Data-Driven Incipient Fault Detection and Diagnosis for the Running Gear in High-Speed Trains

被引：29

作者：

Cheng, Chao ^{[1
,2
,3
]}

Qiao, Xinyu ^{[1
]}

Luo, Hao ^{[4
]}

Wang, Guijiu ^{[3
]}

Teng, Wanxiu ^{[3
]}

Zhang, Bangcheng ^{[5
]}

机构：

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

[2] Tsinghua Univ, Dept Automat, Beijing 100084, Peoples R China

[3] CRRC Changchun Railway Vehicles Co Ltd, Natl Engn Lab, Changchun 130062, Peoples R China

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

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

来源：

IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY | 2020年 / 69卷 / 09期

基金：

中国国家自然科学基金;

关键词：

Gears; Fault detection; Circuit faults; Feature extraction; Predictive models; Interference; Principal component analysis; Data-driven fault detection and diagnosis; incipient fault; the running gears; deep slow feature analysis; belief rule base; BELIEF RULE BASE; SYSTEM; MODEL;

D O I：

10.1109/TVT.2020.3002865

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：

Incipient fault detection and diagnosis (FDD) is an important measure to improve the efficient, safe and stable operation of high-speed trains. This paper proposes a data-driven FDD method, namely deep slow feature analysis and belief rule base method (DSFA-BRB), for the running gears of high-speed trains. The method uses two kinds of statistics to perform fault detection on the multi-dimensional data of the running gears. In addition, the characteristics of more accurate data are extracted, which greatly reduces the complexity of constructing a diagnostic and quantitative model. Further, by constructing a BRB model combining expert knowledge and data, it is possible to avoid misjudgment caused by data incompleteness. Compared with the traditional methods, the DSFA-BRB algorithm has better performance in reducing fault alarm probability. Finally, the validity of the algorithm is verified by the actual running gears system.

引用

页码：9566 / 9576

页数：11

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