Underdetermined operational modal parameter identification based on adaptive dictionary compressed sensing

To solve the problem of low accuracy and poor robustness of underdetermined operational mode parameters identification methods based on sparse component analysis and orthogonal basis compressed sensing, a method based on adaptive dictionary compressed sensing was proposed. In this method, the adaptive dictionary compressed sensing was used to reconstruct the modal coordinate response on the basis of modal shape estimation. The training samples of dictionary learning were constructed by filtering separation method under the framework of compressed sensing. The dictionary learning method based on K-means Singular Value Decomposition (K-SVD) and the hierarchical coupling dictionary training strategy was used to generate the adaptive dictionary, and the unsupervised dictionary learning was realized. The Orthogonal Matching Pursuit (OMP) algorithm was used to obtain the sparse coefficient components and recover the source signal to reconstruct modal coordinate response. Under the framework of compression sensing, the adaptive dictionary obtained by the K-SVD algorithm was better than the orthogonal basis such as Fourier basis or discrete cosine basis in sparse representation of signal decomposition. The identification results of underdetermined operational mode parameter identification under 5-degree-of-freedom simulation dataset showed that the proposed method had better recognition accuracy and robustness than the methods such as sparse component a-nalysis and orthogonal basis compression sensing. © 2023 CIMS. All rights reserved.