Synchronisation of On-Board Track Geometry Monitoring Signals to Enable Machine Learning Predictions

Accurate detection and prediction of railway track geometry defects is critical for ensuring safety, reliability and efficiency in rail operations. In Europe, traditional track geometry monitoring relies heavily on diagnostic trains, which are costly and operate infrequently if compared with commercial trains. This work presents a novel methodology to synchronise signals from onboard monitoring using sensors installed on commercial trains, as a part of the data cleansing needed to prepare for Machine Learning prediction of track geometry evolution. The sensor setup utilises accelerometers, gyroscopes and Global Navigation Satellite System (GNSS) to estimate track geometry features, but it faces inherent challenges due to inevitable differences in sensor placement between runs, different starting points for data recording and GNSS misalignments, particularly in tunnels. To overcome these challenges, an innovative data synchronisation methodology is introduced, leveraging cross-level references combined with a peak finder algorithm to ensure precise alignment across multiple recordings. This synchronisation is crucial for accurately tracking the temporal evolution of defects at specific locations, enabling effective predictive maintenance through advanced machine learning and AI technologies. By integrating AI-driven models with synchronised data, this work supports enhancements of the accuracy of defect detection and prediction, in a view to establish a scalable, data-driven approach for railway maintenance.