Operational modal analysis of three-dimensional structure based on Laplacian Eigenmaps

被引：0
|
作者：
Fu W. [1 ]
Wang C. [1 ,2 ]
Chen J. [3 ]
Lai X. [4 ]
Li H. [1 ,2 ]
机构：
[1] College of Computer Science and Technology, Huaqiao University, Xiamen
[2] Xiamen Engineering Research Centre of Enterprise Interoperability and Business Intelligence, Xiamen
[3] Department of Mathematics and Statistics, San Diego State University, San Diego, 92182, CA
[4] College of Mechanical Engineering and Automation, Huaqiao University, Xiamen
来源：
Jisuanji Jicheng Zhizao Xitong/Computer Integrated Manufacturing Systems, CIMS | 2022年 / 28卷 / 03期
基金：
中国博士后科学基金; 中国国家自然科学基金;
关键词：
Laplacian Eigenmaps; Low dimensional embedding; Operating modal parameters; The moore-penrose matrix inverse; Three-dimensional structure;
D O I：
10.13196/j.cims.2022.03.017
中图分类号：
学科分类号：
摘要：
To identify the operational modal parameters of linear time-invariant three- dimensional structures only from the stationary vibration response signals, a method based on Laplacian Eigenmaps was proposed. The vibration response data was regarded as the data set in the high-dimensional space, and the low-dimensional embedded data was found by using Laplacian Eigenmaps. Then, the natural frequency of the modal was identified from the modal response matrix corresponding to the low dimensional embedded matrix by using the single degree of freedom recognition technique. The modal shapes could be calculated from the Moore-Penrose matrix inverse of the low-dimensional embedding matrix. The simulation results of three-dimensional cylindrical shell showed that compared with equidistant mapping, Laplacian Eigenmaps was effectively identify the modal shape and natural frequency of the structure with lower time consumption and higher recognition accuracy. Compared with principal component analysis, Laplacian Eigenmaps had higher recognition accuracy. © 2022, Editorial Department of CIMS. All right reserved.
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页码：834 / 842
页数：8
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