Track monitoring from the dynamic response of a passing train: A sparse approach

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.

Railway Track Monitoring Using Train Measurements: An Experimental Case Study

“…A feature detection method is proposed to find changes in the contour plot, which can be indicators of faults on the track. In another study, Lederman et al [27] show that in some parts of the track, the train is more excited which creates bumps in the measured acceleration signals. They suggest that changes in the sizes of the bumps in consecutive runs are indicative of changes to the track.…”

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.…”

A Condition-Based Maintenance Methodology for Rails in Regional Railway Networks Using Evolutionary Multiobjective Optimization

“…However, under the interference of measurement noises and normal vibrations of the wheel and rail, some defects might be difficult to observe directly from acceleration peaks in the time domain. Then, it is common in the literature that frequency domain methodologies are employed to focus on defect-induced vibration frequencies that can be easily observed from the signals, [7][8][9][10][11][12][13][14][15]. To circumvent the manual selection of method parameters, this paper employs the HHT to extract instantaneous frequency features.…”