This paper introduces an enhanced ultrasonic sonar-based ranging technique developed by the Experimental Mechanics, NDE & SHM Laboratory at UC San Diego to estimate the deflections of railroad ties. The deflection profile of the ties can be subsequently inspected to evaluate their performance and prevent unwanted events, like train derailments. The proposed sensing layout is comprised of an array of air-coupled capacitive transducers to perform pulse-echo ultrasonic tests in multiple points along the tie, and a high frame-rate camera to capture images of the objects probed by the array. A machine learning-based image processing technique is developed to classify the tie/ballast images based on the texture signature of the visible objects in the camera’s field of view. Next, the relative deflection profile of the ties is reconstructed by tracking the Time of Flight (ToF) of the received waveforms at the points flagged as a tie. A series of field tests was carried out at the Rail Defect Testing Facility of UC San Diego as well as a BNSF yard in San Diego, CA, by mounting the sensing prototype on a car moving at walking speed. The obtained results confirm the potential of the proposed airborne ranging technique for in-motion measurement of the deflections of railroad ties.
Matched Field Processing (MFP) is a generalized beamforming method which matches the received data to a dictionary of replica vectors to localize wave scattering sources (e.g., acoustic sources) in the complex media. The approach has also been used for passive structural monitoring and defect detection. The MFP requires an accurate model of medium, and this is a challenge in some applications. To tackle this issue, data-driven MFP has been recently introduced. Data-driven approaches are considered as model-free methods, which perform with no prior knowledge of the propagation environment to localize a source. This paper introduces a data-driven MFP approach for localizing the primary (i.e., impact) and secondary (i.e., defect) sources in plates. The replica vectors are made using the Fast Fourier Transform of the time history responses of the pristine plate under a controlled external excitation. Then, the MFP is implemented to localize the source. For defect localization, a subtraction approach under Born approximation is employed to remove or weaken the signature of the primary source and extract a set of data which purely contains the acoustic signature of the defect. The performance of the method for primary and secondary source localization is evaluated by studying a small aluminum plate, excited by a controlled broadband noise imposed by an impact hammer. A comparative study is carried out to evaluate the performance of the conventional Bartlett and adaptive White Noise Constraint processors in forming the ambiguity surfaces.
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.
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