Packaging model selection and parametric estimation based on approximate Bayesian calculation

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Journal of Vibration and Shock ›› 2023, Vol. 42 ›› Issue (23) : 253-259.

ZHU Dapeng1,CAO Xingxiao2

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Abstract

Accurately modeling for package is the base of transport packaging safety assessment, the modeling of package includes two aspects: model selection and model parameters estimation. Taking the uncertainties and errors of package model into account, it is reasonable to formulate a uncertainty model for the package in the Bayesian inference framework. In Bayesian inference, the likelihood function is usually either intractable or unavailable, the model selection and parameters estimation can not be realized in efficient and accurate manner. In this paper, to circumvent the evaluation of likelihood function and alleviate the computation burden, an approximate Bayesian computation algorithm is proposed, with which one can realize the model selection and model parameters identification simultaneously. The algorithm is formulated based on the sequential Monte-Carlo and importance sampling. With the threshold decreases in approximate Bayesian computation, the optimal model which can best match the dynamical behavior of real structure in the competing models can be obtained. Simultaneously, the model parameters are convergent to adjacent areas of true parameters values. The random vibration experiment is carried out, the experiment data is analyzed using the algorithm, it is indicated that Bouc-Wen model(n=2) is the optimal model for package dynamic. The model response simulation with the identified parameters is accurate comparing with experiment data.

Key words

transport packaging / model selection / package model parameters estimation / approximate Bayesian computation / sequential Monte-Carlo

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ZHU Dapeng1,CAO Xingxiao2. Packaging model selection and parametric estimation based on approximate Bayesian calculation[J]. Journal of Vibration and Shock, 2023, 42(23): 253-259

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