MPC
Project Details

Title:	Automated Track Geometry Monitoring System
Principal Investigators:	Pan Lu, Raj Bridgelall, and Denver Tolliver
University:	North Dakota State University
Status:	Completed
Year:	2017
Grant #:	69A3551747108 (FAST Act)
Project #:	MPC-551
RH Display ID:	15675
Keywords:	algorithms, data collection, flaw detection, maintenance of way, mobile applications, monitoring, railroad tracks, sensors, smartphones

Abstract

This study will develop, implement, and evaluate an autonomous track geometry monitoring system to screen the network for faults during normal train operations. The technology performance will depend on the specific implementation and deployment options selected. Therefore, automatic data collection and recording devices will be necessary to gather motion, location, and speed data to evaluate the performance of various system implementation options. Initial data collection will begin with a smartphone that has all of the required sensors. The PIs will develop a smartphone application that will be capable of autonomously collecting and uploading data from hi-rail vehicles where power is available. The technology transfer phase will inform commercialization partners about the best approaches to develop a lower-cost and self-sufficient version of the sensor system deployed during the research. This research project will focus on developing the signal processing and machine learning algorithms and models that will transform the on-board sensor data into track geometry equivalents. The research team will also develop a reporting and mapping system to provide decision-makers with a data visualization tool.

Project Deliverables

• [Final Report] Track Surface Irregularity Position Localization with Smartphone-Based Solution — MPC-21-445
  • View Research Brief

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