Track-monitoring from the dynamic response of an operational train

Abstract

We explore a data-driven approach for monitoring rail infrastructure from the dynamic response of a train in revenue-service. Presently, track inspection is performed either visually or with dedicated track geometry cars. In this study, we examine a more economical approach where track inspection is performed by analyzing vibration data collected from an operational passenger train. The high frequency with which passenger trains travel each section of track means that faults can be detected sooner than with dedicated inspection vehicles, and the large number of passes over each section of track makes a data-driven approach statistically feasible. We have deployed a test-system on a light-rail vehicle and have been collecting data for the past two years. The collected data underscores two of the main challenges that arise in train-based track monitoring: the speed of the train at a given location varies from pass to pass and the position of the train is not known precisely. In this study, we explore which feature representations of the data best characterize the state of the tracks despite these sources of uncertainty (i.e., in the spatial domain or frequency domain), and we examine how consistently change detection approaches can identify track changes from the data. We show the accuracy of these different representations, or features, and different change detection approaches on two types of track changes, track replacement and tamping (a maintenance procedure to improve track geometry), and two types of data, simulated data and operational data from our

Damage-Sensitive and Domain-Invariant Feature Extraction for Vehicle-Vibration-Based Bridge Health Monitoring

“…The use of vibration signals collected from travelling vehicles to monitor structures ( Figure 1) has recently become a viable alternative to traditional structural health monitoring approaches [2,3,4,5,6,7]. This approach does not require intensive deployment and maintenance.…”

Railway Track Monitoring Using Train Measurements: An Experimental Case Study

“…These measurements have been used for several health monitoring purposes in the literature, as summarized in Table 1. Track profile Stiffness profile, elevation profile [11,[17][18][19] 3 Others Track replacement, tamping, rail bump and rail surface irregularities [20][21][22][23] In the context of railway track components, Wei et al [13] investigate the degradation of a railway crossing while using real train axle box measurements over time. They show that uneven deformation between the wing rail and crossing nose and local irregularity in the longitudinal slope of the crossing nose can be identified.…”

“…They show that decomposing the signals while using the Hilbert transform has the potential to find track irregularities. Recently, Lederman et al [23] propose an energy-based approach for track change monitoring. They plot the average energy of the measured acceleration in the space domain in a contour plot.…”

Evaluation of Railway Ballast Layer Consolidation After Maintenance Works

“…The practical application of the methods for the track maintenance is shown in another paper [25] at an example of a track quality index analysis. The datadriven rail-infrastructure monitoring that is based on data fusion, feature extraction, selection and other data mining methods is presented in the literature [26][27][28]. In the present paper, one part of the data mining is used: the feature extraction and selection, that is based on the statistical classification and clustering methods.…”