Deep learning, especially transfer learning, has made a great deal of extraordinary achievements in intelligent fault diagnosis. In practical situations, data shift problem is inevitable due to complicated and changeable working conditions. Ignoring this problem may result in considerable degradation of diagnostic accuracy. Thus, domain distance is measured in only one metric space, and the result of domain alignment may not be ideal. This paper proposes a novel transfer learning method named hybrid distance-guided adversarial network (HDAN) to deal with this problem. Specifically, HDAN contains two parts: a feature extractor composed of a convolutional neural network and a shared classifier. Wasserstein distance and multi-kernel maximum mean discrepancy are applied in the proposed method to measure the domain distance in different metric spaces for improving the result of domain alignment. The domain distance of the last two hidden layers is minimized to improve the efficiency of domain-invariant feature extraction. Two experiments are implemented to confirm the superiority of the proposed method. The results of two experiments demonstrate that the proposed HDAN can achieve better feature cluster performance of the same class in different domains than the methods selected for comparison.

深度学习，尤其是转移学习，在智能故障诊断中取得了非凡的成就。在实际情况下，由于工作条件复杂多变，不可避免地会发生数据转移问题。忽略此问题可能会导致诊断准确性大大降低。因此，仅在一个度量空间中测量域距离，并且域对齐的结果可能不是理想的。针对这一问题，本文提出了一种新颖的转移学习方法，称为混合距离导引对抗网络（HDAN）。具体来说，HDAN包含两个部分：由卷积神经网络和共享分类器组成的特征提取器。提出的方法利用Wasserstein距离和多核最大均值差异来测量不同度量空间中的域距离，以改善域对齐的结果。最后两个隐藏层的域距离被最小化，以提高域不变特征提取的效率。进行了两个实验，以确认该方法的优越性。两次实验的结果表明，与选择进行比较的方法相比，所提出的HDAN可以在不同的领域中实现更好的特征类聚类性能。进行了两个实验，以确认该方法的优越性。两次实验的结果表明，与选择进行比较的方法相比，所提出的HDAN可以在不同的领域中实现更好的特征类聚类性能。进行了两个实验，以确认该方法的优越性。两次实验的结果表明，与选择进行比较的方法相比，所提出的HDAN可以在不同的领域中实现更好的特征类聚类性能。