Acceleration-based condition monitoring of track longitudinal level using multiple regression models

Abstract

In this paper, a condition monitoring system for railway track geometry is presented. The methodology has been designed for high-speed application, where the train travels at the maximum allowed speed for most of the trip. The system is designed to rely on acceleration data recorded by in-service vehicles to provide estimations of the track longitudinal level, based on pre-built regression models. It exploits synthetic indicators sampled over predefined track sections 100 m long. Different predictors are considered, computed both from acceleration data and from track geometry measured by the diagnostic train. The proposed modelling strategy allows distinguishing between isolated and distributed defects that populate the railway track as well as reproducing the evolution over time of the maximum longitudinal level registered in the considered track section; moreover, also accurate predictions of the defect amplitude are made. The results have been validated against track geometry data recorded by the diagnostic train during a monitoring period of 2 years. It is proven that the proposed system could support current maintenance strategies, providing a continuous flow of data to monitor the track infrastructure.