An ultrasonic signal processing method to improve defect depth estimation in composites based on empirical mode decomposition

In this paper, an ultrasonic signal processing method is proposed to improve depth evaluation of phased array ultrasonic non-destructive testing in composite structures. The proposed algorithm is based on an improved adaptive time–frequency analysis algorithm, and is a combination of empirical mode decomposition, correlation coefficient analysis, a fuzzy entropy algorithm and Hilbert transform. The ultrasonic signal is decomposed into intrinsic mode functions (IMFs) using an improved complete ensemble empirical mode decomposition with adaptive noises. Subsequently, the correlation coefficient and fuzzy entropy are used to select the optimal IMFs to reconstruct the signal. Then, Hilbert transform is executed to obtain the envelope of the reconstructed signal. Finally, the arrival time of the ultrasonic echo is estimated through the signal envelope, and then used to calculate the defect depth. The simulation and experimental results demonstrated that the proposed method has high evaluation accuracy in processing intense noisy signals or overlapped echoes. For simulated signals with different signal-to-noise ratios, the maximum estimation error of arrival time is 0.06 µs. Compared with the traditional gating method, the defect depth evaluation result is significantly improved. In particular, for near-surface defects, the maximum depth detection error is reduced from 0.13 mm to 0.06 mm.