频域归一化结合MUSIC子空间投影的Lamb波损伤检测方法OA

Carbon fiber reinforced plastic(CFRP)structures are prone to corrosion and delamination during engineering applications,which leads to structural failure.To address this issue,a Lamb waves damage detection method integrating frequency-domain normalization and multiple signal classification(MUSIC)subspace projection was proposed.This method dynamically optimized the dispersion factor β to construct a frequency-adaptive steering vector,which accurately corrected phase mismatch in anisotropic media and significantly enhanced the focusing performance of the MUSIC spatial spectrum.The feasibility of this approach was first verified through ABAQUS simulation.By applying frequency-domain normalization to the echo signals and extracting damage features via signal differentiation,the impact of damage signals on imaging accuracy was amplified.Corrosion damage was subsequently identified and imaged using MATLAB software.Experimental results demonstrated that the proposed method effectively identified damage with a maximum error rate less than 1.6%.Moreover,wave velocity error analysis showed that the modulation of the dispersion factor β confined the error within±0.8%,thereby improving wave velocity measurement accuracy and reducing its impact on damage localization.The proposed technique offers significant advantages in enhancing imaging precision and provides an effective solution for damage detection in CFRP structures.