In actual production, bearings are usually in a normal working state, which results in a lack of data for fault diagnosis (FD). Yet, the majority of existing studies on FD of rolling bearings focus on scenarios with ample fault data, while research on diagnosing small-sample bearings remains scarce. Therefore, this study presents an FD method for small-sample bearings, employing variational-mode decomposition and Symmetric Dot Pattern, combined with a pre-trained and fine-tuned Residual Network18 (VSDP-TLResNet18). The approach utilizes variational-mode decomposition (VMD) to break down the signal, determining the k value and the best Intrinsic-Mode Function (IMF) component based on center frequency and kurtosis criteria. Following this, the chosen IMF component is converted into a two-dimensional image using the Symmetric Dot Pattern (SDP) transform. In order to maximize the discrimination between two-dimensional fault images, Pearson correlation analysis is carried out on the parameters of SDP to select the optimal parameters. Finally, we use the pre-trained and fine-tuned method combined with ResNet18 for small-sample FD to improve the diagnosis accuracy of the model. Relative to alternative approaches, the suggested method demonstrates strong performance when dealing with small-sample FD.

Fault data under real operating conditions are often difficult to collect, making the number of trained fault data small and out of proportion to normal data. Thus, fault diagnosis symmetry (balance) is compromised. This will result in less effective fault diagnosis methods for cases with a small number of data and data imbalances (S&I). We present an innovative solution to overcome this problem, which is composed of two components: data augmentation and fault diagnosis. In the data augmentation section, the S&I dataset is supplemented with a deep convolutional generative adversarial network based on a gradient penalty and Wasserstein distance (WDCGAN-GP), which solve the problems of the generative adversarial network (GAN) being prone to model collapse and the gradient vanishing during the training time. The addition of self-attention allows for a better identification and generation of sample features. Finally, the addition of spectral normalization can stabilize the training of the model. In the fault diagnosis section, fault diagnosis is performed through a convolutional neural network with coordinate attention (CNN-CA). Our experiments conducted on two bearing fault datasets for comparison demonstrate that the proposed method surpasses other comparative approaches in terms of the quality of data augmentation and the accuracy of fault diagnosis. It effectively addresses S&I fault diagnosis challenges.