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- W4210344025 abstract "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." @default.
- W4210344025 created "2022-02-08" @default.
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- W4210344025 date "2022-03-28" @default.
- W4210344025 modified "2023-10-15" @default.
- W4210344025 title "Development of metro track geometry fault diagnosis convolutional neural network model based on car-body vibration data" @default.
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- W4210344025 doi "https://doi.org/10.1177/09544097221080366" @default.
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