As a useful data augmentation technique, generative adversarial networks have been successfully applied in fault diagnosis field. But traditional generative adversarial networks can only generate one category fault signals in one time, which is time-consuming and costly. To overcome this weakness, we develop a novel fault diagnosis method which combines conditional generative adversarial networks and stacked autoencoders, and both of them are built by stacking one-dimensional full connection layers. First, conditional generative adversarial networks is used to generate artificial samples based on the frequency samples, and category labels are adopted as the conditional information to simultaneously generate different category signals. Meanwhile, spectrum normalization is added to the discriminator of conditional generative adversarial networks to enhance the model training. Then, the augmented training samples are transferred to stacked autoencoders for feature extraction and fault classification. Finally, two datasets of bearing and gearbox are employed to investigate the effectiveness of the proposed conditional generative adversarial network–stacked autoencoder method.

中文翻译：

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基于条件生成对抗网络的机器故障诊断数据增强方法

作为一种有用的数据增强技术，生成对抗网络已成功应用于故障诊断领域。但传统的生成对抗网络一次只能生成一类故障信号，既费时又费钱。为了克服这个弱点，我们开发了一种新的故障诊断方法，它结合了条件生成对抗网络和堆叠自编码器，它们都是通过堆叠一维全连接层来构建的。首先，利用条件生成对抗网络基于频率样本生成人工样本，并采用类别标签作为条件信息，同时生成不同类别的信号。同时，将频谱归一化添加到条件生成对抗网络的鉴别器中以增强模型训练。然后，增强的训练样本被转移到堆叠的自动编码器以进行特征提取和故障分类。最后，采用轴承和齿轮箱的两个数据集来研究所提出的条件生成对抗网络堆叠自编码器方法的有效性。