A feature map of frequency response functions based model updating method using the Bayesian convolutional neural network

Abstract

Combined with the application of deep learning is a hot topic in the current research of finite element (FE) model updating. In this paper, A feature map of frequency response functions (FMFRF) based model updating method using the Bayesian convolutional neural network (BCNN) is proposed. This proposed method is based on frequency response functions (FRFs), which eliminates the steps of modal identification and modal matching and can adjust the structural parameters and damping parameters of the model at the same time, thus improving efficiency. The FRFs of multiple sensors are combined and transformed into the feature map, which is used as the input to the BCNN. The output of BCNN is the vector composed of uncertain model parameters. The FMFRF method solves the model updating problems as positive problems with the help of BCNN, and can accurately build the complex mapping between the FRFs and the model parameters. The BCNN introduces probability distributions over the weights, which helps avoid overfitting when training on small datasets and achieves high accuracy of the network. The benchmark multi-plate structure is used as an example to verify the validity and accuracy of the FMFRF method, and the correlation and sensitivity of the model parameters to the feature maps of FRFs are systematically analyzed. Finally, the robustness of the model updating results is investigated, and it proves that the FMFRF method shows good performance.