Sparse Locally Linear Embedding for Modal Identification

Wei Guan , L. L. Dong , J. Zhou

DOI Number XXX-YYY-ZZZ

Conference Number HiSST 2018_1090835

In order to address false modal parameters identification caused by the uncertainty of the selection of adjacent points in manifold learning, a novel modal identification method for structural dynamics using sparse Locally Linear Embedding (LLE) method is proposed. Compared with conventional LLE algorithm, this method can adaptively find the neighbors and weight coefficients by solving a sparse optimization problem, which assumes the neighbors that lie in the same manifold and the low dimensional manifold embedding is extracted from observation space with high dimensional then. Numerical simulation and experiment results illustrate that the proposed method can effectively preserve the neighborhood structure of high dimensional response signals with small nonzero weights, and the modal parameters (modal shapes and modal frequencies) can be accurately identified in comparison to classical LLE algorithm or its various improvement strategies. In addition, the proposed method is more robust than various improvement strategies under different noise levels.

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Dimension Reduction, HiSST 2018, Locally Linear Embedding, Manifold Learning, Modal Identification, Sparse Representation

In Categories: HiSST 2018, Materials and Structures, Materials and Structures

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