Sparse component analysis with optimized clustering for underdetermined blind modal identification

Abstract

Modal identification performs one of the most important roles in structural dynamics analysis and structural health monitoring, especially when the input excitations are not measurable. Most of the traditional blind source separation approaches can only handle determined or overdetermined blind modal identification, where the number of observed sensors is equal to or greater than the number of active modes. When the number of observed sensors is less than the number of active modes, new methods to perform underdetermined blind modal identification should be considered. To tackle this issue, a novel operational modal identification method based on an enhanced sparse component analysis with optimized clustering is proposed. Firstly, a robust K-means clustering with differential evolution algorithm is put forward to estimate the mode shape matrix utilizing the sparse property of observed mixtures. Secondly, the modal responses are recovered by the least squares method from the incomplete knowledge of the mode shape matrix and the system outputs. Subsequently, the modal responses are transformed into a time domain through time-frequency transformation, where the modal parameters are extracted. Finally, numerical simulation and experimental verification demonstrate that in both the determined and underdetermined case, the proposed method can perform accurate and robust parameter identification of structural systems.