Rolling bearing fault diagnosis is of great significance to the stable operation of rotating machinery systems. However, the fault data collected in practical engineering is seriously imbalanced, which degrades the diagnosis performance. In this paper, a novel data synthesis method called deep feature enhanced generative adversarial network is proposed to improve the performance of imbalanced fault diagnosis. Firstly, to avoid the mode collapse phenomenon and improve the stability of the generative adversarial networks, a pull-away function is integrated to design a new objective function of the generator. Secondly, a self-attention module is utilized in the networks to enhance the deep features of real signals, thereby the quality of synthesized data is improved. Finally, an automatic data filter is established to timely ensure the accuracy and diversity of synthesized samples. Experiments are implemented on two rolling bearing datasets. The results indicate that the proposed method outperforms other intelligent methods and shows great potential in imbalanced fault diagnosis.

滚动轴承故障诊断对旋转机械系统的稳定运行具有重要意义。然而，实际工程中采集的故障数据严重不平衡，降低了诊断性能。在本文中，提出了一种称为深度特征增强生成对抗网络的新型数据合成方法，以提高不平衡故障诊断的性能。首先，为了避免模式崩溃现象并提高生成对抗网络的稳定性，集成了一个pull-away函数来设计一个新的生成器目标函数。其次，在网络中利用自注意力模块来增强真实信号的深层特征，从而提高合成数据的质量。最后，建立自动数据过滤器，及时保证合成样本的准确性和多样性。实验在两个滚动轴承数据集上进行。结果表明，所提出的方法优于其他智能方法，在不平衡故障诊断中显示出巨大的潜力。