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

共 50 条

[31] Reference-based combined deterministic-stochastic subspace identification for experimental and operational modal analysis
Reynders, Edwin
De Roeck, Guido
MECHANICAL SYSTEMS AND SIGNAL PROCESSING, 2008, 22 (03) : 617 - 637
[32] Study on Relation Between Noise and Matrix Dimension of Data-driven Stochastic Subspace Identification Method
Xin, Jun-feng
Zhang, Yong-bo
Sheng, Jin-lu
JOURNAL OF THE CHINESE SOCIETY OF MECHANICAL ENGINEERS, 2018, 39 (01): : 113 - 120
[33] Uncertainty bounds on modal parameters obtained from stochastic subspace identification
Reynders, Edwin
Pintelon, Rik
De Roeck, Guido
MECHANICAL SYSTEMS AND SIGNAL PROCESSING, 2008, 22 (04) : 948 - 969
[34] Stochastic system identification for operational modal analysis: A review
Peeters, B
De Roeck, G
JOURNAL OF DYNAMIC SYSTEMS MEASUREMENT AND CONTROL-TRANSACTIONS OF THE ASME, 2001, 123 (04): : 659 - 667
[35] Data-Driven Pareto-DE-Based Intelligent Optimal Operational Control for Stochastic Processes
Yin, Liping
Wang, Hong
Guo, Lei
Zhang, Hongyan
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS, 2021, 51 (07): : 4443 - 4452
[36] Modal parameters identification of bridge by improved stochastic subspace identification method with Grubbs criterion
Zhou, Yulin
Jiang, Xulei
Zhang, Mingjin
Zhang, Jinxiang
Sun, Hao
Li, Xin
MEASUREMENT & CONTROL, 2021, 54 (3-4): : 457 - 464
[37] MODAL ADDITIVE MODELS WITH DATA-DRIVEN STRUCTURE IDENTIFICATION
Gong, Tieliang
Xu, Chen
Chen, Hong
MATHEMATICAL FOUNDATIONS OF COMPUTING, 2020, 3 (03): : 165 - 183
[38] Data based stochastic subspace identification for structural strain modal parameter
Xiao, Xiang
Ren, Wei-Xin
Dai, En-Bin
Zhongnan Daxue Xuebao (Ziran Kexue Ban)/Journal of Central South University (Science and Technology), 2012, 43 (09): : 3601 - 3608
[39] Reference-based combined deterministic-stochastic subspace identification for operational modal analysis with deterministic inputs
Reynders, E.
De Roeck, G.
PROCEEDINGS OF ISMA2006: INTERNATIONAL CONFERENCE ON NOISE AND VIBRATION ENGINEERING, VOLS 1-8, 2006, : 3035 - 3046
[40] Automatic stochastic subspace identification of modal parameters based on hierarchical clustering method
Tang, B.-P., 1600, Chinese Vibration Engineering Society (31):

← 1 2 3 4 5 →