Feature extraction of ultrasonic guided wave weld detection based on group sparse wavelet transform with tunable Q-factor

Ultrasonic guided wave (UGW) is suitable for defect detection of long weld, but it is difficult to extract defect echo features due to dispersion, multi-mode, background noise and structural noise. To solve this problem, a group sparse tunable Q-factor wavelet transform (GS-TQWT) model is proposed in this paper. Firstly, it is revealed that the defect echo of UGW nondestructive testing (NDT) has the characteristic of group sparsity. Based on this, the UGW defect features extraction model of the GS-TQWT is established. Then, a simulation signal is constructed according to the dispersion and attenuation characteristics of UGW, and the adaptive selection of the GS-TQWT optimal parameters has been completed based on the simulation signal. Moreover, the majorizationminimization (MM) algorithm is used to solve the model. Finally, the experiment of UGW weld defect detection was carried out to verify the effectiveness of the GS-TQWT model.

Automated summary

This paper proposes a group sparse tunable Q-factor wavelet transform (GS-TQWT) model for defect detection of long weld using ultrasonic guided wave (UGW). The model is able to extract defect echo features in the presence of dispersion, multi-mode, background noise, and structural noise. Simulation and experimental results verify the effectiveness of the GS-TQWT model for UGW weld defect detection.