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The National Academies

High Speed Rail IDEA Project 25 [Completed (IDEA)]

Neural Network Based Rail Flaw Detection Using Unprocessed Ultrasonic Data

  Project Data
Funds: $85,000
Staff Responsibility: Chuck Taylor
Completion Date: 7/1/2003
Fiscal Year: 2000

Field inspection to detect rail flaws is performed primarily using ultrasonic transducers mounted on track-mounted vehicles (see Figure 1). Failure to promptly detect and repair these flaws imposes service reliability problems for both freight and passenger operations, and can pose safety concerns. Current ultrasonic rail flaw detection technology limits inspection vehicle operating speeds to about 15 mph and detects only about 70 percent of the flaws. Both of these restrictions result from the limitations of the human operator’s ability to interpret the signal from the detection equipment. When flaws are indicated with the current system, it is often necessary to stop the inspection vehicle and perform a hand test. This limits efficient management of rail repair and delays traffic. Neural networks have the potential to substantially enrich the capability to efficiently and reliably extract comprehensive information from the ultrasonic signal used to inspect rail, and to do so in a more automated manner.  

Neural networks can improve the capability to recognize flaws using the ultrasonic signal after it has been filtered by the signal processor. However, the information in the processed signal is substantially abridged because of limitations in the human operator’s ability to interpret it, and much useful information about flaws is discarded.
 
In this project, laboratory and field techniques were to be used to develop neural networks that use all of the information, not just the processed signal. The end product envisioned was a rail inspection car that can operate at significantly higher speeds, detect flaws earlier and more reliably, and detect flaws that currently cannot be detected.

 

The final report for this IDEA project can be found at:
https://onlinepubs.trb.org/onlinepubs/archive/studies/idea/finalreports/highspeedrail/HSR-25Final_Report.pdf

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