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Title:Track Surface Irregularity Position Localization with Smartphone-Based Solution
Authors:Pan Lu, Raj Bridgelall, Denver Tolliver, Bhavana Bhardwaj, and Neeraj Dhingra
University:North Dakota State University
Publication Date:Nov 2021
Report #:MPC-21-445
Project #:MPC-551
TRID #:01831012
Keywords:flaw detection, maintenance of way, railroad tracks, sensors, signal processing, smartphones
Type:Research Report – MPC Publications

 

Abstract

Tracks are a critical and expensive railroad asset, requiring frequent maintenance. Railroads companies often rely on the accurate localization and identification of the track anomalies that could cause serious damage to infrastructure, environment, and the traveling public. However, the deployed method of inspection and maintenance is expensive, slow, increases the safety risk of workers, and requires track closure. Also, the technical limitations of present methods prevent their network-wide scaling to all railroads. Low-cost GPS receivers and accelerometers aboard regular vehicles offer a promising alternative to monitor all railroads in real time. However, low resolution and low accuracy of GPS receivers and non-uniform sample rates of the inertial sensors produced signal position misalignment and additive signal noise. Subsequently, signal-to-noise ratio (SNR) decreases and the detection error to locate the track irregularity will increase. In particular, false positives and false negatives can increase when SNR decreases. The introduced framework in this research reduces detection error while enhancing the quality of the signals and extracted features. This research demonstrates the potential use of low-cost sensors aboard hi-rail vehicles to monitor automatically and continuously for inertial events caused by irregular track geometry. The study characterizes and validates its accuracy by comparing the estimated positions of detected irregularities with the actual positions of irregularities that the railroad inspector observes. Therefore, railroad agencies that employ developed frameworks and methods will benefit from reliable track and equipment condition situation to make informed decisions, leading to resource optimization.

How to Cite

Lu, Pan, Raj Bridgelall, Denver Tolliver, Bhavana Bhardwaj, and Neeraj Dhingra. Track Surface Irregularity Position Localization with Smartphone-Based Solution, MPC-21-445. North Dakota State University - Upper Great Plains Transportation Institute, Fargo: Mountain-Plains Consortium, 2021.

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