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The research aims to develop a deep learning framework for automated classification of railway track faults using a 2D simulation model.

Railway Track Fault Classification for Predictive Maintenance Using Deep Learning Algorithms Based on a 2D Train‐Track‐Bridge Interaction Model

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Demonstrates deep learning algorithms for railway track fault classification, suggesting improvements for predictive maintenance.

The research aims to develop a deep learning framework for automated classification of railway track faults using a 2D simulation model.
Developed a 2D train-track-bridge interaction simulation model.
Computed vertical acceleration for the train vehicle and bogie using MATLAB.
Utilized continuous wavelet transform (CWT) and short time Fourier transform (STFT) for analyzing time-frequency signatures.
Created 51,000 labeled samples categorizing faults into corrugation, ballast discontinuity, and healthy conditions.
Trained various deep learning models including LSTM, 1D-CNN, GRU, and MLP.
Achieved 98% accuracy using the LSTM model for fault classification.
Obtained an F1 score of 97%, validating precision and recall across all classes.
Demonstrated the effective integration of numerical simulations with deep learning for fault diagnosis.

Memon et al. (2026) studied this question.

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