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
7.1 Preliminaries and problem formulation 7.2 The proposed robust semisupervised classification method 7.2.1 The proposed framework 7.2.2 Optimization 7.2.3 Convergence analysis 7.3 Verification study 7.3.1 Parameter setting 7.3.2 Case study for width - related coupling faults in RMP 7.3.3 Case study for thickness - related coupling faults in FMP Chapter 8 A Multi - label Classification Framework for Coupling Faults 8.1 Preliminaries and problem formulation 8.2 The proposed multi - label classification method 8.2.1 The proposed framework 8.2.2 Optimization 8.2.3 Convergence analysis 8.3 Verification study 8.3.1 Case study for coupling faults in RMP 8.3.2 Case study for coupling faults in FMP Chapter 9 A Multi - task Learning Based Collaborative Modeling of Heterogeneous Data for Compound Fault Diagnosis 9.1 The MTL based collaborative modeling framework 9.1.1 The MTL based collaborative modeling method 9.1.2 Optimization and solution 9.1.3 The attention based feature fusion network for compound fault diagnosis 9.2 Verification study 9.2.1 Case study for compound fault Ⅰ 9.2.2 Case study for compound fault Ⅱ References
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