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.
Tsai-Jung Kuo, Chin-Hsiung Loh, and Wen Hsueh, "Discussion of using SSI-COV, refined FDD and multivariate AR model for operational modal analysis," Proc. SPIE 10598, Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2018, 105981W (Presented at SPIE Smart Structures and Materials + Nondestructive Evaluation and Health Monitoring: March 08, 2018; Published: 27 March 2018); https://doi.org/10.1117/12.2295877.
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