Blind modal identification for decentralized sensor network by modified sparse component analysis in frequency-domain subspace

Abstract

It is practical to develop a modal identification method using a decentralized sensor network for field tests. Sparse component analysis (SCA) is a potential method in the framework of blind source separation to overcome the difficulty of using limited sensors. Nevertheless, it is still challenging for SCA to identify a larger number of modes than the number of measurements using the decentralized sensor network. This is because the partial mode shapes may be similar. It is convenient to perform SCA in frequency-domain subspaces to improve SCA further for decentralized sensor implementation; this is owing to the distinguishability of different partial mode shapes. In this study, a modal identification method applied to a largely underdetermined case caused by a decentralized sensor network is proposed by modifying SCA by automatic segmentation of frequency-domain subspaces. First, a method for determining the segmenting point is developed by identifying the valleys of the energy fluctuation spectrum using the scale-space peak detection method. Second, single-mode-points in the time–frequency plane are detected to facilitate mode shape estimation in the frequency-domain subspaces. Third, density peak clustering with the clustering number determination method is developed to estimate mode shapes, particularly for closely spaced modes. Finally, the modal frequencies and damping ratios are identified through the inverse operator in the frequency-domain subspaces and the curve fitting of the expected instant spectrum. The proposed method is verified by a 10 degree-of-freedom system, a structure with close modes, and an actual cable-stayed bridge. The investigations verified the effectiveness of the proposed method for decentralized sensor networks and practical applications.