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内容大纲
本专著围绕热轧过程复合故障检测与诊断中的共性难点问题(运行状态监测数据繁多,难描述;强噪声下复合故障信号微弱,难获取;复合故障特征可分性弱,难诊断),针对其对应的科学问题(系统未知扰动与强噪声下的复合故障检测;多重并发与耦合故障信息下的复合故障诊断),重点论述了融合层级信息的全流程分解与建模、强噪声下的复合故障自主检测、自上而下的复合故障精准诊断等内容,最后在实际系统中验证了上述理论与方法的正确性和可行性。 -
作者介绍
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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
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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