Progressive Fault Identification and Prognosis of Railway Tracks Based on Intelligent Inference

The objectives of this project are to synthesize novel sensors integrated with physics-informed data analytics to monitor the railway track for enhanced reliability and durability. New active sensing mechanisms will be explored, to enable autonomous detection and identification. New physics-informed statistical inference algorithms will be formulated, to realize highly accurate fault diagnosis and prognosis. Direct collaboration with industry partner will be carried out.

Language

  • English

Project

  • Status: Active
  • Funding: $277440
  • Contract Numbers:

    69A3551847101

  • Sponsor Organizations:

    University of Connecticut, Storrs

    Storrs, CT  United States  06268-5202

    Office of the Assistant Secretary for Research and Technology

    University Transportation Centers Program
    Department of Transportation
    Washington, DC  United States  20590
  • Managing Organizations:

    Transportation Infrastructure Durability Center

    University of Maine
    Orono, ME  United States  04469

    Office of the Assistant Secretary for Research and Technology

    University Transportation Centers Program
    Department of Transportation
    Washington, DC  United States  20590
  • Project Managers:

    Dunn, Denise

  • Performing Organizations:

    Transportation Infrastructure Durability Center

    University of Maine
    Orono, ME  United States  04469

    University of Connecticut, Storrs

    Connecticut Transportation Institute
    270 Middle Turnpike, Unit 5202
    Storrs, CT  United States  06269-5202
  • Principal Investigators:

    Tang, Jiong

  • Start Date: 20181001
  • Expected Completion Date: 20210930
  • Actual Completion Date: 0
  • USDOT Program: University Transportation Centers Program

Subject/Index Terms

Filing Info

  • Accession Number: 01698524
  • Record Type: Research project
  • Source Agency: Transportation Infrastructure Durability Center
  • Contract Numbers: 69A3551847101
  • Files: UTC, RiP
  • Created Date: Mar 4 2019 10:51AM