Leveraging machine learning to predict rail corrugation level from axle-box acceleration measurements on commercial vehicles

Rail corrugation is a prominent degradative problem in the health monitoring of railway systems. Monitoring process is dependent on use of a diagnostic trolley, which is expensive and needs the track to be out-of-service. Alternatively, in-service rail vehicles with Axle-Box Acceleration measurement systems installed, have shown success in detecting rail corrugation levels based on physical models, albeit with limitations. Extending this approach, we build a Machine Learning model, represented by a tuned Random Forest regressor, trained on collected accelerometer signals along with other offline and/or static features. We also propose a method to engineer acceleration-based features which nullifies the aggregated acceleration vibrations inherited from the other rail due to dynamically coupled vibrations between the left and right rails. The resulting model is able to recreate the moving RMS irregularity profile at bandwidth 100-300 mm, especially in highly corrugated sections, with an R-2 score of 0.97-0.98. The results show that the suggested data-driven approach outperforms a state-of-the-art model-based benchmark.