Deep semi-supervised transfer learning method on few source data with sensitivity-aware decision boundary adaptation for intelligent fault diagnosis

Abstract

The data distribution shifting under varied working conditions impedes the application of deep learning models for fault diagnosis. Transfer learning can reduce the discrepancy in data distribution and improve model generalization from one condition with abundant labeled source data to another condition with unlabeled target data. However, existing transfer learning methods may hardly be applied to scenarios with few source domain data, and the insufficient source data hinder the establishment of the initial model. To address this issue, a deep semi-supervised transfer learning method with sensitivity-aware decision boundary adaptation (Sa-DBA) is proposed in this paper. The proposed triangular consistency in semi-supervised learning module can ensure the construction of the initial decision boundary. In transfer process, the Sa-DBA directly reduces the discrepancy of predicted matrixes between two domains to facilitate the adaptation of decision boundaries to the target data. Given that target data adjacent to decision boundaries may exhibit varying boundary sensitivities across different tasks, the nuclear-norm with task-specific parameter is introduced to further refine decision boundaries. Finally, extensive experiments on three diverse datasets, including CWRU, PHM, our own DPS test bench, and the best average accuracy reached 99.70%, 86.41%, and 88.59% respectively, which performed better than the other methods.