Vold–Kalman filter order tracking of axle box accelerations for track stiffness assessment
Cyprien Hoelzl, Vasilis Dertimanis, Lucian Ancu, Aurelia Kollros, Eleni Chatzi
Abstract
Intelligent data-driven monitoring procedures hold enormous potential for ensuring safe operation and optimal management of railway infrastructure in the face of increasing demands on cost and operations efficiency. Numerous studies have highlighted track stiffness as a main parameter 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. In this work, we propose a rail stiffness assessment scheme relying on low-cost, on board monitoring sensors, namely axle-box accelerometers, that are mounted on in-service trains and enable frequent, real-time monitoring of the railway infrastructure network. A Vold–Kalman filter is proposed for decomposing the signal into periodic wheel and track related excitation–response pair functions. We demonstrate that these components are in turn correlated to operational conditions, such as wheel out-of-roundness and the 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.