Feature Extraction of Echo Signal of Weld Defect Guided Waves Based on Sparse Representation

Abstract

In order to accurately detect the location of weld defect from strong background noises, a new method incorporating Morlet wavelet basis atom into sparse representation theory is investigated and it is suitable for detecting the weld defect feature from the guided wave echo signal. After determining the matching basis function, the split augmented Lagrangian shrinkage algorithm is introduced to solve the basis pursuit denoising problem. The echo signal can be intuitively represented in the sparse coefficients, then the defect signal can be effectively extracted from strong background noises. Both the simulated studies and the real applications of experimental signals show that this method is precise and stable, and can effectively suppress the influence of noises.