Error-compensated one-dimensional convolutional neural network-based ultrasonic defect signal recognition method for flat ceramic membranes

Abstract

For the flowing hole affecting the one-dimensional convolutional neural network to identify the ultrasonic defect signal inside the flat ceramic membrane, this study proposed a 1D-CNN based on error compensation for the ultrasonic defect signal identification method of flat ceramic membrane. First, the ultrasonic flaw detector was used to scan the flat ceramic film and obtain the ultrasonic signal of the flat ceramic film. Second, through the analysis of the pulse reflection method, the inherent position of the flowing hole that causes the flowing hole ultrasonic signal was generated with the movement of the probe and change. A "rectangular box" was used for the ultrasonic signal error compensation of the generated flowing hole. Finally, the error-compensated ultrasonic signals were learned and classified employing a 1D-CNN model involving a fused attention mechanism. The experimental results demonstrated that the accuracy of the proposed 1D-CNN based on error compensation for ultrasonic defect identification of flat ceramic films was 95.63%, which was 17.06% higher than that of the 1D-CNN model without error compensation. Thus, the proposed detection method indicates promising potential and value in industrial applications.