基于频响函数奇异值的模型修正方法

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Abstract In order to reduce noise interference in measurement environment, a model updating method based on singular value of frequency response function (FRF) was proposed. Firstly, FRF obtained with calculation was used to reconstruct the attractor matrix, and the singular value decomposition (SVD) was performed for it. Under influence of noise, the number of singular values containing the main characteristic information was selected according to the principle of sudden change in the number of extreme value points, and then parameters to be corrected were determined. Secondly, initial sample points were extracted with Latin hypercube sampling, and the particle swarm optimization algorithm was used to find the optimal correlation coefficient, it was combined with singular value response corresponding to parameters to be corrected to construct Kriging model. Then, the objective function was constructed using the least square of the response difference of singular value, and Cuckoo algorithm was used to solve parameter correction values. Simulation examples showed that Kriging model can obtain higher correction accuracy by using singular value as structural response; adding Gaussian white noise with different signal-to-noise ratios in FRF, a more satisfactory updating effect can be obtained to verify stronger robustness of the proposed method to noise.

Key words： model updating frequency response function (FRF) singular value decomposition (SVD) extreme value point particle swarm optimization algorithm Kriging model

Received: 09 September 2019 Published: 15 February 2021

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	Articles by authors
	CAO Mingming
	PENG Zhenrui
	LIU Mandong

Cite this article:

CAO Mingming,PENG Zhenrui,LIU Mandong. Model updating method based on singular value of frequency response function[J]. JOURNAL OF VIBRATION AND SHOCK, 2021, 40(3): 195-203.

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http://jvs.sjtu.edu.cn/EN/ OR http://jvs.sjtu.edu.cn/EN/Y2021/V40/I3/195

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