Sparse-based defect detection of weld feature guided waves with a fusion of shear wave characteristics

Abstract

Shear wave is suitable for long weld defect detection in offshore platforms with its advantages of long-distance propagation, small attenuation, and high accuracy. Owing to dispersion, multi-mode, and strong background noises, the defect signal can generally be overwhelmed. To solve this problem, a new sparse-based defect detection method is proposed for weld feature guided waves with a fusion of shear wave characteristics. First, an over-complete dictionary is established combining wavenumber and scattering characteristics of shear wave. Then, the split augmented Lagrangian shrinkage algorithm is introduced in the basis pursuit algorithm for the sparse solution. The defect features of the signal can be sparsely extracted. The effectiveness is evaluated via simulation studies and further verified through the practical experiment data. Compared with the wavelet atom method, the location error of the proposed method reduces by about 1.0% to only 0.564% in simulation and about 2.0% to only 0.671% in practical experiment.