Recently, transfer learning has been receiving growing interests in machinery fault diagnosis due to its strong generalization across different industrial scenarios. The existing methods generally assume identical label spaces, and propose minimizing marginal distribution discrepancy between source and target domains. However, this assumption usually does not hold in real industries, where testing data mostly contain a subspace of the source label space. Therefore, transferring diagnosis knowledge from a comprehensive source domain to a target domain with limited machine conditions is motivated. This challenging partial transfer learning problem is addressed in this study using deep learning-based domain adaptation method. A class weighted adversarial neural network is proposed to encourage positive transfer of the shared classes and ignore the source outliers. Experimental results on two rotating machinery datasets suggest the proposed method is promising for partial transfer learning.

近年来，由于转移学习在不同工业场景中的强大归纳能力，在机械故障诊断中引起了越来越多的兴趣。现有方法通常假定相同的标签空间，并建议最小化源域和目标域之间的边际分布差异。但是，这种假设通常不适用于实际行业，在这些行业中，测试数据大多包含源标签空间的子空间。因此，促使将诊断知识从有限的机器条件从全面的源域转移到目标域。此研究使用基于深度学习的领域自适应方法解决了这一具有挑战性的部分转移学习问题。提出了一种类别加权对抗神经网络，以鼓励共享类别的积极转移，并忽略源离群值。在两个旋转机械数据集上的实验结果表明，该方法对于部分传递学习是有希望的。