Game-based K-means clustering for automated modal parameters identification

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Journal of Vibration and Shock ›› 2023, Vol. 42 ›› Issue (24) : 92-100.

GUO Peng1，LI Dongsheng1，GUO Xin2

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Abstract

In order to improve the automation of modal parameter identification based on Stochastic Subspace Identification (Stochastic subspace identification, SSI), a two-stage automatic modal parameter identification algorithm based on game-based k-means (Game-based k-means, GBK) is proposed. Firstly, identify mode candidates from a large number of system orders through SSI for the collected data and remove certainly spurious modes using hard validation criteria. Secondly, the multi type deviation indicator of two adjacent poles is constructed into a feature vector matrix for GBK clustering, and the spurious modes are further eliminated. Then, the extracted structural modes are hierarchically clustered to obtain each order of modes; A six-degree-of-freedom mass-spring structure numerical model is then used to validate the suggested approach. Finally, it is applied to the actual monitoring data of a long-span suspension bridge for automatic modal parameter identification, which further verifies the feasibility and applicability of this method for automatic identification of massive data collected in practical engineering.

Key words

Structural Health Monitoring / Operational modal analysis / Automated modal identification / Bargaining game / Stochastic Subspace Identification

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GUO Peng1，LI Dongsheng1，GUO Xin2. Game-based K-means clustering for automated modal parameters identification[J]. Journal of Vibration and Shock, 2023, 42(24): 92-100

References

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