High-speed railway stations adopt track circuits equipped with impedance match bonds to effectively suppress the interference of unbalanced traction currents on the signaling system. Concerning the harsh working environment of the impedance match bonds, the present manual experience method for trouble shoot will inevitably affect the operation efficiency and safety. Under this background, this paper proposes a fault diagnosis method considering the impact of hidden risks on railway safety. This novel method is based on the risk assessment of different hazards to define the types of safety-related faults, selects the radial basis function kernel to establish a support vector machine prediction model, and optimizes the parameters through the differential evolution algorithm. Further, making use of the characteristic under different working scenarios of equipment in data processing, the fault diagnosis of the impedance match bonds is designed. The verification results based on measured data demonstrate that the high-risk fault diagnosis integrated model proposed in this paper can achieve 100% prediction accuracy under the given data set, realize accurate fault identification, and is helpful for safety maintenance and risk management of high-speed railway systems.


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    Titel :

    Fault Diagnosis of Impedance Match Bond in High-Speed Railway Concerning Risk Assessment


    Weitere Titelangaben:

    Lect. Notes Electrical Eng.


    Beteiligte:
    Qin, Yong (Herausgeber:in) / Jia, Limin (Herausgeber:in) / Liang, Jianying (Herausgeber:in) / Liu, Zhigang (Herausgeber:in) / Diao, Lijun (Herausgeber:in) / An, Min (Herausgeber:in) / Liu, Chang (Autor:in) / Chu, Shaotong (Autor:in) / Yang, Shiwu (Autor:in) / Han, Guangxin (Autor:in)

    Kongress:

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



    Erscheinungsdatum :

    2022-02-23


    Format / Umfang :

    12 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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