Industrial equipment fault diagnosis often encounter challenges such as the
scarcity of fault data, complex operating conditions, and varied types of
failures. Signal analysis, data statistical learning, and conventional deep
learning techniques face constraints under these conditions due to their
substantial data requirements and the necessity for transfer learning to
accommodate new failure modes. To effectively leverage information and extract
the intrinsic characteristics of faults across different domains under limited
sample conditions, this paper introduces a fault diagnosis approach employing
Multi-Scale Graph Convolution Filtering (MSGCF). MSGCF enhances the traditional
Graph Neural Network (GNN) framework by integrating both local and global
information fusion modules within the graph convolution filter block. This
advancement effectively mitigates the over-smoothing issue associated with
excessive layering of graph convolutional layers while preserving a broad
receptive field. It also reduces the risk of overfitting in few-shot diagnosis,
thereby augmenting the model's representational capacity. Experiments on the
University of Paderborn bearing dataset (PU) demonstrate that the MSGCF method
proposed herein surpasses alternative approaches in accuracy, thereby offering
valuable insights for industrial fault diagnosis in few-shot learning
scenarios.

本文针对工业设备故障诊断中的数据稀缺、复杂操作条件和各种故障类型等挑战，介绍了一种使用多尺度图卷积滤波（MSGCF）的故障诊断方法。该方法通过整合局部和全局信息融合模块，在图卷积滤波块内增强传统图神经网络（GNN）框架，有效地解决了过多层图卷积层导致的过度平滑问题，并保持了广泛的感受野。在少样本诊断方面，该方法降低了过拟合风险，增加了模型的表示能力。对帕德博恩大学轴承数据集进行的实验表明，所提出的 MSGCF 方法在准确性上优于其他方法，为少样本学习场景中的工业故障诊断提供了有价值的洞见。