Small sample cross-domain bearing fault diagnosis method based on signal denoising lightweight model

Abstract During the operation of bearings, their structural parameters, loads, signal transmission paths, etc, are highly dynamic, resulting in obvious distribution differences in the statistical characteristics of the data. In addition, the real fault data set is often too small and lacks fault labels, resulting in poor performance of data-driven neural networks in small sample cross-domain fault diagnosis. In order to solve the above problems faced by engineering practice and achieve high-precision fault diagnosis, a signal denoising lightweight (FMECR-18) model for small sample cross-domain bearing fault diagnosis is proposed. First, the two-domain signal data is denoised using a denoising module (FME) based on the combination of eigenmode decomposition (FMD) and multiscale entropy screening (MSE), and the denoised vibration signal is upgraded to a multi-dimensional signal through channel expansion. Next, the basic structure of the ResNet18 model is changed by replacing the conventional convolution residual module with a deformable convolution residual module and a convolution attention module (CBAM) to create a CR-18 model. Using the fine-tuning strategy discussed in this paper, the ability of the FMECR-18 model to extract fault features for two types of data and the effectiveness of bearing fault classification are improved by training two types of data and adjusting the model settings. Using the fine-tuning strategy theory proposed in this paper, the fault feature extraction ability of the FMECR-18 model for dual-domain data and the bearing fault classification effect are improved through dual-domain data training and model freezing adjustment. Six cross-dataset migration tasks are constructed using 7 different public datasets. The average diagnostic results of the target domain test set of the FMECR-18 model are 99.89%, 99.70%, 99.61%, 98.18%, 96.91%, and 96.06%, respectively, indicating that the method proposed in this paper has achieved good results in cross-dataset migration tasks.