Track-monitoring from the dynamic response of an operational train. (15th March 2017)

• Record Type: Journal Article
• Authors: Lederman, George; Chen, Siheng; Garrett, James; Kovačević, Jelena; Noh, Hae Young; Bielak, Jacobo
• Is Part Of: Mechanical systems and signal processing. Volume 87:Part A(2017)
• Page Start: 1
• Page End: 16
• Publication Date: 2017-03-15
• Subjects: Rail maintenance -- Signal processing -- Change detection -- Vehicle-based inspection -- Data acquisition -- Position uncertainty; Structural dynamics -- Periodicals; Vibration -- Periodicals; Constructions -- Dynamique -- Périodiques; Vibration -- Périodiques; Structural dynamics; Vibration; Periodicals; 621
• DOI: 10.1016/j.ymssp.2016.06.041
• Languages: English
• ISSNs: 0888-3270

Abstract

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 test-system. The sensing, signal processing, and data analysis we propose in the study could facilitate safer trains and more cost-efficient maintenance in the future. Moreover, the proposed approach is quite general and could be extended to other parts of the infrastructure, including bridges. Highlights: Accelerometers in the cabin of a passenger train are used to monitor the tracks. Challenges due to varying train speed and GPS position uncertainty are addressed. Robust features and unsupervised change detection approaches are proposed. We validate using 2-years of operational train data that we collected in Pittsburgh.