In this paper, a methodology for continuous monitoring of railway track geometry based on acceleration measurements from commercial trains is presented. A strict correlation between track longitudinal level and bogie vertical acceleration was identified making use of synthetic indicators. A linear regression model relating the standard deviation of the track geometry to the RMS of the bogie vertical acceleration was realized. The combined use of vehicle dynamic data and the proposed linear model allowed to accurately estimate the longitudinal level in a specific section of the rail line. The described results represent a first step towards the development of methodologies for track condition-based maintenance relying on measurements coming from the commercial fleet.

Track continuous monitoring using vehicle dynamic measurements from the commercial fleet

Ivano La Paglia;Egidio Di Gialleonardo;Alan Facchinetti
2022-01-01

Abstract

In this paper, a methodology for continuous monitoring of railway track geometry based on acceleration measurements from commercial trains is presented. A strict correlation between track longitudinal level and bogie vertical acceleration was identified making use of synthetic indicators. A linear regression model relating the standard deviation of the track geometry to the RMS of the bogie vertical acceleration was realized. The combined use of vehicle dynamic data and the proposed linear model allowed to accurately estimate the longitudinal level in a specific section of the rail line. The described results represent a first step towards the development of methodologies for track condition-based maintenance relying on measurements coming from the commercial fleet.
2022
Proceedings of The Fifth International Conference on Railway Technology: Research, Development and Maintenance
Condition-based maintenance, rail vehicle dynamics, track longitudinal level, in service vehicle
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1231689
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