Broken rail monitoring equipment is essential for ensuring the safety of train operations, and it is necessary to conduct functional and performance testing before the equipment is put into operation. Considering the safety and order of line operations, laboratory testing methods are typically used, employing simulation boards to mimic actual rails. Since the rail is an inductive element, its impedance characteristics are closely related to the frequency of the electrical signal. Accurately simulating the impedance transmission characteristics of the rail under different scenarios and conditions is vital to implementing these testing. To achieve this, we utilize real measured data of rail impedance under different scenarios and frequencies, and establish an equivalent model of rail impedance characteristics using ensemble learning algorithms. This model takes the application conditions of the broken rail monitoring equipment as input and determines the impedance parameters of the signal during rail transmission. Based on this, a rail simulation board can be designed, and a track circuit and broken rail monitoring equipment testing platform can be set up, enabling scientific and efficient testing in the laboratory.


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    Title :

    Research on Testing Method for Broken Rail Monitoring Equipment Based on Ensemble Learning


    Contributors:
    Wang, Qi (author) / Liu, Congcong (author) / Xing, Yulong (author) / Zhao, Yu (author)


    Publication date :

    2024-11-15


    Size :

    602767 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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