Uncertainty calculation for modal parameters used with stochastic subspace identification: an application to a bridge structure

Wei-Ting Hsu; Chin-Hsiung Loh; Shu-Hsien Chao

doi:10.1117/12.2083919

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27 March 2015 Uncertainty calculation for modal parameters used with stochastic subspace identification: an application to a bridge structure

Wei-Ting Hsu, Chin-Hsiung Loh, Shu-Hsien Chao

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Proceedings Volume 9435, Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2015; 94350Q (2015) https://doi.org/10.1117/12.2083919
Event: SPIE Smart Structures and Materials + Nondestructive Evaluation and Health Monitoring, 2015, San Diego, California, United States

Abstract

Stochastic subspace identification method (SSI) has been proven to be an efficient algorithm for the identification of liner-time-invariant system using multivariate measurements. Generally, the estimated modal parameters through SSI may be afflicted with statistical uncertainty, e.g. undefined measurement noises, non-stationary excitation, finite number of data samples etc. Therefore, the identified results are subjected to variance errors. Accordingly, the concept of the stabilization diagram can help users to identify the correct model, i.e. through removing the spurious modes. Modal parameters are estimated at successive model orders where the physical modes of the system are extracted and separated from the spurious modes. Besides, an uncertainty computation scheme was derived for the calculation of uncertainty bounds for modal parameters at some given model order. The uncertainty bounds of damping ratios are particularly interesting, as the estimation of damping ratios are difficult to obtain. In this paper, an automated stochastic subspace identification algorithm is addressed. First, the identification of modal parameters through covariance-driven stochastic subspace identification from the output-only measurements is used for discussion. A systematic way of investigation on the criteria for the stabilization diagram is presented. Secondly, an automated algorithm of post-processing on stabilization diagram is demonstrated. Finally, the computation of uncertainty bounds for each mode with all model order in the stabilization diagram is utilized to determine system natural frequencies and damping ratios. Demonstration of this study on the system identification of a three-span steel bridge under operation condition is presented. It is shown that the proposed new operation procedure for the automated covariance-driven stochastic subspace identification can enhance the robustness and reliability in structural health monitoring.

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Wei-Ting Hsu, Chin-Hsiung Loh, and Shu-Hsien Chao "Uncertainty calculation for modal parameters used with stochastic subspace identification: an application to a bridge structure", Proc. SPIE 9435, Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2015, 94350Q (27 March 2015); https://doi.org/10.1117/12.2083919

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