Track monitoring from the dynamic response of a passing train: A sparse approach. (June 2017)

• Record Type: Journal Article
• Authors: Lederman, George; Chen, Siheng; Garrett, James H.; Kovačević, Jelena; Noh, Hae Young; Bielak, Jacobo
• Is Part Of: Mechanical systems and signal processing. Volume 90(2017)
• Page Start: 141
• Page End: 153
• Publication Date: 2017-06
• Subjects: Signal processing -- Sparse representation -- Orthogonal matching pursuit -- Vehicle-based inspection -- Inertial sensing; Structural dynamics -- Periodicals; Vibration -- Periodicals; Constructions -- Dynamique -- Périodiques; Vibration -- Périodiques; Structural dynamics; Vibration; Periodicals; 621
• DOI: 10.1016/j.ymssp.2016.12.009
• Languages: English
• ISSNs: 0888-3270

Abstract

Highlights: We use accelerometers in the cabin of a passenger train to monitor the tracks. We impose sparsity in estimating the track profile to make the problem stable. We also estimate the properties of the train from the recorded data. We validate using 2-years of operational train data that we collected in Pittsburgh. Abstract: Collecting vibration data from revenue service trains could be a low-cost way to more frequently monitor railroad tracks, yet operational variability makes robust analysis a challenge. We propose a novel analysis technique for track monitoring that exploits the sparsity inherent in train-vibration data. This sparsity is based on the observation that large vertical train vibrations typically involve the excitation of the train's fundamental mode due to track joints, switchgear, or other discrete hardware. Rather than try to model the entire rail profile, in this study we examine a sparse approach to solving an inverse problem where (1) the roughness is constrained to a discrete and limited set of "bumps"; and (2) the train system is idealized as a simple damped oscillator that models the train's vibration in the fundamental mode. We use an expectation maximization (EM) approach to iteratively solve for the track profile and the train system properties, using orthogonal matching pursuit (OMP) to find the sparse approximation within each step. By enforcing sparsity, the inverse problem is well posed and the train's position can be found relative to the sparse bumps, thus reducing the uncertainty in the GPS data. We validate the sparse approach on two sections of track monitored from an operational train over a 16 month period of time, one where track changes did not occur during this period and another where changes did occur. We show that this approach can not only detect when track changes occur, but also offers insight into the type of such changes.