Signal Feature Extraction and Combination to Enhance the Detection and Localization of Railroad Track Irregularities

Abstract

Tracks are critical and expensive railroad asset, requiring frequent maintenance. The stress from heavy car axle loads increases the risk of deviations from uniform track geometry. Irregularities in track geometry, such as track warping, can cause an excessive harmonic rocking condition that can lead to derailments, traffic delays, and associated financial losses. This paper presents an approach to enhance the location identification accuracy of track geometry irregularities by combining measurements from sensors aboard Hi-Rail vehicles. However, speed variations, position recording errors, low GPS update rates, and the non-uniform sampling rates of inertial sensors pose significant challenges for signal processing, feature extraction, and signal combination. This study introduces a method of extracting features from the fused data of inertial sensors and GPS receivers with multiple traversals to locate and characterize irregularities of track geometry. The proposed method provides robust detection and enhanced accuracy in the localization of irregularities within spatial windows along the track segment. Tradeoff analysis found that the optimal spatial window size is 5-meter.