Transfer learning has attracted great attention in intelligent fault diagnosis of bearings under different working conditions. However, existing studies have the following limitation. (1) The metric of feature distribution discrepancy between different working conditions is not sufficiently domain adaptive. (2) The decision boundaries among different classes are not sufficiently clear in the target domain. To overcome the aforementioned limitations: (1) A fault transfer diagnosis model based on deep convolution Wasserstein adversarial networks(DCWANs) is proposed to handle the first limitation; (2) A variance constraint is developed for the DCWAN-based model to increase the aggregation of extracted features, which enlarges the margins among features of different classes in the source domain and also helps in feature extraction by adaptively aligning features by classes under different working conditions, thus, overcoming the second limitation. Experimental results showed that the proposed model achieves a higher fault diagnosis accuracy than existing models.

传递学习在不同工况下的轴承智能故障诊断中引起了极大的关注。但是，现有研究具有以下局限性。（1）不同工作条件之间特征分布差异的度量标准不足以进行领域自适应。（2）在目标域中，不同类别之间的决策边界不够清晰。为克服上述局限性：（1）提出了一种基于深度卷积Wasserstein对抗网络（DCWAN）的故障转移诊断模型。（2）为基于DCWAN的模型开发了方差约束，以增加提取特征的集合，这扩大了源域中不同类别的特征之间的余量，并通过在不同工作条件下按类别自适应对齐特征来帮助进行特征提取，从而克服了第二个限制。实验结果表明，提出的模型比现有模型具有更高的故障诊断精度。