Uncorrelated multi-sources load identification in frequency domain based on improved Tikhonov regularization method

Wang, Cheng; Zhong, Bineng; Guo, Wang-Ping; Chen, Weibin; Sun, Chengzhu

doi:10.3233/jae-162199

Cited by 7 publications

(1 citation statement)

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“…As part of the structural characteristics, transfer functions between multiple loads excitation and multi-point vibration response are obtained in the transfer function experiment. For example, the response function of the system in the frequency domain can be acquired by simultaneously measuring the input spectrum function of the system's multiple loads excitation and the output spectrum of the multi-point vibration response [11].…”

Section: Introductionmentioning

confidence: 99%

MIMO LS-SVR-Based Multi-Point Vibration Response Prediction in the Frequency Domain

et al. 2020

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To predict the multi-point vibration response in the frequency domain when the uncorrelated multi-source loads are unknown, a data-driven and multi-input multi-output least squares support vector regression (MIMO LS-SVR)-based method in the frequency domain is proposed. Firstly, the relationship between the measured multi-point vibration response and unmeasured multi-point vibration response is formulated using the transfer function in the frequency domain. Secondly, the data-driven multiple regression analysis problem of multi-point vibration response prediction in the frequency domain is described formally, and its mathematical model is established. With the measured multi-point vibration response as the input and the unmeasured multi-point vibration response as the output, the vibration response history data are assembled as a MIMO training dataset at each frequency. Thirdly, using the MIMO LS-SVR algorithm and MIMO history training dataset, the multi-point vibration response prediction model is built at each frequency point. By comparing the transmissibility matrix method, multiple linear regression model-based method, and MIMO neural network method, the application scope of the proposed method and its advantages are analyzed. The experimental results for acoustic and vibration experiment on a cylindrical shell verified that the MIMO LS-SVR-based method predicts the multi-point vibration response effectively when the loads are unknown, and has higher precision than the transfer function method, multiple linear regression method, MIMO neural network method, and transmissibility matrix method.

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Section: Introductionmentioning

confidence: 99%