The increased demand for rail transportation requires continuous monitoring of both railway lines and vehicle dynamics for different reasons, ranging from system safety and reliability to effective management of maintenance interventions. The target of the article is that of proposing innovative methodologies for data processing and analysis of experimental signals collected onboard, during long-term monitoring of in-service vehicles. The proposed methodologies are suitable for various rail transportation systems, ranging from metro to main line. At first, a positioning algorithm is proposed to associate all the measured acceleration signals to the corresponding track sections where they were acquired. It consists in a map-matching procedure to project the GPS positions, stored by the train during commercial service, onto the digital map of the railway line. Once completed, synthetic indexes representative of vehicle dynamics performances, such as RMS acceleration values, can be computed and matched to their exact position along the track, allowing to investigate vehicle dynamics evolution both in time and with respect to the position along the railway line. Two different data analysis approaches are proposed: the first one concentrates on the evolution of indexes and investigates how the train running dynamics changes with time, as a consequence of wheel/rail profile wear and track irregularity degradation; the second analysis relates the vehicle dynamics to local phenomena along the line and to the evolution of track maintenance conditions. One of the main outcomes of the proposed methodology is the possibility of continuously monitoring track quality and of identifying critical sections along the line that may potentially need to be inspected, to decide whether dedicated maintenance actions are necessary.

Continuous monitoring of rail vehicle dynamics by means of acceleration measurements

Ivano La Paglia;Roberto Corradi;
2022-01-01

Abstract

The increased demand for rail transportation requires continuous monitoring of both railway lines and vehicle dynamics for different reasons, ranging from system safety and reliability to effective management of maintenance interventions. The target of the article is that of proposing innovative methodologies for data processing and analysis of experimental signals collected onboard, during long-term monitoring of in-service vehicles. The proposed methodologies are suitable for various rail transportation systems, ranging from metro to main line. At first, a positioning algorithm is proposed to associate all the measured acceleration signals to the corresponding track sections where they were acquired. It consists in a map-matching procedure to project the GPS positions, stored by the train during commercial service, onto the digital map of the railway line. Once completed, synthetic indexes representative of vehicle dynamics performances, such as RMS acceleration values, can be computed and matched to their exact position along the track, allowing to investigate vehicle dynamics evolution both in time and with respect to the position along the railway line. Two different data analysis approaches are proposed: the first one concentrates on the evolution of indexes and investigates how the train running dynamics changes with time, as a consequence of wheel/rail profile wear and track irregularity degradation; the second analysis relates the vehicle dynamics to local phenomena along the line and to the evolution of track maintenance conditions. One of the main outcomes of the proposed methodology is the possibility of continuously monitoring track quality and of identifying critical sections along the line that may potentially need to be inspected, to decide whether dedicated maintenance actions are necessary.
2022
PROCEEDINGS OF THE FIFTH INTERNATIONAL CONFERENCE ON RAILWAY TECHNOLOGY: RESEARCH, DEVELOPMENT AND MAINTENANCE
Condition-based maintenance, rail vehicle dynamics, railway infrastructure, onboard measurement.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1231690
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