LSTM-Based Temperature Prediction for Hot-Axles of Locomotives

Abstract

The reliability of locomotives plays a central role for the smooth operation of railway systems. Hot-axle failures are one of the most commonly found problems leading to locomotive accidents. Since the operating status of the locomotive axle bearings can be distinctly reflected by the axle temperatures, online temperature monitoring has become an essential way to detect hot-axle failures. In this work, we explore the feasibility of predict the hot-axle failures by identifying the temperature from predicted nominal values. We propose a data-driven approach based on the Long Short-Term Memory (LSTM) network to predict the sensor temperature for axle bearings. The effectiveness of the prediction model was validated with operation data collected from commercial locomotives. With a prediction accuracy is within a few percent, the proposed techniques can be used as a dynamic reference for hot-axle monitoring.