Track segment automated characterisation via railway–vehicle–based random vibration signals and statistical time series methods

Abstract

The feasibility of automated railway track segment characterisation, as part of a broader track condition based maintenance procedure, is explored via railway–vehicle–based random vibration signals and Statistical Time Series (STS) methods. In particular, three methods within a Multiple Model (MM) framework, which are founded on data-driven stochastic parametric models for the representation of the partial vehicle-rail dynamics, are employed. The random vibration signals are obtained from two sensors, which are mounted on the axlebox and the bogie frame, respectively, of an Athens Metro railway vehicle moving under three different speeds (60, 70 or 80 km/h). The performance assessment of the methods is based on two distinct track characterisation problems. The first corresponds to the characterisation of a specific track segment after 6 months of continuous use, while the second to the comparison between two nominally identical track segments which were installed with a 2 month time interval and are used by different numbers and types of trains. The results indicate deterioration of the track segment in the first characterisation problem and differences between the two nominally identical segments in the second. The superiority of the employed methods over a state-of-the-art method is also demonstrated via proper comparisons.