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For efficient railway operation and maintenance, the demand for onboardmonitoring systems is increasing with technological advances in high-speedtrains. Wheel flats, one of the common defects, can be monitored in real-timethrough accelerometers mounted on each axle box so that the criteria ofrelevant standards are not exceeded. This study aims to identify the locationand height of a single wheel flat based on non-stationary axle box acceleration(ABA) signals, which are generated through a train dynamics model with flexiblewheelsets. The proposed feature extraction method is applied to extract theroot mean square distribution of decomposed ABA signals on a balanced binarytree as orthogonal energy features using the Hilbert transform and waveletpacket decomposition. The neural network-based defect prediction model iscreated to define the relationship between input features and output labels.For insufficient input features, data augmentation is performed by the linearinterpolation of existing features. The performance of defect prediction isevaluated in terms of the accuracy of detection and localization and improvedby augmented input features and highly decomposed ABA signals. The results showthat the trained neural network can predict the height and location of a singlewheel flat from orthogonal energy features with high accuracy.

Defect Prediction of Railway Wheel Flats based on Hilbert Transform and Wavelet Packet Decomposition