As a critical barrier for power network safety, insulating materials are susceptible to internal microcracks, delamination, and other hidden defects that can trigger dielectric strength degradation and space charge accumulation, ultimately leading to insulation breakdown. Ultrasonic shear wave non-destructive testing enables defect identification without damaging the material. Therefore, this paper focuses on the identification and imaging of internal defects in insulating components using ultrasonic shear waves. First, a physical model for ultrasonic shear wave NDT is established. Based on the refraction and reflection characteristics of ultrasonic waves in materials with different acoustic impedances, a defect localization formula is derived. Through simulation verification, for the three defects set at different positions in the defect model, the positioning error is less than 0.5 mm. Subsequently, defects such as circular holes, triangular shapes, cracks, and bottom grooves were simulated. Analysis of the echo data revealed a correlation between the distance from the sensor to the defect and the echo amplitude. For groove defect imaging, the differential SAFT algorithm was employed, achieving a width error of 1 mm for imaging a 2 mm wide by 5 mm high groove, clearly presenting the defect morphology. Finally, an imaging software program for defect structure reconstruction was developed based on the simulation model presented in this article. We collected side and back view data through the constructed ultrasonic transverse wave non-destructive testing experimental platform, and visualized defects in insulation materials with grooves using this ultrasonic imaging program. This study achieved defect localization and imaging through simulation of various defect types combined with synthetic aperture focused imaging algorithms, providing a reference for visualization and industrial application of ultrasonic shear wave non-destructive testing technology.
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Yukun Ma
Yi Tian
Tian Tian
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Ma et al. (Sat,) studied this question.
www.synapsesocial.com/papers/69a67ec3f353c071a6f0a2d2 — DOI: https://doi.org/10.3390/app16052400