Automatic modal parameters identification and uncertainty quantification based on block-bootstrap and multi-stage clustering under ambient excitation

doi:10.1177/14613484211051844

	Automatic modal parameters identification and uncertainty quantification based on block-bootstrap and multi-stage clustering under ambient excitation
	Luo, Yongpeng 1,3; Liu, Yuangui 1; Han, Jianping 2; Liu, Jingliang 1
刊名	JOURNAL OF LOW FREQUENCY NOISE VIBRATION AND ACTIVE CONTROL
	2021
卷号	41 期号:2 页码:551-565
关键词	Automatic modal parameters identification stochastic subspace identification uncertainty quantification block-bootstrap clustering stabilization diagram
ISSN号	1461-3484
DOI	10.1177/14613484211051844
英文摘要	This study proposes an algorithm for autonomous modal estimation to automatically eliminate false modes and quantify the uncertainty caused by the clustering algorithm and ambient factors. This algorithm belongs to the stochastic subspace identification (SSI) techniques and is based on the Block-Bootstrap and multi-stage clustering analysis. First, the Block-Bootstrap is introduced to decompose the response signal of the structure into M blocks of data. The covariance-driven stochastic subspace identification (SSI-Cov) method is used to process a random sample of data and obtain the corresponding M stabilization diagrams. In addition, the hierarchical clustering method is adopted to carry out the secondary clustering of the picked stable axis according to the defined distance threshold. Then, false modes are eliminated according to the proposed true and false modal discrimination index (MDI). Finally, the above steps are repeated B times, and MDI is used to modify the initial modal parameters of group B. The mean value of elements in the cluster is taken as the recognition result of modal parameters, and the standard deviation is used to measure the accuracy of the recognition result. The numerical simulation results and the modal parameter identification of the Jing-yuan Yellow River Bridge show that, for identifying true and false modals, the proposed modal discrimination index is more effective than the threshold value of the traditional index. Also, it was found that the proposed method can eliminate the uncertainty introduced in the clustering process. In addition, this method can remove the influence of ambient noises, and it can improve the identification accuracy. It will be shown that this method has better anti-noise performance.
WOS研究方向	Acoustics
语种	英语
出版者	SAGE PUBLICATIONS LTD
WOS记录号	WOS:000727410500001
内容类型	期刊论文
源URL	[http://ir.lut.edu.cn/handle/2XXMBERH/154985]
专题	教务处（创新创业学院）
作者单位	1.Fujian Agr & Forestry Univ, Sch Transportat & Civil Engn, Fuzhou 350002, Peoples R China; 2.Lanzhou Univ Technol, Sch Civil Engn, Lanzhou 710050, Peoples R China 3.Huaqiao Univ, Key Lab Struct Engn & Disaster Prevent Fujian Pro, Xiamen, Peoples R China;
推荐引用方式 GB/T 7714	Luo, Yongpeng,Liu, Yuangui,Han, Jianping,et al. Automatic modal parameters identification and uncertainty quantification based on block-bootstrap and multi-stage clustering under ambient excitation[J]. JOURNAL OF LOW FREQUENCY NOISE VIBRATION AND ACTIVE CONTROL,2021,41(2):551-565.
APA	Luo, Yongpeng,Liu, Yuangui,Han, Jianping,&Liu, Jingliang.(2021).Automatic modal parameters identification and uncertainty quantification based on block-bootstrap and multi-stage clustering under ambient excitation.JOURNAL OF LOW FREQUENCY NOISE VIBRATION AND ACTIVE CONTROL,41(2),551-565.
MLA	Luo, Yongpeng,et al."Automatic modal parameters identification and uncertainty quantification based on block-bootstrap and multi-stage clustering under ambient excitation".JOURNAL OF LOW FREQUENCY NOISE VIBRATION AND ACTIVE CONTROL 41.2(2021):551-565.