Project 1.06: Progressive Fault Identification and Prognosis of Railway Tracks Based on Intelligent Inference

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


Project Summary

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 developed, 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 partners will be carried out.

Principal Investigator

Dr. Jiong Tang


University of Connecticut

Project Status


Project Cost


Start Date


Project Type


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Office of the Assistant Secretary for Research and Technology, University Transportation Centers Program, Department of Transportation, University of Connecticut.

Implementation of Research Outcomes:

The formulation of high-frequency finite element analysis with piezoelectric actuation has been shared with Sperry Rail Service and partially utilized by Sperry to facilitate wave propagation analysis in Sperry probe.

Impacts and Benefits of Implementation:

Impacts and benefits of the research have yet to be realized upon the recent completion of the work.

Printable Project Information Sheet

Project 1.06 Final Report

April 2019 Semi-Annual Progress Report

June 2019 Bi-Monthly Progress Report

July 2019 Bi-Monthly Progress Report

September 2019 Semi-Annual Progress Report

December 2019 Quarterly Progress Report

March 2020 Quarterly Progress Report

August 2020 Quarterly Progress Report

September 2020 Quarterly Progress Report

January 2021 Quarterly Progress Report

March 2021 Quarterly Progress Report

July 2021 Quarterly Progress Report

October 2021 Quarterly Progress Report

December 2021 Quarterly Progress Report

April 2022 Quarterly Progress Report

June 2022 Quarterly Progress Report