With the rapid construction and development of high-speed railways, high-speed railway accidents are common. Ensuring the reliability, availability, maintainability, and safety of trains is an important technical issue at present. The train auxiliary power supply system is an important subsystem to ensure the safety and comfort of passengers. By studying the fault prediction model of the auxiliary power supply system, it can provide a strong guarantee for the normal operation of the system and the safe and normal operation of China’s high-speed railways. This paper proposes a data-driven auxiliary power supply system fault diagnosis and prediction model based on deep neural network LSTM, which is used for auxiliary power supply system fault prediction. At the same time, this paper establishes a fault diagnosis system based on BP neural network, which is in contrast with the former. The experimental results show that the LSTM-based framework provided in this paper is a feasible method for fault diagnosis and prediction of auxiliary systems.


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    Title :

    Research on Fault Prediction of High-Speed Train Auxiliary Power Supply System Based on LSTM


    Additional title:

    Lect. Notes Electrical Eng.


    Contributors:
    Qin, Yong (editor) / Jia, Limin (editor) / Liang, Jianying (editor) / Liu, Zhigang (editor) / Diao, Lijun (editor) / An, Min (editor) / Wang, Zhuo (author) / Dong, Honghui (author) / Man, Jie (author) / Jia, Liming (author)

    Conference:

    International Conference on Electrical and Information Technologies for Rail Transportation ; 2021 October 21, 2021 - October 23, 2021



    Publication date :

    2022-02-23


    Size :

    9 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




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