碳纤维层合板Lamb波损伤检测的加权块稀疏成像法

A weighted group-sparse imaging method is proposed via sparse priori-knowledge of damage in the measured structure. This approach attempts to deal with the cases of low imaging resolution and large interference of pseudo-points in the traditional Lamb wave imaging methods. An over-complete group-sparse dictionary of Lamb wave scattering is constructed with Lamb wave propagation model in thin plates, and the measured scattering Lamb waves are sparsely decomposed in the pre-built over-complete dictionary, so the Lamb wave imaging is transformed into convex optimization with weighted l₁ norm minimization. The weighted group-sparse convex optimization is solved by the spectral gradient projection method, and the sparse reconstruction coefficients of scattering Lamb waves are obtained. The coefficients are then taken as damage indicators to generate an image, which can locate damages in the measured structure. The results of imaging conducted on a plate of 16-layer CFRP laminate verify the effectiveness of the proposed method. The localization errors of the proposed method are 3.16 mm in single damage imaging case and 4.24 mm in double damage imaging case, respectively. The corresponding imaging results using the delay-and-sum imaging method are also comparatively presented.The imaging results obtained with the proposed method demonstrate higher resolution and smaller pseudo-point interference, but the computational task becomes heavier.