Traditional track geometry fault diagnosis methods, such as track geometry car, track detection trolley and manual measurement, suffer from low detection frequency. Therefore, using vibration data from in-service metro vehicles to diagnose metro track geometry faults can improve the efficiency of metro track condition detection. Hence, a metro track geometry fault diagnosis model using car-body vibration was established in this study based on a convolutional neural network model, and the effects of the structural hyperparameters on the model performance were analysed. First, the problem of car-body vibration data collection was addressed, and a mileage-locating model without the global navigation satellite system was developed. Then, the metro track geometry fault diagnosis model was derived using a convolutional neural network whose architecture is determined by four hyperparameters. Finally, a case study was performed using the Beijing Subway 1 track geometry car detection data and car-body vibration data. The results demonstrated the effectiveness of the proposed model.


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    Title :

    Development of metro track geometry fault diagnosis convolutional neural network model based on car-body vibration data


    Contributors:
    Wang, Zhipeng (author) / Liu, Rengkui (author) / Wang, Futian (author) / Tang, Yuanjie (author)


    Publication date :

    2022-10-01


    Size :

    10 pages




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




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