LAUSR

The small sample problem is an open challenge in industrial data-driven fault diagnosis. Domain adaptation (DA) has been successfully used to enhance small samples by transferring fault samples in other working conditions, but it suffers from low accuracy and poor generalization for modeling. To address this issue, in this article, a task-based bias DA network (TBDA-net) is proposed. First, an adaptive dimension alignment subnet is proposed, which overcomes the information loss of the target domain caused by the dimension alignment of different domains, thus the accuracy of modeling is improved. Second, a task-based distribution matching subnet is proposed, in which the correlation difference of domains is considered and the designed auxiliary branch is embedded in the matching network to protect the sample diversity of multisource domains, thus the generalization of modeling is improved. Finally, benchmark simulated experiments and real-world application experiments are conducted to evaluate the proposed method. Experiment results show the TBDA-net outperforms the existing methods for modeling under industrial small samples