Abstract
To address the problem of insufficient feature extraction abilities of traditional fault diagnosis methods under conditions of sample scarcity and strong noise interference, a rolling bearing fault diagnosis method based on the Gramian Angular Difference Field (GADF) and Dynamic Self-Calibrated Convolution (DSC) is proposed. First, the GADF method converts one-dimensional signals into GADF images to capture nonlinear relationships and periodic information in time-series data. Second, a dynamic self-calibrated convolution module is introduced to enhance the feature extraction ability of the model. The DSC module dynamically adjusts the weights of parallel convolution kernels based on real-time data characteristics, effectively improving the feature extraction ability and generalization performance of the model. Finally, the proposed method is validated using bearing datasets from Huazhong University of Science and Technology and Harbin Institute of Technology, and is compared with other advanced models. The results show that the classification accuracy of the proposed method is basically above 90% when adding Gaussian white noise with a signal-to-noise ratio of -8 dB, which is a significant improvement of 6%-15% compared with other models. Therefore, the proposed method has excellent diagnostic performance in the rolling bearing fault diagnosis task under strong noise and small training samples.