Stochastic Model Updating Based on Kriging Model and Lifting Wavelet Transform

Abstract

In order to improve the efficiency of stochastic model updating and reduce the amount of calculation, a stochastic model updating method based on Kriging model and lifting wavelet transform is proposed. Firstly, perform lifting wavelet transform on the acceleration frequency response function, and extract the fifth-level approximate coefficients to replace the original frequency response function; secondly, use the Latin hypercube sampling to sample the parameters to be updated and the corresponding approximate coefficients as the outputs to build the Kriging model. A butterfly optimization algorithm with the Lévy flight(LBOA) is proposed and use to improve the accuracy of the Kriging model; finally, with the goal of minimizing the Wasserstein distance, the mean value of the parameters to be updated is solved by the whale optimization algorithm. The results of the test function show that LBOA has greatly improved in terms of optimization, convergence accuracy and stability. The updating errors of the numerical examples are all less than 0.4%, verifying that the proposed model updating method has high accuracy and efficiency.