Predictive Maintenance of Railway Tracks Using LSTM

Railway tracks are the most prominent base of train movement, any defect in the track may lead to minor/major unhappening. To minimize risk and maximize safety, regular maintenance or condition-based maintenance must be performed. In this paper, a deep learning-based algorithm is proposed for automatic fault detection in railway tracks using LSTM (Long Short-Term Memory). LSTM model, an advanced version of recurrent neural networks known for their accuracy are compared with existing Deep Learning techniques to identify common railway faults, such as defects in rails and fasteners. Predictive maintenance is used to perform required maintenance before the failure of tracks by estimating RUL (Remaining Useful Life) of tracks and their components. This Proposed model is applied to the trusted source dataset which detects three defect types (DIP, XLEVEL, SURFACE). This approach enhances proactive maintenance, reduces downtime, optimizes schedules, and improves railway system reliability and safety. Our research advances predictive maintenance practices, showcasing how LSTM networks can transform railway infrastructure maintenance strategies.