Multiblock Dynamic Slow Feature Analysis-Based System Monitoring for Electrical Drives of High-Speed Trains

被引：13

作者：

Cheng, Chao ^{[1
]}

Qiao, Xinyu ^{[1
]}

Zhang, Bangcheng ^{[2
]}

Luo, Hao ^{[3
]}

Zhou, Yang ^{[4
]}

Chen, Hongtian ^{[5
]}

机构：

[1] Changchun Univ Technol, Sch Comp Sci & Engn, Changchun 130012, Peoples R China

[2] Changchun Univ Technol, Sch Mechatron Engn, Changchun 130012, Peoples R China

[3] Harbin Inst Technol, Dept Control Sci & Engn, Harbin 150001, Peoples R China

[4] TU Dortmund Univ, Inst Energy Syst Energy Efficiency & Energy Econ, D-44227 Dortmund, Germany

[5] Univ Alberta, Dept Chem & Mat Engn, Edmonton, AB T6G 2V4, Canada

来源：

IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT | 2021年 / 70卷

基金：

中国国家自然科学基金;

关键词：

Bayesian inference; dynamic slow feature analysis (DSFA); electrical drive systems; mutual information (MI); system monitoring; FAULT-DETECTION; DIAGNOSIS; DESIGN;

D O I：

10.1109/TIM.2021.3070593

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

The electrical drive system of high-speed trains is a key subsystem to ensure the continuous supply of train power and stable operation. By the use of local information, this article presents a method called multiblock dynamic slow feature analysis (MBDSFA) with its application in the electrical drive system of high-speed trains. First, the relevance among all variables of electrical drive systems is calculated by using mutual information, based on which all variables are divided into blocks. Then, the dynamic slow feature analysis-based system monitoring scheme is employed for each subblock, and the local characteristics of electrical drive systems are analyzed via two kinds of test statistics. All subblocks are integrated based on the Bayesian inference to obtain the global monitoring results. Finally, the effectiveness and feasibility of the proposed approach are verified through the case study on the electrical drive system of high-speed trains.

引用

页数：10

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