Intelligent Fault Diagnosis With Deep Adversarial Domain Adaptation
Intelligent Fault Diagnosis With Deep Adversarial Domain Adaptation
复制标题
具有深度对抗域适应的智能故障诊断
DOI：
10.1109/tim.2020.3035385
复制
发表时间：
2021-01-01
影响因子：
5.6
通讯作者：
Yang, Ying
中科院分区：
文献类型：
--
作者：
Wang, Yu;Sun, Xiaojie;Yang, Ying
随着基于深度学习的故障诊断方法的快速发展，许多研究都对智能故障诊断方法的迁移进行了研究，以学习不同条件下机器的领域不变特征。以往的研究主要集中在通过领域自适应来学习领域不变特征。然而，域对齐方法不能消除域的漂移，目标样本可能会被从源域学习的决策边界错误地分类，最终导致域在错误的方向上对齐。针对这一问题，我们提出了一种深度对战域自适应网络(DADAN)来传递故障诊断知识。达丹使用基于沃瑟斯坦距离的领域对抗性训练，从原始信号中学习领域不变特征。此外，将该网络与一种基于有监督实例的方法相结合，学习具有较好类内内聚力和类间可分性的区分特征，从而有利于领域对齐。使用包括三种速度条件的轴承数据集以及由加速退化试验和实际条件获得的硬盘数据集来评估所提出的DADAN的性能。
With the rapid development of fault diagnosis methods based on deep learning, many studies have investigated the transfer of intelligent fault diagnosis methods to learn the domain-invariant features of machines under different conditions. Previous researches focused on learning domain-invariant features through domain adaptation. However, the domain alignment methods cannot remove the domain shift, the target samples may be incorrectly classified by the decision boundary learned from the source domain and eventually cause the domains to be aligned in the wrong direction. To cope with this problem, we propose a deep adversarial domain adaptation network (DADAN) to transfer fault diagnosis knowledge. DADAN uses domain-adversarial training based on the Wasserstein distance to learn domain-invariant features from the raw signal. In addition, the network is combined with a supervised instance-based method to learn the discriminative features with better intraclass cohesion and interclass separability, which can benefit the domain alignment. A data set of bearing data including three speed conditions and a data set of hard disk data acquired from accelerated degradation test and real-case conditions were used to evaluate the performance of the proposed DADAN.