TY - JOUR
T1 - Track monitoring from the dynamic response of a passing train
T2 - A sparse approach
AU - Lederman, George
AU - Chen, Siheng
AU - Garrett, James H.
AU - Kovačević, Jelena
AU - Noh, Hae Young
AU - Bielak, Jacobo
N1 - Funding Information:
This material is based on work supported by the National Science Foundation through a Graduate Research Fellowship for Lederman under Grant No. 0946825, by National Science Foundation awards 1130616 and 1017278, and a University Transportation Center grant (DTRT12-G-UTC11) from the US Department of Transportation. The authors also gratefully acknowledge the Port Authority of Allegheny County for their partnership, particularly David Kramer, and the many helpful workers of Amalgamated Transit Union Local 85.
Publisher Copyright:
© 2016 Elsevier Ltd
PY - 2017/6/1
Y1 - 2017/6/1
N2 - 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.
AB - 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.
KW - Inertial sensing
KW - Orthogonal matching pursuit
KW - Signal processing
KW - Sparse representation
KW - Vehicle-based inspection
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U2 - 10.1016/j.ymssp.2016.12.009
DO - 10.1016/j.ymssp.2016.12.009
M3 - Article
AN - SCOPUS:85009895619
SN - 0888-3270
VL - 90
SP - 141
EP - 153
JO - Mechanical Systems and Signal Processing
JF - Mechanical Systems and Signal Processing
ER -