis
1.2 Objectives of the thesis
1.3 Outline of the thesis
Chapter 2 Overview of the Hierarchical Monitoring and Intelligent Diagnosis
Methods for Compound Faults
2.1 Research status of the hierarchical monitoring methods for compound
faults
2.1.1 The multivariate statistics based methods
2.1.2 The multi-block or decentralized monitoring methods
2.2 Research status of the compound fault diagnosis methods
2.2.1 The model based methods
2.2.2 The signal processing based methods
2.2.3 The traditional machine learning based methods
2.2.4 The deep learning based methods
Chapter 3 A Decentralized Detection Framework for Quality-related Faults
3.1 Preliminaries and problem formulation
3.1.1 Mutual information
3.1.2 Kernel principle component analysis
3.2 The decentralized quality-related fault detection method
3.2.1 DMKPCA based offline modeling
3.2.2 Bayesian fusion based online detection
3.3 Verification study.
3.3.1 Descriptions of the HRP
3.3.2 Case study for quality-related faults in RMP
3.3.3 Case study for quality-related faults in FMP
Chapter 4 A Dynamic Hierarchical Monitoring Method for Quality-related Compound Faults
4.1 Preliminaries and problem formulation
4.2 The proposed dynamic hierarchical monitoring method
4.2.1 CCVA based offline modeling
4.2.2 Bayesian inference based online monitoring
4.3 Verification study
4.3.1 Case study for independent compound faults 1 and 3
4.3.2 Case study for independent compound faults 2 and 3
Chapter 5 A Nonlinear and Dynamic Hierarchical Monitoring Method for Quality-related Compound Faults
5.1 Preliminaries and problem formulation
5.2 The proposed nonlinear and dynamic hierarchical monitoring method
5.2.1 AKCVA based offline modeling
5.2.2 Bayesian inference based online monitoring
5.3 Verification study
5.3.1 Parameter setting
5.3.2 Hierarchical monitoring results
Chapter 6 A Semisupervised Classification Framework for
Coupling Faults
6.1 Preliminaries and problem formulation
6.2 The semisupervised MTL method for coupling fault
classification
6.2.1 The proposed framework
6.2.2 Optimization
6.2.3 Convergence analysis
6.3 Verification study
6.3.1 Case study for coupling faults in RMP
6.3.2 Case study for coupling faults in FMP
……
Chapter 7 A Robust Semisupervised Classification Framework for Quality Related Coupling Faults
Chapter 8 A Multi-Iabel Classification Framework for Coupling Faults
Chapter 9 A Multi-task Learning Based Collaborative Modeling of Heterogeneous Data for Compound Fault Diagnosis
References