Abstract
The body of high-speed train is the most closely related part with passengers in the process of train operation. Traction motor is not only the power source of high-speed train, it is also the basic unit form that can reflect the reliable and stable operation of high-speed trains. The running safety of the train can be effectively protected by diagnosing the traction fault of the train. Based on research data on the development process of high-speed rail, this paper introduces the importance and pertinence of this research. Based on the study of separable linear SVM and nonlinear SVM, the composite fault diagnosis for traction motor using SVM technology is realized. After discussing the actual faults of current sensor and position sensor and their causes, a dynamic model based on traction motor is established and its parameters are simulated. Finally, a diagnostic observer is designed based on an example to determine the sensitivity of the composite fault to the high-speed rail traction motor, and experiments are designed to verify the detection results. Finally, this paper concludes by describing, and then points out some deficiencies and shortcomings of the research, which guides the direction and lays the data foundation for the subsequent research work. This paper studies the load fault of traction motor through a comprehensive analysis of SVM and sensor technology, which improves the effectiveness and practical efficiency of fault detection.
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Funding
The study was supported by “The national science fund for distinguished young scholars (Grant No.62001079); Science and Technology Innovation Program of Higher Education Institutions (Grant No.2022L431); Basic Research Program of Shanxi Province (Grant No.202303021211325); Datong Key R&D Program Project (Grant No.2022002); Shanxi Datong University scientific research projects (Grant No.2022K13)”.
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Li, Y., Li, F., Lu, C. et al. Composite fault diagnosis of traction motor of high-speed train based on support vector machine and sensor. Soft Comput 27, 8425–8435 (2023). https://doi.org/10.1007/s00500-023-08140-w
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DOI: https://doi.org/10.1007/s00500-023-08140-w