Recently, the deep transfer learning approaches have been widely developed for mechanical fault diagnosis issue, which could identify the health state of unlabeled data in the target domain with the help of knowledge learned from labeled data in the source domain. The tremendous success of these methods is generally based on the assumption that the label spaces across different domains are identical. However, the partial transfer scenario is more common for industrial applications, where the label spaces are not identical. This partial transfer scenario arises a more difficult problem that it is hard to know where to transfer since the shared label spaces are unavailable. To tackle this challenging problem, a double-layer attention based adversarial network (DA-GAN) is proposed in this paper. The proposed method sheds a new angle to deal with the question where to transfer by constructing two attention matrices for domains and samples. These attention matrices could guide the model to know which parts of data should be concentrated or ignored before conducting domain adaptation. Experimental results on both transfer in the identical machine (TIM) and transfer on different machines (TDM) suggest that the DA-GAN model shows great superiority on mechanical partial transfer problem.

近来，针对机械故障诊断问题的深度转移学习方法已得到广泛开发，该方法可以借助从源域中已标记数据中学到的知识来识别目标域中未标记数据的健康状态。这些方法的巨大成功通常是基于以下假设：跨不同域的标签空间是相同的。但是，部分传输方案对于标签空间不相同的工业应用更为常见。此部分传输方案会产生一个更困难的问题，因为共享标签空间不可用，因此很难知道要传输到哪里。为了解决这一难题，本文提出了一种基于双层注意力的对抗网络（DA-GAN）。通过为域和样本构建两个注意矩阵，提出的方法为转移问题提供了新的视角。这些关注矩阵可以指导模型在进行域自适应之前知道应集中或忽略哪些数据部分。在同一台机器上传输（TIM）和在不同机器上传输（TDM）的实验结果表明，DA-GAN模型在机械部分传输问题上显示出极大的优势。