Monitoring of Turnout Ballast Degradation Using Statistical Low-Complexity Behavioral Models

Abstract

The dependability of the railway infrastructure is paramount to guarantee safety, comfort, and network capacity. Turnouts are the key infrastructure element to enable the maximization of network capacity and minimization of transport delays. Their failure upsets the overall performance of the railway network; hence, infrastructure managers are interested in securing as high as possible uptime. The ballast layer provides the elastic support to the superstructure (sleepers and rail); thereby, it is crucial to ensure both safety and comfort in railway transport. This article presents a novel detection system for the monitoring of ballast degradation throughout its service life. A statistical model based on the generalized extreme value distribution is proposed to describe the behavior of the resonance frequency associated with the ballast. The generalized likelihood ratio test is then adopted to detect when the state of health of the ballast changes over time. The monitoring system is tested exploiting full-scale measurements of train-induced track vertical acceleration collected at a turnout of the Danish railway network over a two-year period, which includes a maintenance event. Results demonstrate the ability of the ballast monitoring system in detecting the progressive degradation of the ballast quality.