Mechanical fault diagnosis is crucial to ensure the safe operations of equipment in intelligent manufacturing systems. Deep learning-based methods have been recently developed for fault diagnosis due to their advantages in feature representation. However, most of these methods fail to learn relations between samples and thus perform poorly without sufficient labeled data. In this article, we propose a new few-shot learning method named dual graph neural network (DGNNet) with residual blocks to address fault diagnosis problems with limited data. First, the residual module learns the feature of samples with image data transferred from original signals. Second, two complete graphs built on the sample features are used to extract the instance-level and distribution-level relations between samples. In particular, an alternate update policy between the instance and distribution graphs integrates the multilevel relations to propagate the label information of a few labeled samples to unlabeled samples. This technique leverages labeled and unlabeled samples to identify unseen faults, encouraging DGNNet competency in fault diagnosis tasks with very few labeled samples. Extensive results on various datasets show that DGNNet achieves excellent performance in supervised fault diagnosis tasks and outperforms baselines by a great margin in semisupervised cases.

中文翻译：

---

使用双图神经网络进行故障诊断的小样本学习

机械故障诊断是保障智能制造系统设备安全运行的关键。由于其在特征表示方面的优势，最近开发了基于深度学习的方法用于故障诊断。然而，这些方法中的大多数都无法学习样本之间的关系，因此在没有足够的标记数据的情况下表现不佳。在本文中，我们提出了一种新的带有残差块的双图神经网络 (DGNNet) 的少样本学习方法，以解决数据有限的故障诊断问题。首先，残差模块使用从原始信号传输的图像数据来学习样本的特征。其次，基于样本特征构建的两个完整图用于提取样本之间的实例级和分布级关系。尤其是，实例图和分布图之间的替代更新策略集成了多级关系，以将少数标记样本的标签信息传播到未标记样本。该技术利用标记和未标记的样本来识别不可见的故障，从而鼓励 DGNNet 在具有极少标记样本的故障诊断任务中的能力。在各种数据集上的大量结果表明，DGNNet 在有监督的故障诊断任务中取得了出色的性能，并且在半监督的情况下大大优于基线。鼓励 DGNNet 在带有很少标记样本的故障诊断任务中的能力。在各种数据集上的大量结果表明，DGNNet 在有监督的故障诊断任务中取得了出色的性能，并且在半监督的情况下大大优于基线。鼓励 DGNNet 在带有很少标记样本的故障诊断任务中的能力。在各种数据集上的大量结果表明，DGNNet 在有监督的故障诊断任务中取得了出色的性能，并且在半监督的情况下大大优于基线。