Real-time extraction of structural modal features is an important means of structural health monitoring. Aiming at the problem of the traditional stochastic subspace algorithm being not able to reflect overall characteristics of a structure under finite measuring points, an improved covariance-driven stochastic subspace modal features real-time extraction method was proposed. Firstly, based on a new Hankel element reconstruction form, the correlation between the actual spatial information of a structure and the algorithm was constructed, and the dimension of the calculation matrix was effectively reduced to significantly improve the accuracy and efficiency of the platform modal identification. Then, the identification method and elimination criteria of false modes based on signal accumulation characteristics were further established to solve the problem of excessive false modes in the calculation of large-scale complex structure with the traditional stochastic subspace method. Finally, taking an offshore platform in Bohai Sea as an example, the response signals of the platform were calculated using the proposed method, and the results were compared with those obtained using the traditional stochastic subspace method to verify the excellent applicability and robustness of the proposed method.
Key words
stochastic subspace method /
modal parameter extraction /
Hankel matrix /
offshore platform
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