Optimising Wheel Replacement Schedules for Freight Trains based on Machine Learning Models

Efficient maintenance planning is crucial for railway operators to minimise costs and operational disruptions. Railway freight providers currently replace train wheels either during scheduled maintenance or as reactive repairs, leading to inefficiencies. This paper explores the application of machine learning to predictive maintenance by analysing sensor data from Dynamic Pressure Check (DPC) detectors mounted along railway tracks. These detectors measure the force impacts as the train moves, providing key indicators of wheel wear. Here, machine-learning-based predictive models with uncertainty quantification are developed to detect damaged train wheels. The model achieved R2 > 0.98 with RMSEs of 0.08 kilonewton for the left wheel and 0.36 kilonewton for the right wheel. Further, forecasting trends over a three-year horizon identified one axle requiring replacement, with left wheels typically failing first. A maintenance scheduling strategy was implemented in which both wheels on an axle are replaced when either wheel exceeds predefined thresholds. This work demonstrates the potential of predictive maintenance to improve efficiency and reduce costs in railway operations by applying eXtreme Gradient Boosting (XGBoost), Isolation Forest, and Linear regression methods to historical data from DPC detectors.