Development of Frequency-Mixed Point-Focusing Shear Horizontal Guided-Wave EMAT for Defect Inspection Using Deep Neural Network

Abstract

We propose a new frequency-mixed point-focusing shear horizontal (SH) guided-wave electromagnetic acoustic transducer (EMAT) in this work to obtain the defect positions and plate thickness simultaneously and accurately. Compared with other guided-wave detection methods, it is not required to measure the plate thickness in advance because we can easily obtain it during the test. We use the variational mode decomposition method to decompose the received frequency-mixed defect signal into subsignals with different center frequencies and to remove the noise. Furthermore, we use the continuous wavelet transform to analyze these subsignals using the time-frequency method and to obtain the time-of-flight information of the guided wave under different frequencies and modes. Therefore, we can obtain accurate defect positions and plate thicknesses via the new transducer and signal processing methods while improving the signal intensities. In the identification of defect types, we first constructed a database set containing three types of defects of different sizes using data enhancement methods. Then, the dense network, convolutional neural network, recurrent neural network, and newly proposed deep GFresNet are studied to analyze the defect classification performance of each structure. The results show that the proposed GFresNet has very good defect identification accuracy, which is about 95% along any depth of the defects, and that it can automatically extract high-level information without sophisticated feature engineering.