A Novel Transfer Learning Framework Based on Deep Contrastive Convolution for Wind Turbine Bearing Fault Localization

Significant advancements have been achieved in the realm of wind turbine fault diagnosis through the application of deep learning. However, most methods generally assume that the distribution between training samples and testing samples remains consistent, which is impractical owing to the varying working conditions. Furthermore, the interpretability of the deep model has always been a focus of attention. Therefore, a novel interpretable empowered deep transfer framework (IEDTF) based on deep contrastive convolution, Wasserstein distance (WD), and improved Gramian angular summation field (IGASF) is proposed. By blending multichannel parallel and spatial polling mechanisms, the proposed method can adequately exploit multiscale information at a deep level from the source domain. Then, contrastive learning combined with the WD is proposed to establish relationships between data from different domains, which generalizes labeled data to unlabeled data. The IGASF is proposed for interpreting distinguishable transfer features intuitively. The effectiveness of the proposed method is demonstrated on the wind turbine bearing dataset with different bearing fault positions. Experimental results indicate that IEDTF can locate fault positions exactly under varying working conditions and shows high stability, which outperforms many existing transfer models applied in wind turbine bearing fault diagnosis.