西安电子科技大学学报（自然科学版）2017，Vol.44Issue(6)：26-30,58,6.DOI:10.3969/j.issn.1001-2400.2017.06.005
一种改进的识别结构模态参数的随机子空间法
Improved stochastic subspace method for identifying structural modal parameters
摘要
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.关键词
随机子空间法/计算效率/数据驱动/协方差/模态参数Key words
stochastic subspace identification/computing efficiency/data-driven/covariance/modal parameters分类
机械制造引用本文复制引用
李团结,刘伟萌,唐雅琼,高利强..一种改进的识别结构模态参数的随机子空间法[J].西安电子科技大学学报（自然科学版）,2017,44(6):26-30,58,6.基金项目
国家自然科学基金资助项目(51775403) （51775403）
