Adaptive Weighted Damage Imaging of Lamb Waves Based on Deep Learning

The damage imaging method based on Lamb wave beamforming has been widely used in the field of SHM. The DAS method has a high imaging efficiency, but its ability to suppress interference signals is weak, resulting in low imaging resolution and signal-to-noise ratio. Drawing inspiration from the adaptive weighted MVDR damage imaging method, this paper constructs a neural network based on FCNN, with the images generated by the MVDR method as the target. By training the model, the mapping relationship between delayed channel input data and adaptive weighting factors is established, thereby improving the resolution and signal-to-noise ratio of Lamb wave damage imaging and achieving rapid imaging of damage. To verify the effectiveness and imaging performance of the FCNN method, imaging of two types of damage in aluminum plates is conducted through simulation and experiments, and the imaging results are compared and analyzed with DAS and MVDR. The results show that the imaging quality and the quantitative indicators of the FCNN method have not yet reached the performance level of the MVDR, but compared with DAS, FCNN has a significantly narrower main lobe width and lower sidelobe level. Furthermore, its quantitative indicators such as API, SNR, and FWHM are better than DAS. The proposed adaptive Lamb wave beamforming method based on FCNN combines high resolution and signal-to-noise ratio, as well as the advantage of rapid imaging, providing reference and support for real-time SHM based on Lamb waves.