CFRP damage imaging based on MVDR weighted sparse reconstruction

Carbon fiber reinforced polymer (CFRP) is widely used in aerospace and other fields due to its excellent performance, and it will be damaged in service. Sparse reconstruction (SR) algorithm can be used to image the CFRP damage and locate the damage, but the atomic mismatch problem will cause artifacts and even misjudge the damage. Aiming at the above problems, it was proposed that a sparse reconstruction imaging method weighted by minimum variance distortionless response (MVDR). The CFRP monitoring area was divided into several grids, the dictionary was constructed based on the scattering model of Lamb wave to form the SR model with the scattering signal and the sparse solution variables. Secondly, the MVDR imaging method was used for imaging. Based on the imaging results, the MVDR weighting factor was constructed to weight the sparse solution variables. Finally, the basis pursuit denoising algorithm was adopted to solve the weighted SR model, the optimal sparse solution was obtained and converted into pixel value to realize the damage imaging of CFRP. The experimental results of CFRP damage imaging show that the imaging effect of the proposed method is better than that of the SR imaging method under the same regularization parameters, the localization errors are reduced by 72.9 mm, 77.4 mm and 14.7 mm respectively compared with the SR imaging method under three different regularization parameters. Under four different damage locations, the imaging results of MVDR-SR imaging method have fewer artifacts and the maximum damage localization error is 7.9 mm. Compared with MVDR and SR imaging methods, MVDR-SR imaging method has better imaging performance, which verifies the correctness and effectiveness of the proposed method.