Blind modal identification for decentralized sensor network by modified sparse component analysis in frequency-domain subspace. (15th October 2022)

• Record Type: Journal Article
• Authors: Yao, Xiao-Jun; Yi, Ting-Hua; Zhao, Shao-Wei
• Is Part Of: Engineering structures. Volume 269(2022)
• Publication Date: 2022-10-15
• Subjects: Blind modal identification -- Largely underdetermined blind source separation -- Decentralized sensor network -- Sparse component analysis; Structural engineering -- Periodicals; Structural analysis (Engineering) -- Periodicals; Construction, Technique de la -- Périodiques; Génie parasismique -- Périodiques; Pression du vent -- Périodiques; Earthquake engineering; Structural engineering; Wind-pressure; Periodicals; 624.105
• DOI: 10.1016/j.engstruct.2022.114794
• Languages: English
• ISSNs: 0141-0296

Abstract

Highlights: Decentralized modal identification is accomplished by segmenting the frequency-domain subspaces using the energy fluctuation spectrum; Sparse component analysis method which is modified by frequency-domain subspaces shows to be effective for estimating similar partial mode shapes; Density peak clustering is suitable for identification of mode shapes, even closely spaced modes are included; Identificaiton of damping ratio using instant spectrum in frequency-domain subspace is suitable for decentralized sensors. 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.