In real industries, there often exist application scenarios where the target domain holds fault categories never observed in the source domain, which is an open-set domain adaptation (DA) diagnosis issue. Existing DA diagnosis methods under the assumption of sharing identical label space across domains fail to work. What is more, labeled samples can be collected from different sources, where multisource information fusion is rarely considered. To handle this issue, a multisource open-set DA diagnosis approach is developed. Specifically, multisource domain data of different operation conditions sharing partial classes are adopted to take advantage of fault information. Then, an open-set DA network is constructed to mitigate the domain gap across domains. Finally, a weighting learning strategy is introduced to adaptively weigh the importance on feature distribution alignment between known class and unknown class samples. Extensive experiments suggest that the proposed approach can substantially boost the performance of open-set diagnosis issues and outperform existing diagnosis approaches.

中文翻译：

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用于旋转机械开集故障诊断的多源加权深度传输网络

在实际行业中，经常存在目标域包含源域中从未观察到的故障类别的应用场景，这是一个开放集域自适应（DA）诊断问题。在跨域共享相同标签空间的假设下，现有的 DA 诊断方法无法正常工作。更重要的是，标签样本可以从不同的来源收集，很少考虑多源信息融合。为了解决这个问题，开发了一种多源开放集 DA 诊断方法。具体来说，采用共享部分类的不同运行条件的多源域数据来利用故障信息。然后，构建一个开放集 DA 网络以减轻跨域的域差距。最后，引入加权学习策略来自适应地权衡已知类和未知类样本之间特征分布对齐的重要性。大量实验表明，所提出的方法可以大大提高开放集诊断问题的性能，并优于现有的诊断方法。