Mode Separation and Dispersion Compensation Based on PSO-OMP for Enhanced Lamb Wave Damage Imaging

Lamb waves exhibit multimode and dispersive characteristics, which mask crucial damage information and deteriorate damage localization resolution in structural health monitoring. This article aims to enhance damage detection capability through multi-mode fusion imaging by developing an advanced signal processing approach. A computationally efficient Particle Swarm Optimization-enhanced Orthogonal Matching Pursuit (PSO-OMP) algorithm with physics-guided over-complete dictionaries is proposed to separate overlapping wave packets of A<sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> and S<sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> modes and compensate dispersion effects. Numerical simulations demonstrate that the proposed method achieves superior wave packet arrival time accuracy (errors < 2%) compared with conventional MP and OMP algorithms, while maintaining robust performance under various noise conditions. Experimental validation using a sparse array of piezoelectric wafers confirms successful mode separation and dispersion compensation. Comparative analysis reveals that the multi-mode fusion imaging significantly outperforms single-mode imaging, achieving 23.8% improvement in damage localization accuracy and effective artifact suppression. This work contributes to the field of structural health monitoring by establishing an effective multi-mode imaging framework, enabling more accurate and reliable damage assessment in plate-like structures.