Search

ABSTRACTBearings are widely applied in rotating machinery of nuclear power plants (NPPs). Data-driven fault diagnosis technology is critical to ensuring the reliable operation of rotating machinery. Aiming at the problem of poor model generalization ability caused by discrepant data distribution of monitoring signals under various working conditions, a deep transfer learning method based on fully categorized alignment subdomain adaptation (FCA-SAN) is proposed in this paper. Firstly, the bearing vibration signals of the source and target operating conditions are preprocessed and converted into time-frequency domain images suitable for model input. Subsequently, a pre-trained deep convolutional neural network (DCNN) model is adopted as the feature extractor, which is combined with FCA-SAN to extract transferable features across different working conditions. The subdomain adaptation method reduces the data distribution discrepancy more fine-grained by aligning the feature distribution of different working conditions, thereby effectively improving the model generalization ability. Finally, the experimental results show that, compared with the traditional method, the proposed subdomain adaptation method reaches the highest fault diagnosis accuracy in different transfer tasks, which demonstrates the potential application value in rotating machinery of NPPs.