Abstract: To improve the efficiency of model updating and satisfy its robustness for the measured environmental noise, the Kriging model and wavelet decomposition are introduced into the model updating of the acceleration frequency response function. Firstly, the acceleration frequency response function is decomposed by wavelets, and the obtained wavelet coefficients with large amplitudes in the first layer are used to represent the original frequency response function. Secondly, the Latin hypercube sampling is utilized to design the primary parameters to be updated, and the sensitivity analysis of each parameter carried out according to the design results to determine the parameters to be modified, which are then used as the inputs of the Kriging model, while the corresponding wavelet coefficients as the outputs of the model. The optimal correlation coefficients of the Kriging model are found through the mixed grey wolf algorithm, with which an accurate and effective Kriging model is established. Finally, with the error between the wavelet coefficients calculated from the Kriging model and those from tests as the objective function, a minimization problem is solved by the water cycle algorithm for parameter updating. Numerical examples show the effectiveness of the proposed model updating method. When Gaussian white noise with a signal-to-noise ratio of 5 dB is added to the acceleration frequency response function, the updating error is smaller than 4%, proving the robustness of the method against random noise.

Key words: model updating, Kriging model, acceleration frequency response function, wavelet decomposition, correlation coefficients