<p>Automated inspection systems are quickly becoming a commonplace in the railroad industry. Automated track geometry measurement systems (ATGMS), for example, are starting to be used to conduct more frequent safety inspections without sacrificing operational efficiency in revenue service. Along with the vast distances these systems cover per annum, an equally substantial amount of raw data is collected that contain valuable insight into long-term trends. This will allow for operators to monitor track geometry conditions as they develop over time, predict degradation rates, optimize maintenance efforts, and, in turn, prevent safety-critical issues before they become problematic. This is the key to timely data-driven decision making and, while this is the end for which we aim, the current process by which critical information is obtain from the raw data is labor intensive and time consuming.</p><p>The Federal Railroad Administration is developing innovative computational strategies that will form the basis for the automated management, alignment, processing, analysis, and reporting of recursive track geometry measurements gathered by ATGMS vehicles. This paper presents the efforts to date for this initiative, including the successes and challenges inherent with this undertaking, including the proper alignment of recursive measurements obtained from different ATGMS vehicles within the fleet. Using the raw data collected from the ATGMS fleet of a Northeastern U.S. passenger railroad, a manual process has been developed that (1) segments and aligns the track geometry measurements from multiple time-separated runs, (2) identifies and processes peak-value deviations, (3) and reports the appropriate severity level of the deviations as they approach maintenance and safety thresholds. This will form the basis for an automated process that employs machine learning to streamlines the steps taken to bring actionable information from the ATGMS vehicle to the decision maker.</p>

Development of Predictive Analytics Using Autonomous Track Geometry Measurement System Data

Paper Proceedings: https://asmedigitalcollection.asme.org/JRC/JRC2021/volume/84775

2021 Joint Rail Conference (JRC2021)

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