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I. La Paglia, H. Tsunashima, "Data-Driven Forecasting of Track Geometry Degradation", in J. Pombo, (Editor), "Proceedings of the Seventh International Conference on Railway Technology: Research, Development and Maintenance ", Civil-Comp Press, Edinburgh, UK, Online volume: CCC 15, Paper 7.2, 2026, doi:10.4203/ccc.15.7.2

Keywords: railway track geometry, condition monitoring, vehicle accelerations, longitudinal level, time-series, forgetting factor.

Abstract

This paper presents a methodology to monitor the temporal evolution of the track longitudinal level. It is designed to be integrated into a condition monitoring system installed on in-service vehicles, and it relies on the sequential analysis of degradation time series. To estimate the expected growth of a defect, time-weighted linear regressions with different forgetting factors are computed at each iteration. This way, a probability density function of degradation trajectories is generated. Given predefined threshold amplitudes associated to maintenance interventions, the proposed methodology allows to predict the threshold-crossing times. The application to different defects recorded along the monitored railway line shows that the method captures non-linear degradation trends and progressively refines the forecast of the threshold-crossing time when new data are available. When implemented on in-service vehicles, the proposed strategy can provide useful information to support maintenance decisions.

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Front Page Browse CCC CCP CSETS CTR IJRT Other Authors Search Purchase Guide FAQ Contact us	Civil-Comp Conferences ISSN 2753-3239 CCC: 15 PROCEEDINGS OF THE SEVENTH INTERNATIONAL CONFERENCE ON RAILWAY TECHNOLOGY: RESEARCH, DEVELOPMENT AND MAINTENANCE Edited by: J. Pombo Paper 7.2 Data-Driven Forecasting of Track Geometry Degradation I. La Paglia¹ and H. Tsunashima² ¹Department of Mechanical Engineering, Politecnico di Milano, Italy ²College of Industrial Technology, Nihon University, Japan doi:10.4203/ccc.15.7.2 Full Bibliographic Reference for this paper I. La Paglia, H. Tsunashima, "Data-Driven Forecasting of Track Geometry Degradation", in J. Pombo, (Editor), "Proceedings of the Seventh International Conference on Railway Technology: Research, Development and Maintenance ", Civil-Comp Press, Edinburgh, UK, Online volume: CCC 15, Paper 7.2, 2026, doi:10.4203/ccc.15.7.2 Keywords: railway track geometry, condition monitoring, vehicle accelerations, longitudinal level, time-series, forgetting factor. Abstract This paper presents a methodology to monitor the temporal evolution of the track longitudinal level. It is designed to be integrated into a condition monitoring system installed on in-service vehicles, and it relies on the sequential analysis of degradation time series. To estimate the expected growth of a defect, time-weighted linear regressions with different forgetting factors are computed at each iteration. This way, a probability density function of degradation trajectories is generated. Given predefined threshold amplitudes associated to maintenance interventions, the proposed methodology allows to predict the threshold-crossing times. The application to different defects recorded along the monitored railway line shows that the method captures non-linear degradation trends and progressively refines the forecast of the threshold-crossing time when new data are available. When implemented on in-service vehicles, the proposed strategy can provide useful information to support maintenance decisions. download the full-text of this paper (PDF, 12 pages, 822 Kb) go to the previous paper go to the next paper return to the table of contents return to the volume description
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