Autonomous Modal Parameter Extraction Based on Stochastic Subspace Identification

doi:10.3901/JME.2018.09.187

Abstract

Abstract: To remove spurious modes automatically and effectively, to avoid artificial participation in modal model order determination and modal pickup, so as to extract the modal parameter automatically and exactly, a novel autonomous modal parameter extraction method based on stochastic subspace identification method is proposed. Firstly, two stochastic subspace models with different dimension Hankel matrices for operating modal parameter identification are constructed and their poles are computed. On this basis, a crystal stabilization diagram is obtained by matching the same order poles of the two models. Finally, hierarchical clustering analysis is performed to the stabilization diagram and then exact modal parameters are automatically extracted. The effectiveness of the proposed method is verified by the simulation of operational modal analysis for a 5DOF mass-spring-damping system and the experiment for a rectangle plate.

Key words: automatic extraction, modal parameter, operational modal analysis, stabilization diagram, stochastic subspace identification

CLC Number:

TB123

ZHANG Yongxiang, LIU Xin, CHU Zhigang, HUANG Di, WANG Guangjian. Autonomous Modal Parameter Extraction Based on Stochastic Subspace Identification[J]. Journal of Mechanical Engineering, 2018, 54(9): 187-194.

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