Recent years, cross-domain fault diagnosis of rotating machinery has been a hot topic, and various kinds of methods taking advantage of transfer learning are proposed correspondingly. Despite their success, they mainly focus on marginal distribution alignments, which ignore weighing between marginal and conditional distributions in network training. However, this kind of weighting can boost diagnosis network performance further and make it more robust. Hence, a novel transfer learning method called discriminative feature-based adaptive distribution alignment (DFADA) is proposed, which can extract discriminative features and conduct a two-stage adaptive distribution alignment on L2 ball. In DFADA, maximum mean discrepancy (MMD) and graph Laplacian regularization are fused to extract discriminative and task-specific features. Meanwhile, for comprehensive and adaptive distribution alignments, the distributions of datasets are pre-matched via MMD and further matched in feature classifier via dynamic distribution alignment (DDA), which can not only reduce both marginal and conditional distribution discrepancies but also weigh their importance adaptively. Finally, a DFADA-based fault diagnosis method for rotating machinery with volatile working conditions is constructed correspondingly. The validity of the proposed method is also confirmed by extensive experiments and comparisons with some state of the arts on 18 transfer learning cases.

近年来，旋转机械的跨域故障诊断一直是一个热门话题，相应地提出了多种利用传递学习的方法。尽管取得了成功，但他们主要关注边际分布对齐方式，而忽略了网络训练中边际分布和条件分布之间的权衡。但是，这种加权可以进一步提高诊断网络的性能并使其更可靠。因此，提出了一种新的转移学习方法，称为基于判别特征的自适应分布对准（DFADA），该方法可以提取判别特征并在L2球上进行两阶段自适应分布对准。在DFADA中，融合了最大平均差异（MMD）和图拉普拉斯正则化以提取区分特征和特定于任务的特征。与此同时，对于全面的自适应分布比对，数据集的分布通过MMD进行预匹配，然后通过动态分布比对（DDA）在特征分类器中进行进一步匹配，这不仅可以减少边际和条件分布的差异，而且可以自适应地权衡其重要性。最后，建立了基于DFADA的旋转条件下旋转机械故障诊断方法。通过广泛的实验以及与18个转移学习案例的一些现有技术的比较，也证实了该方法的有效性。这不仅可以减少边际和条件分布的差异，而且可以自适应地权衡其重要性。最后，建立了基于DFADA的旋转条件下旋转机械故障诊断方法。通过广泛的实验以及与18个转移学习案例的一些现有技术的比较，也证实了该方法的有效性。这不仅可以减少边际和条件分布的差异，而且可以自适应地权衡其重要性。最后，建立了基于DFADA的旋转条件下旋转机械故障诊断方法。通过广泛的实验以及与18个转移学习案例的一些现有技术的比较，也证实了该方法的有效性。