It is difficult to obtain expensive labeled data in industrial fault diagnosis applications, which easily leads to overfitting of deep learning and restricts its extensive usage. Aiming at this issue, this article proposed an improved few-shot semisupervised learning method, called semisupervised momentum prototype network (SSMPN), to realize gearbox fault diagnosis under limited labeled samples. First, the proposed SSMPN utilizes the powerful few-shot learning ability of the prototype network to learn the feature mapping and obtains prototypes by using limited labeled samples. Then, a threshold selection based on Monte Carlo uncertainty is adopted in pseudo label learning to increase the confidence of pseudo labels. Finally, the expended labeled dataset is utilized to optimize feature extraction and the momentum prototype method is proposed to fine-tune the prototype of each category. The experiments on both test-bench and wind turbine gearbox fault diagnosis demonstrated that SSMPN is more effective than the comparable methods under the same situation.

中文翻译：

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有限标记样本下齿轮箱故障诊断的半监督动量原型网络


在工业故障诊断应用中，很难获得昂贵的标记数据，这很容易导致深度学习的过度拟合，限制了其广泛使用。针对这一问题，本文提出了一种改进的少样本半监督学习方法，称为半监督动量原型网络（SSMPN），以实现有限标记样本下的变速箱故障诊断。首先，所提出的SSMPN利用原型网络强大的少样本学习能力来学习特征映射，并通过使用有限的标记样本来获得原型。然后，在伪标签学习中采用基于蒙特卡罗不确定性的阈值选择，以增加伪标签的置信度。最后，利用扩展的标记数据集来优化特征提取，并提出动量原型方法来微调每个类别的原型。试验台和风力发电机齿轮箱故障诊断实验表明，在相同情况下，SSMPN比同类方法更有效。