Progressive Fault Identification and Prognosis of Railway Tracks Based on Intelligent Inference (1.6)

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.

• Record URL:
  https://tidc.umaine.edu/tidc_project/1-06-uconn/

Language

• English

Project

• Status: Completed
• Funding: $254,000
• 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: 20220630
• Actual Completion Date: 20220630
• USDOT Program: University Transportation Centers Program

Subject/Index Terms

• TRT Terms: Algorithms; Diagnostic tests; Durability; Fault monitoring; Intelligent agents; Railroad tracks; Reliability; Sensors
• Subject Areas: Data and Information Technology; Maintenance and Preservation; Railroads;

Filing Info

• Accession Number: 01698524
• Record Type: Research project
• Source Agency: Transportation Infrastructure Durability Center
• Contract Numbers: 69A3551847101
• Files: UTC, RIP
• Created Date: Mar 5 2019 10:20AM