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Proceedings Paper

Discussion of using SSI-COV, refined FDD and multivariate AR model for operational modal analysis
Author(s): Tsai-Jung Kuo; Chin-Hsiung Loh; Wen Hsueh
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Paper Abstract

The objective of this study is to discuss three different methods on operational modal analysis: Covariance-driven stochastic subspace identification (SSI-COV), Refined frequency domain decomposition (rFDD) and Multivariate autoregressive (MVAR) model. First the SSI-COV method is employed. Through the proposed two steps of evaluation criteria on the discrimination of spurious modes from the stabilization diagram, identification on the correct models can be elaborated. Besides, discussion on the identification of harmonic component from stabilization diagram using the concept of singular value spectrum generated by refined frequency domain decomposition (rFDD) is also presented. Combine the SSI-COV, rFDD and the proposed criteria to remove the spurious modes, one can accurate estimate the structural modal dynamic properties. Comparison on the final stabilization diagram with respect to the model order spectrum using multivariate AR model is also presented. As an application, an ambient vibration test of 8-story steel frame from shaking table test is used to demonstrate the proposed algorithms. Discussion on the missing data to the influence on the result of identification is also presented. Compensation on the missing data to enhance the stabilization diagram.

Paper Details

Date Published: 27 March 2018
PDF: 10 pages
Proc. SPIE 10598, Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2018, 105981W (27 March 2018); doi: 10.1117/12.2295877
Show Author Affiliations
Tsai-Jung Kuo, National Taiwan Univ. (Taiwan)
Chin-Hsiung Loh, National Taiwan Univ. (Taiwan)
Wen Hsueh, National Taiwan Univ. (Taiwan)

Published in SPIE Proceedings Vol. 10598:
Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2018
Hoon Sohn, Editor(s)

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