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            Autonomous Inspection System Based on Passive Sensing and Anomaly Detection
      

      
  Document Series
              Technical Reports


      
  Author
          Federal Railroad Administration


      
Report Number
              DOT/FRA/ORD-23/23

  Office
              RDI-21


      Subject
                    Track


      
  Keywords
              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


      

      
      
              
                  
      
              
  Document
              Passive Sensing-A.pdf (2.68 MB)

      
              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 U.S. DEPARTMENT OF TRANSPORTATION

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