Infrastructure Monitoring from an In- Service Light Rail Vehicle

In this project, we explore a data-­‐driven approach for monitoring rail infrastructure from the dynamic response of a train in revenue-­‐service. Presently, rail inspection is performed either visually or with dedicated track geometry cars. We examine a more economical approach where rail 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. With the financial and logistical support of the Technologies for Safe and Efficient Transportation University Transportation Center, 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. 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 test-­‐system. The sensing, signal processing, and data analysis we present could facilitate safer trains and more cost-­‐efficient maintenance in the future. Moreover, this approach is quite general and could be extended to other parts of the infrastructure, including bridges.