基于近似非凸RPCA的Lamb波损伤监测OACSTPCD

In structural health monitoring(SHM)technology,Lamb wave-based damage detecting method shows great potential in plate structures.An outlier analysis method based on an approximate non-convex robust prin-cipal component analysis(ANC-RPCA)algorithm is proposed.The algorithm is capable of achieving effective defect diagnosis for high-dimensional measurement signals under the condition of dimensionality reduction.By approximating the rank of the matrix using a rank approximation function,and approximating the l0-norm with a non-convex penalty function,the non-convex penalty function can guarantee the uniqueness of the sparse so-lution under certain conditions.As the scale of the data matrix expands,the computational complexity of singu-lar value decomposition also increases when the traditional RPCA uses the kernel norm approximation.The new approximate method can retain effective information and identify outliers at a lower rank level for wave field im-ages with higher computational efficiency.This algorithm is applied to wavefield images based on Lamb waves and validated through simulations and experimental data.The solution is obtained using the inexact augmented Lagrange multiplier(IALM)method and compared with mainstream robust principal component analysis(RP-CA)algorithms commonly used in the field.The experimental results demonstrate the excellent performance of the ANC-RPCA algorithm in outlier identification.It exhibits significant advantages in terms of computational efficiency and low-rank characteristics compared to other algorithms,substantiating the reliability and complete-ness of the proposed algorithm.