Unsupervised domain adaptation-based fault diagnosis methods have been extensively studied due to their powerful knowledge transferability under different working conditions. Despite their encouraging performance, most of them cannot sufficiently account for the temporal dimension of the vibration signal, resulting in incomplete feature information used in the domain alignment procedure. To alleviate the limitation, we present a self-supervised domain adaptation fault diagnosis network (SDAFDN), which considers two temporal dependencies to improve the transferability of the learned representations. Specifically, we first design a down-sampling and interaction network that considers the temporal dependency among subsequences with low temporal resolution in feature space. Then, we combine domain adversarial learning with feature mapping to achieve domain alignment. Finally, we introduced a self-supervised learning module, which considers the temporal dependency between the past and future temporal segments via classification tasks. Extensive experiments on public Paderborn University and PHM data sets demonstrate the superiority of the proposed SDAFDN and the effectiveness of considering temporal dependencies in domain alignment.

中文翻译：

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用于跨域故障诊断的自监督域自适应

基于无监督域自适应的故障诊断方法因其在不同工作条件下强大的知识可迁移性而得到广泛研究。尽管它们的性能令人鼓舞，但它们中的大多数不能充分考虑振动信号的时间维度，导致域对齐过程中使用的特征信息不完整。为了缓解这种局限性，我们提出了一种自监督域自适应故障诊断网络 (SDAFDN)，它考虑了两个时间依赖性以提高学习表示的可迁移性。具体来说，我们首先设计了一个下采样和交互网络，该网络考虑了特征空间中具有低时间分辨率的子序列之间的时间依赖性。然后，我们将领域对抗学习与特征映射相结合以实现领域对齐。最后，我们引入了一个自监督学习模块，它通过分类任务考虑了过去和未来时间段之间的时间依赖性。在公共帕德博恩大学和 PHM 数据集上进行的大量实验证明了所提出的 SDAFDN 的优越性以及在域对齐中考虑时间依赖性的有效性。