Aiming at  the problem of low accuracy in bearing fault diagnosis caused by insufficient typical samples of fault data in the industrial domain and agricultural machinery,  a bearing fault classification method based on improved adversarial distillation is proposed.  The adversarial knowledge distillation method is used to classify the bearing faults. Based on the soft labels of the teacher network, the student network produces samples similar to those output by the student network. The modification of the parameters of the student network proceeds as the discriminator evaluates the samples. In this paper,  an annealed modified adversarial distillation method is proposed to improve the robustness and generalization ability of the student network. With dynamic temperature training in adversarial distillation, the difficulty of generating samples is increased for more efficient utilization of information from the teacher network. The effectiveness of the method is verified through experiments based on the bearing fault dataset from Case Western Reserve University in the United States. The student network trained with the proposed method achieves an accuracy of 91.85% in the simulation of onsite bearing fault diagnosis classification task, with only 214602 parameters involved in the computation, which not only improves the accuracy of fault diagnosis but also saves computing resources of the equipment.

针对工业现场条件下和农业机械设备故障数据典型样本不充足导致轴承故障诊断精度低的问题，提出一种基于改进对抗蒸馏的轴承故障分类方法。使用对抗蒸馏方法进行轴承故障分类，让学生网络通过对抗学习教师网络的软标签所提供的信息，同时生成器输出与学生网络输出相似的样本提供给判别器后修改学生网络参数。提出退火改进对抗蒸馏方法，在对抗蒸馏中使用动态温度进行训练，增加生成器制作样本难度，使教师网络输出的信息被更好地利用，以提高学生网络泛化能力和鲁棒性。试验使用美国凯斯西储大学轴承故障数据集验证方法的有效性，利用所提出的方法训练出的学生网络在模拟现场轴承故障诊断分类任务中仅使用214 602个参数参与计算，准确率可达91.85%，提高故障诊断精度并节省设备的计算资源。