Research Center for High-Speed Railway Network Management of Ministry of Education, School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, China.
Sensors (Basel). 2020 Jul 4;20(13):3753. doi: 10.3390/s20133753.
Bearings are ubiquitous in rotating machinery and bearings in good working conditions are essential for the availability and safety of the machine. Various intelligent fault diagnosis models have been widely studied aiming to prevent system failures. These data-driven fault diagnosis models work well when training data and testing data are from the same distribution, which is not easy to sustain in industry since the working environment of rotating machinery is often subject to change. Recently, the domain adaptation methods for fault diagnosis between different working conditions have been extensively researched, which fully utilize the labeled data from the same machine under different working conditions to address this domain shift diploma. However, for a target machine with seldom occurred faulty data under any working conditions, the domain adaptation approaches between working conditions are not applicable. Hence, the cross-machine fault diagnosis tasks are recently proposed to utilize the labeled data from related but not identical machines. The larger domain shift between machines makes the cross-machine fault diagnosis a more challenging task. The large domain shift may cause the well-trained model on source domain deteriorates on target domain, and the ambiguous samples near the decision boundary are prone to be misclassified. In addition, the sparse faulty samples in target domain make a class-imbalanced scenario. To address the two issues, in this paper we propose a semi-supervised adversarial domain adaptation approach for cross-machine fault diagnosis which incorporates the virtual adversarial training and batch nuclear-norm maximization to make the fault diagnosis robust and discriminative. Experiments of transferring between three bearing datasets show that the proposed method is able to effectively learn a discriminative model given only a labeled faulty sample of each class in target domain. The research provides a feasible approach for knowledge transfer in fault diagnosis scenarios.
轴承在旋转机械中无处不在，良好工作状态的轴承对于机器的可用性和安全性至关重要。各种智能故障诊断模型已被广泛研究，旨在防止系统故障。这些基于数据的故障诊断模型在训练数据和测试数据来自同一分布时效果很好，但在工业中很难维持，因为旋转机械的工作环境经常会发生变化。最近，针对不同工作条件下的故障诊断的域自适应方法得到了广泛研究，这些方法充分利用了同一机器在不同工作条件下的有标签数据来解决这种域迁移问题。然而，对于任何工作条件下故障数据都很少的目标机器，工作条件之间的域自适应方法就不适用了。因此，最近提出了跨机器故障诊断任务，以利用相关但不相同机器的有标签数据。机器之间更大的域偏移使得跨机器故障诊断成为一项更具挑战性的任务。较大的域偏移可能导致在源域上训练良好的模型在目标域上性能下降，并且决策边界附近的模糊样本容易被错误分类。此外，目标域中稀疏的故障样本导致了类别不平衡的情况。为了解决这两个问题，本文提出了一种用于跨机器故障诊断的半监督对抗性域自适应方法，该方法结合了虚拟对抗训练和批量核范数最大化，使故障诊断具有鲁棒性和判别性。在三个轴承数据集之间进行的转移实验表明，该方法能够在目标域中仅对每个类别的一个有标签的故障样本进行学习，从而有效地学习到一个有判别力的模型。这项研究为故障诊断场景中的知识迁移提供了一种可行的方法。
