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U.S. Department of Transportation U.S. Department of Transportation Icon United States Department of Transportation United States Department of Transportation

Autonomous Inspection System Based on Passive Sensing and Anomaly Detection

Document Series
Technical Reports
Federal Railroad Administration
Report Number
Subject Track
Ballast moisture monitoring, fouled ballast monitoring, passive sensing system, frequency doubling reflectenna, sequential change detection, deep neural networks, machine learning, artificial intelligence, high-frequency electrical simulation models

Detecting poor track conditions before they become safety-critical is essential for improving the safety and efficiency of railroads. To this end, autonomous track inspection technologies are beneficial, since established manual inspection methods are time-consuming and require significant track time windows. This report documents the initial investigation of a proposed innovative wireless technology with passive embedded sensors to provide an autonomous, energy-efficient, low-cost, and long-term platform for the non-destructive health monitoring of ballast. The research team performed a 1-year feasibility study to (1) assess the viability, risks, and costs of the proposed technology for ballast monitoring; and (2) deliver a system development plan to bring this technology to practice. The lab experiments and data analysis conducted in this feasibility study indicate that the proposed passive sensing technology can be used to regularly monitor ballast moisture for possible failures. The report details a plan for continued research including additional development and testing.

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Last updated: Monday, August 21, 2023