Vold-Kalman Filter Order tracking of Axle Box Accelerations for Railway Stiffness Assessment

TL;DR

This paper introduces a Vold-Kalman filter-based method to decompose axle box acceleration signals, enabling real-time railway track stiffness assessment and predictive maintenance using low-cost onboard sensors.

Contribution

The study presents a novel application of Vold-Kalman filtering to extract periodic excitation-response components from acceleration data for railway track monitoring.

Findings

Decomposition of signals correlates with wheel and track conditions.

Track stiffness indicators relate to wheel-rail forces and sleeper passage amplitude.

Method enables real-time, cost-effective infrastructure monitoring.

Abstract

Intelligent data-driven monitoring procedures hold enormous potential for ensuring safe operation and optimal management of the railway infrastructure in the face of increasing demands on cost and efficiency. Numerous studies have shown that the track stiffness is one of the main parameters influencing the evolution of degradation that drives maintenance processes. As such, the measurement of track stiffness is fundamental for characterizing the performance of the track in terms of deterioration rate and noise emission. This can be achieved via low-cost On Board Monitoring (OBM) sensing systems (i.e., axle-box accelerometers) that are mounted on in-service trains and enable frequent, real-time monitoring of the railway infrastructure network. Acceleration-based stiffness indicators have seldom been considered in monitoring applications. In this work, the use of a Vold-Kalman filter is proposed, for decomposing the signal into periodic wheel and track related excitation--response pairs functions. We demonstrate that these components are in turn correlated to operational conditions, such as wheel out-of-roundness and rail type. We further illustrate the relationship between the track stiffness, the measured wheel-rail forces and the sleeper passage amplitude, which can ultimately serve as an indicator for predictive track maintenance and prediction of track durability.