Correlation-Based Detection and Classification of Rail Wheel Defects Using Air-Coupled Ultrasonic Acoustic Emissions

A non-destructive method for inspecting stationary rail wheels, based on air-coupled ultrasonic acoustic emissions, is presented and evaluated in this paper. In the proposed method, an impulsive excitation, such as a hammer strike, is applied radially to the rail wheel. A portable battery-powered ultrasonic microphone, with a frequency response from 15kHz to 170kHz, is then used to measure the resulting acoustic emissions. Each wheel defect emits a characteristic signature in response to an impulsive excitation. In order for the proposed method to accurately and reliably detect defects, a high-fidelity characteristic signature must be extracted for each defect of interest. A procedure has been developed for extracting defect signatures, and is presented in the first part of the paper. After a characteristic signature has been extracted for a defect, it is then used in a correlation-based approach to detect the presence of that defect signature in an ultrasonic acoustic emission measurement. A metric is proposed for quantifying the degree of presence of the signature in a measurement. This metric is evaluated in an experimental case study using a scale model of a rail wheel. Experimental tests are performed using a no-defect wheel, wheels with two different defects, and two locations of the ultrasonic microphone relative to the defect. Results confirm that the proposed metric is able to detect and classify defects using data from only a single impulsive excitation; however, the reliability of the prediction is shown to increase with each additional batch of data. One interesting outcome from the experimental testing is that the method can detect and classify defects even when the ultrasonic microphone is not at the same location that was used to extract the characteristic signature.

References
  • Anderson, Robert Thomas. "Quantitative analysis of factors affecting railroad accident probability and severity." PhD diss., University of Illinois at Urbana-Champaign, 2005.
  • Liu, Xiang, M. Saat, and Christopher Barkan. "Analysis of causes of major train derailment and their effect on accident rates." Transportation Research Record: Journal of the Transportation Research Board 2289, 2012: 154- 163.
  • Mix, Paul E. Introduction to nondestructive testing: a training guide. John Wiley & Sons, 2005.
  • Avisoft Bioacoustic, www.avisoft.com
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