一种改进的识别结构模态参数的随机子空间法

doi:10.3969/j.issn.1001-2400.2017.06.005

Abstract

Abstract:

Stochastic subspace identification can be used to identify the modal parameters of a structure according to its dynamic response to ambient excitation. However, some high dimensional matrices (Toeplitz matrices) must be constructed in the process of identification, and lots of memory and computation time are cost to the singular value decomposition of these high dimensional matrixes. Stochastic subspace identification affects the computational efficiency seriously. Therefore, this paper investigates a new method for constructing lower-dimension Toeplitz matrices to improve the computing efficiency. Finally, a numerical simulation is presented to demonstrate the computing efficiency of the method. The result shows that the computing consumption of the proposed method is only 106％ the computing consumption of the traditional stochastic subspace identification while the identification accuracy is maintained.

Key words: stochastic subspace identification, computing efficiency, data-driven, covariance, modal parameters

LI Tuanjie;LIU Weimeng;TANG Yaqiong;GAO Liqiang. Improved stochastic subspace method for identifying structural modal parameters[J].Journal of Xidian University, 2017, 44(6): 26-30+58.

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Improved stochastic subspace method for identifying structural modal parameters

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