Evaluation of the Transverse Crack Depth of Rail Bottoms Based on the Ultrasonic Guided Waves of Piezoelectric Sensor Arrays

Abstract

A method based on the high-frequency ultrasonic guided waves (UGWs) of a piezoelectric sensor array is proposed to monitor the depth of transverse cracks in rail bottoms. Selecting high-frequency UGWs with a center frequency of 350 kHz can enable the monitoring of cracks with a depth of 3.3 mm. The method of arranging piezoelectric sensor arrays on the upper surface and side of the rail bottom is simulated and analyzed, which allows the comprehensive monitoring of transverse cracks at different depths in the rail bottom. The multi-value domain features of the UGW signals are further extracted, and a back propagation neural network (BPNN) is used to establish the evaluation model of the transverse crack depth for the rail bottom. The optimal evaluation model of multi-path combination is reconstructed with the minimum value of the root mean square error (RMSE) as the evaluation standard. After testing and comparison, it was found that each metric of the reconstructed model is significantly better than each individual path; the RMSE is reduced to 0.3762; the coefficient of determination R2 reached 0.9932; the number of individual evaluation values with a relative error of less than 10% and 5% accounted for 100% and 87.50% of the total number of evaluations, respectively.

Full-rank Imaging Detection of Multi-frames of Ultrasonic-guided Waves in Welded Structural Plate Based on Arc Sparse Array

“…In plate imaging detection using ultrasonicguided wave synthetic aperture array, both arc array and linear array can be considered as regular line-arc arrays. By translating the regular array elements radially or horizontally form the arc array configuration, a sparse distribution is achieved, forming an arc sparse array [17]. Compared to regular arc arrays, arc sparse arrays offer greater flexibility and improved adaptability for engineering detection.…”