Defect detection performance of a high-speed rail inspection system from passive acoustic identification

Diptojit Datta; Albert Liang; Ranting Cui; Francesco Lanza di Scalea

doi:10.1117/12.2558205

23 April 2020 Defect detection performance of a high-speed rail inspection system from passive acoustic identification

Diptojit Datta, Albert Liang, Ranting Cui, Francesco Lanza di Scalea

Proceedings Volume 11379, Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2020; 113791G (2020) https://doi.org/10.1117/12.2558205
Event: SPIE Smart Structures + Nondestructive Evaluation, 2020, Online Only

Abstract

Inspection of railway tracks using ultrasonic techniques has been growing in importance since the last few years. Most of the existing technologies, however, operate at low speeds (~30 mph) using specialized test vehicles. This paper is based on a new technology utilizing non-contact air-coupled ultrasonic transducers for high-speed (up to 80 mph) rail inspection through the extraction of the acoustic Green’s function of a rail segment between a pair of sensors. The Green’s function is extracted passively using an output-only approach with the wheels of the locomotive acting as the source of excitation. The paper will focus on the results of various field tests conducted at the Transportation Technology Center in Pueblo, CO. Specifically, the detection performance of the “passive” prototype will be determined based on Receiver Operating Characteristic (ROC) curves that are obtained for various rail discontinuities (joints, welds, defects) and with varying operational parameters (speed, length of baseline distribution, number of runs, etc.). The optimum selection of these parameters will be determined based on these curves.

Citation Download Citation

Diptojit Datta, Albert Liang, Ranting Cui, and Francesco Lanza di Scalea "Defect detection performance of a high-speed rail inspection system from passive acoustic identification", Proc. SPIE 11379, Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2020, 113791G (23 April 2020); https://doi.org/10.1117/12.2558205

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