针对基于深度学习的滚动轴承故障诊断算法需要从大量标注数据中学习，且面对样本数量受限时诊断效果不佳的问题，文中提出了一种基于格拉姆角差场（GADF）和生成对抗网络（GAN）的小样本滚动轴承故障诊断方法。首先，提出了基于GADF变换的数据增强方式，将少数1维振动信号通过GADF变换转换为2维GADF图像，并通过裁剪得到GADF子图，从而得到大量的图像样本；然后，将条件生成对抗网络（CGAN）与带有梯度惩罚的Wasserstein GAN（WGAN-GP）相结合，构建一种新的生成对抗网络，该网络通过条件辅助信息与梯度惩罚增强模型训练稳定性，并设计动态坐标注意力机制以增强模型的空间感知能力，从而生成高质量样本；最后，使用生成的样本对分类器进行训练，并在验证集上得到诊断结果。文中分别使用东南大学数据集和美国凯斯西储大学（CWRU）数据集进行了两组小样本环境下的轴承故障诊断实验。结果表明，与传统生成对抗网络以及先进的小样本故障诊断方法相比，文中所提方法的准确率和精确率等5项故障诊断指标均获得最好的结果，可以准确诊断出小样本条件下的轴承故障类型。

Aiming at the problem that deep learning-based rolling bearing fault diagnosis algorithms need to learn from a large amount of labeled data and face poor diagnosis effect when the number of samples is limited, this paper proposed a small-sample rolling bearing fault diagnosis method based on the Gramian angular difference field (GADF) and generative adversarial networks (GAN). Firstly, a data enhancement method based on GADF transform was proposed, and it converts a few 1D vibration signals into 2D GADF images by GADF transform. GADF subgraphs are obtained by cropping to obtain a large number of image samples. Then, a conditional generative adversarial network (CGAN) was combined with Wasserstein GAN with gradient penalty (WGAN-GP) to construct a novel generative adversarial network, which enhances the model training stability by conditional auxiliary information with gradient penalty and designs dynamic coordinate attention mechanism to enhance the spatial perception of the model, so as to generate high-quality samples. Finally, the generative samples were used to train the classifier, and the diagnosis results were obtained on the validation set. Two sets of bearing fault diagnosis experiments in a small sample environment were conducted using the Southeast University dataset and the Case Western Reserve University dataset, respectively. The results show that, compared with traditional generative adversarial networks as well as advanced small-sample fault diagnosis methods, the proposed method can obtain the best results in five fault diagnosis metrics, including accuracy and precision, and can accurately diagnose the type of bearing faults under small-sample conditions.