College of Sciences, Northeastern University, Shenyang 110819, China; Key Laboratory of Vibration and Control of Aero-Propulsion System Ministry of Education, Northeastern University, Shenyang 110819, China.
School of Aerospace Engineering, Shenyang Aerospace University, Shenyang 110136, China.
Neural Netw. 2020 Sep;129:313-322. doi: 10.1016/j.neunet.2020.06.014. Epub 2020 Jun 20.
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.
最近，迁移学习在机械故障诊断中受到越来越多的关注，因为它在不同的工业场景中具有很强的泛化能力。现有的方法通常假设相同的标签空间，并提出最小化源域和目标域之间的边缘分布差异。然而，在实际工业中，这种假设通常不成立，因为测试数据大多包含源标签空间的一个子空间。因此，从具有有限机器条件的目标域向全面的源域传输诊断知识是很有意义的。本研究使用基于深度学习的域自适应方法解决了这一具有挑战性的部分迁移学习问题。提出了一种类加权对抗神经网络，以鼓励共享类的正迁移并忽略源异常值。在两个旋转机械数据集上的实验结果表明，该方法对于部分迁移学习是有前景的。
