Multisource cross-domain fault diagnosis of rolling bearing based on subdomain adaptation network
Multisource cross-domain fault diagnosis of rolling bearing based on subdomain adaptation network
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DOI：
10.1088/1361-6501/ac7941
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发表时间：
2022-06
影响因子：
2.4
通讯作者：
Zhichao Wang;Wentao Huang;Yi Chen;Yunchuan Jiang;Gaoliang Peng
中科院分区：
文献类型：
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作者：
Zhichao Wang;Wentao Huang;Yi Chen;Yunchuan Jiang;Gaoliang Peng
当前基于深度学习的智能故障诊断方法的优异性能归因于大量标记数据的可用性。然而，在实际轴承故障诊断中，大样本数据的高成本和运行工况的变化导致可用训练数据的稀缺，限制了智能轴承故障诊断的工程应用。针对这一问题，提出了一种基于多子网自适应网络的跨域故障诊断方法。首先，来自多个源域的数据被同时输入到由一维残差网络组成的共享特征提取器。然后，私有特征提取器用于学习来自不同源域的特征，并使用局部最大均值差异来减少每个源域和目标域的域偏移。最后，对齐目标域样本的不同分类器输出。MSDAN的亮点是从多个源域中获取诊断知识，并以类别为标准进一步划分子域，不仅使源域和目标域的全局分布保持一致，而且进行了更精细的子域对齐。该方法有效地消除了多序列迁移诊断中由于区域对齐不充分而引起的负迁移现象。通过构造7个多任务传递任务，并分别对两个轴承故障诊断案例（包括跨工况和跨机故障诊断案例）进行仿真，验证了MSDAN方法的有效性和优越性。
The excellent performance of current intelligent fault diagnosis methods based on deep learning is attributed to the availability of large amounts of labeled data. However, in practical bearing fault diagnosis, the high cost of large sample data and changes in operating conditions lead to the scarcity of available training data, which limits the engineering application of intelligent bearing fault diagnosis. To solve this problem, this paper proposes a cross-domain fault diagnosis method based on multisource subdomain adaptation networks (MSDAN). First, the data from multiple source domains are simultaneously input to a shared feature extractor composed of a one-dimensional residual network. Then, the private feature extractor is used to learn features from different source domains and reduce the domain shifts of each source and target domain using the local maximum mean discrepancy. Finally, the different classifier outputs of the target domain samples are aligned. The highlight of MSDAN is to obtain diagnostic knowledge from multiple source domains and further divide the subdomains using the categories as criteria, which not only aligns the global distribution of the source and target domain but also performs a more refined subdomain alignment. The method effectively alleviates the negative transfer phenomenon caused by insufficient domain alignment in multisource transfer diagnosis. The effectiveness and superiority of the proposed MSDAN method are verified by constructing seven multisource transfer tasks with two bearing fault diagnosis cases, including cross-operating-condition and cross-machine.