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An Automated System for Rail Transit Infrastructure Inspection
  • Published Date:
    2015-03-01
  • Language:
    English
Filetype[PDF-8.26 MB]


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An Automated System for Rail Transit Infrastructure Inspection
Details:
  • Publication/ Report Number:
    RITARS-12-H-UML
  • Resource Type:
  • Edition:
    Final Report
  • Format:
  • Abstract:
    This project applied commercial remote sensing and spatial information (CRS&SI) technologies such as Ground Penetrating Radar (GPR), laser, GIS, and GPS to passenger rail inspections. An integrated rail inspection system that can be mounted on hi-rail vehicles has been developed. This integrated system consisted of four major components: GPR subsystem, laser subsystem, track dynamic model subsystem, and a web-GIS based Decision Support System (DSS). The GPR subsystem was designed to identify track subsurface problems such as fouled ballast and suspicious underground objects. It was developed using a set of home-made horn antennas; the laser subsystem was for detecting surface track defects such as missing fasteners and cross ties, large cracks in cross ties, and wide rail gauge. It was developed based on a commercial laser product called LCMS; the track dynamic model was developed to identify a critical track structure problem called hanging tie using spectrum analysis. The main instrument for this subsystem was a 3-axial accelerometer; and the web-GIS based DSS was developed using ArcGIS for Server and Microsoft SQL Server. Its purpose was mainly to manage and visualize the results generated by the previous three subsystems. The first three subsystems were integrated and mounted on a hi-rail SUV. This automated and operational system was then tested at both Massachusetts Bay Transportation Authority (MBTA) and Metro St. Louis. The team also developed algorithms for processing the GPR, laser, and track dynamic model data. These algorithms include a 2D entropy method for GPR data analysis and a 3D template matching method for identifying missing fasteners. Among them, the laser algorithms have already been commercialized by Pavemetrics as LRAIL. The data collected from MBTA and Metro St. Louis was processed by these algorithms and fed into the DSS. In addition to popular GIS tools for visualizing, querying, and editing spatial and attribute data, the project team developed a mobile App to facilitate field asset inspections. Rail transit agencies in the United States rely heavily on visual observation for their weekly track inspections. This manual method is time-consuming, costly, and cannot effectively identify subsurface safety hazards. The project team reached out to several major rail transit agencies in the United States and demonstrated the developed product. These demonstrations generated substantial interest among these stakeholders. With the aging rail infrastructure, this developed system is expected to substantially benefit the rail transit industry by improving the track inspection efficiency, accuracy, and safety of both the rail transit systems and track workers. During the course of this project, the team identified additional interesting and innovative ideas. Some of them have been successfully implemented such as the mobile App. These ideas are also discussed throughout this report.

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