This study addresses the issues of accuracy and efficiency in train wheel defect detection by proposing a method based on ROCKET and LightGBM. Using wheel pressure time-series data obtained from sensors, the ROCKET algorithm is employed for time-series feature extraction. Combined with the LightGBM model and Bayesian optimization techniques, an effective defect detection method is established. Experimental results validate that this method significantly improves detection accuracy, providing robust technical support for railway transportation safety.


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    Title :

    Enhancing Train Wheel Defect Detection with ROCKET Feature Extraction and LightGBM


    Contributors:
    Su, Kangyou (author) / Li, Chaoming (author) / Yu, Guoqing (author) / Yu, Hongling (author) / Zheng, Zhiying (author)


    Publication date :

    2024-07-26


    Size :

    1175896 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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