Abstract:  Few bearing samples usually led to inadequate learning and low diagnosis accuracy. To solve this problem， an improved convolutional generative adversarial network was constructed. It made full use of data generation ability of the GAN and learning ability of deep CNN， so that the intelligent fault diagnosis might be conducted for the bearings with few samples under varying working conditions. First， a deep convolutional GAN was constructed. The deep features in few real data were mined through adversarial learning between the generator and discriminator of GAN， and then the generator might generate simulated data exactly like real one to make up for the lack of very few samples. Then， the dense block and dilated convolutions were combined with the CNN， named as DDCNN， to improve the learning ability by extending the network depth and perception range. As a result， the DDCNN may identify tiny differences in multi-class datasets and enhance feature extraction. Finally， the proposed method was verified by using bearing datasets with few samples under fixed conditions and varying rotating speeds， and was compared with other frameworks. The experimental results indicate that DDCNN has higher diagnosis accuracy for bearings with few samples and noisy conditions. 

Key words: generative adversarial network（GAN）, convolutional neural network（CNC）, fault diagnosis, bearing, few-shot learning

摘要： 轴承样本较少会使模型学习不充分，导致诊断准确性不高。为解决这一问题，构建了一种改进的卷积生成对抗网络，借助生成对抗网络的数据生成能力和改进深层卷积网络的特征提取能力，提高复杂工况下少样本轴承故障诊断准确性。首先，构建了一种深度卷积对抗生成网络，通过生成器和判别器的对抗学习挖掘真实数据的深层特征，用以生成相似的模拟数据，以弥补少样本的不足；其次，将密集块与扩容卷积引入卷积神经网络中，从深度和广度两个方面提升网络的学习能力，挖掘多类别数据中细微差距，增强复杂数据的故障特征提取性能；最后，采用定工况和变转速两种少样本轴承数据进行方法验证与对比分析，结果表明新构建的对抗网络在少样本、含噪声等复杂情形下仍然具有较高的诊断准确率。

关键词: 生成对抗网络, 卷积神经网络, 故障诊断, 轴承, 少样本学习