Optimal parameters in Data-driven stochastic subspace identification in Operational Modal Analysis

被引：0

作者：

Tsai, Chun-Yu ^{[1
]}

Chan, Yum Ji ^{[1
]}

Chen, Jau-Liang ^{[1
]}

Chao, Ching-Ling ^{[1
]}

Chien, Shih-Yin ^{[1
]}

机构：

[1] Natl Chung Hsing Univ, Dept Mech Engn, 145 Xing da Rd, Taichung 40227, Taiwan

来源：

PROCEEDINGS OF INTERNATIONAL CONFERENCE ON NOISE AND VIBRATION ENGINEERING (ISMA2018) / INTERNATIONAL CONFERENCE ON UNCERTAINTY IN STRUCTURAL DYNAMICS (USD2018) | 2018年

关键词：

SELECTION;

D O I：

暂无

中图分类号：

TH [机械、仪表工业];

学科分类号：

0802 ;

摘要：

Stochastic subspace identification operational modal analysis is a recently-developed method in modal testing. In this method, determining the dimensions of the Hankel matrix in the projection procedure is an important step. In this paper, the number of rows of the Hankel matrix is recommended to be larger than a half period of Toeplitz matrix. There is no need to adopt excessive number of rows in the Hankel matrix, because the projection results would not vary significantly under high number of the matrix rows. Also, if computational power is limited, the projection results stabilize more quickly if the number of rows, number of columns and the sampling frequency are decreased as the same time.

引用

页码：2783 / 2792

页数：10

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