Damage imaging through compressed wavefield recovery in Lamb wave inspections

This research presents a damage detection technique based on a compressive sensing (CS) algorithm applied to full wavefield data. The aim is to realize an effective tool for damage detection and localization which allows to reduce the acquisition time. The proposed technique exploits the compressive sensing framework to infer the damage location and entity from the comparison between the wavefield reconstructions produced by the different representation domains such as those spanned by Wave atoms (WA), Curvelets (CT) and Fourier (FT) exponentials. The procedure was applied to an aluminum plate with a notch cut. The propagation of Lamb waves on such structure was simulated numerically. The results show that the technique can be applied in a variety of structural components to reduce acquisition time and achieve high performance in defect detection and localization by removing up to 80% of the Nyquist sampling grid.