Rolling Bearing Fault Diagnosis Based on Transfer Learning and Dual-Channel CNN and BiGRU

Intelligent machine fault diagnosis methods that leverage machine learning techniques have received widespread attention owing to their proven efficacy in enhancing production efficiency and quality as well as in reducing production costs. However, given the scarcity of rolling bearing failure data, traditional neural network training demonstrates weak noise immunity and limited network generalization capabilities. To address these issues, this study proposes a rolling bearing fault diagnosis method that combines a dual-channel hybrid domain neural network with transfer learning. The innovatively designed MDRSBU-MA module and BiGRU hybrid channel demonstrate enhanced fault feature extraction and anti-noise capabilities across domains, thus obviating the need for denoising algorithms. Furthermore, a diverse array of transfer tasks was designed for various bearing fault datasets and under differing working conditions. Experimental results suggest that the model retains robust fault diagnosis capability, particularly in terms of resistance to noise interference, small-sample cross-working condition domain, and cross-device domain transfer tasks.