A few-shot based phase-batch multi-layer domain adaptation pattern recognition method

Abstract

Deep transfer learning has been widely applied in the field of intelligent fault diagnosis. However, existing deep transfer learning-based diagnostic methods struggle to train reliable diagnostic models when there is a lack of data and significant distribution differences between the two domains. To address this issue, a few-shot based phase-batch multi-layer domain adaptation pattern recognition method is proposed. This method simultaneously measures the feature distribution differences of both the fully connected layers and the classification layers, thus better correcting the data domain bias. Additionally, a phase-batch training strategy and pseudo-label learning are employed to improve the convergence speed and stability of the training process. The proposed method is validated on two public datasets, Jiang Nan and Paderborn University, as well as a dataset obtained through independent experiments. It is compared with traditional feature-based transfer learning methods, the results show that the proposed method achieves higher diagnostic accuracy, faster convergence, and greater stability. Furthermore, its superior diagnostic performance in the few-shot scenario is demonstrated through experiments on a self-collected dataset.