Bayesian model updating method based on the wavelet transform of acceleration frequency response functions

被引：0

作者：

Wang Z. ^{[1
]}

Yin H. ^{[1
]}

Peng Z. ^{[1
]}

Zhang Y. ^{[1
]}

Dong K. ^{[2
]}

机构：

[1] School of Mechanical Engineering, Lanzhou Jiaotong University, Lanzhou

[2] College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou

来源：

Zhendong yu Chongji/Journal of Vibration and Shock | 2022年 / 41卷 / 10期

关键词：

Acceleration frequency response function; Bayesian method; Kriging model; Markov chain Monte Carlo (MCMC) algorithm; Model updating; Wavelet transform;

D O I：

10.13465/j.cnki.jvs.2022.10.005

中图分类号：

学科分类号：

摘要：

Aiming at the derivation complexity of the likelihood function of frequency response functions, difficulty in convergence and high rejection rate of the Markov chain Monte Carlo (MCMC) algorithm, a Bayesian model updating method based on the wavelet transform and an improved MCMC algorithm was proposed. Firstly, the positions of excitation points and measurement points were optimized by introducing the variance coefficient of mode participation criterion and the modal kinetic energy method. Then, the Kriging model was constructed. The acceleration frequency response functions were calculated and the wavelet transform was performed to extract the total wavelet energy as the output of the Kriging model. The correlation coefficient of the Kriging model was optimized by the particle swarm optimization algorithm. Finally, in order to improve the sampling efficiency, on the basis of the delayed rejection strategy, when the candidate sample was rejected, a new candidate sample was generated by introducing the beetle antennae search algorithm to estimate the posterior probability distribution of the parameters to be updated. A 3DOFs vehicle system and a space truss model were used to verify the proposed method. The results show that the overall performance of the updated Markov chain is better, the acceptance rate of samples is improved, and the relative error of the updated parameters basically stays within 1% with good noise resistance. © 2022, Editorial Office of Journal of Vibration and Shock. All right reserved.

引用

页码：30 / 39

页数：9

共 24 条

[1]

MOSAVI A A, SEDARAT H, O'CONNOR S M, Et al., Calibrating a high-fidelity finite element model of a highway bridge using a multi-variable sensitivity-based optimization approach, Structure and Infrastructure Engineering, 10, 5, pp. 627-642, (2014)

[2]

FANG S E, PERERA R, ROECK G D., Damage identification of a reinforced concrete frame by finite element model updating using damage parameterization, Journal of Sound and Vibration, 313, 3, pp. 544-559, (2008)

[3]

JIANG Dong, FEI Qingguo, WU Shaoqing, Updating of structural dynamics model with uncertainty based on interval analysis, Journal of Vibration Engineering, 28, 3, pp. 352-358, (2015)

[4]

SOIZE C., Stochastic modeling of uncertainties in computational structural dynamics-recent theoretical advances, Journal of Sound and Vibration, 332, 10, pp. 2379-2395, (2013)

[5]

BECK J L, KATAFYGIOTIS L S., Updating models and their uncertainties I: bayesian statistical framework, Journal of Engineering Mechanics, 124, 4, pp. 455-461, (1998)

[6]

BECK J L, ZUEV K M., Asymptotically independent Markov sampling: a new Markov chain Monte Carlo scheme for Bayesian interference, International Journal of Uncertainty Quantification, 3, 5, pp. 445-474, (2013)

[7]

CHING J Y, CHEN Y C., Transitional Markov chain Monte Carlo method for Bayesian model updating, model class selection, and model averaging, Journal of Engineering Mechanics, 133, 7, pp. 816-832, (2007)

[8]

LIU Gang, LUO Jun, QIN Yang, Et al., A finite element model updating method based on an improved MCMC method, Engineering Mechanics, 33, 6, pp. 138-145, (2016)

[9]

PENG Zhenrui, ZHENG Jie, BAI Yu, Et al., A model updating method based on an improved MCMC algorithm, Journal of Vibration and Shock, 39, 4, pp. 236-245, (2020)

[10]

CHEUNG S H, BECK J L., Bayesian model updating using hybrid Monte Carlo simulation with application to structural dynamic models with many uncertain parameters, Journal of Engineering Mechanics, 135, 4, pp. 243-255, (2009)

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