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

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...

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Published inIEEE transactions on vehicular technology Vol. 69; no. 9; pp. 9566 - 9576
Main Authors Cheng, Chao, Qiao, Xinyu, Luo, Hao, Wang, Guijiu, Teng, Wanxiu, Zhang, Bangcheng
Format Journal Article
LanguageEnglish
Published New York IEEE 01.09.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Algorithms
belief rule base
Circuit faults
Data-driven fault detection and diagnosis
deep slow feature analysis
Diagnostic systems
Fault detection
Fault diagnosis
Feature extraction
Gears
High speed rail
incipient fault
Interference
Multidimensional data
Predictive models
Principal component analysis
Railroad transportation
Running gear
the running gears
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Abstract 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.
AbstractList 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.
Author Wang, Guijiu
Teng, Wanxiu
Zhang, Bangcheng
Cheng, Chao
Luo, Hao
Qiao, Xinyu
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Snippet Incipient fault detection and diagnosis (FDD) is an important measure to improve the efficient, safe and stable operation of high-speed trains. This paper...
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SubjectTerms Algorithms
belief rule base
Circuit faults
Data-driven fault detection and diagnosis
deep slow feature analysis
Diagnostic systems
Fault detection
Fault diagnosis
Feature extraction
Gears
High speed rail
incipient fault
Interference
Multidimensional data
Predictive models
Principal component analysis
Railroad transportation
Running gear
the running gears
Title Data-Driven Incipient Fault Detection and Diagnosis for the Running Gear in High-Speed Trains
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