时变转速下基于改进图注意力网络的轴承半监督故障诊断

新近的基于图神经网络(GNN)的轴承半监督故障诊断研究仍存在标签信息挖掘不充分和诊断场景较理想等问题。工程实际中，轴承经常运行于启停等时变转速工况，且故障标签样本的获取成本越发昂贵。针对以上挑战，该文提出时变转速下基于改进图注意力网络(GAT)的轴承半监督故障诊断新方法。基于K最近邻(KNN)算法和平滑假设(SA)设计伪标签传播策略，将标签信息沿边传播给分布相似的邻域样本，从而充分利用有限样本的标签信息。将每个振动频谱样本视为一个节点，构建基于节点级图注意力网络的半监督学习模型，通过注意力机制进一步挖掘代表性的轴承故障特征。将所提方法用于分析两组时变转速下轴承故障实验数据，结果表明所提方法能够在不超过2%的低标签率情况下，准确诊断轴承的不同故障模式，性能优于其他常用的图神经网络半监督学习方法。

Semi-supervised Bearing Fault Diagnosis Using Improved Graph Attention Network under Time-varying Speeds

Recent researches on semi-supervised bearing fault diagnosis based on Graph Neural Network (GNN) still have some problems, such as insufficient label information mining and relatively ideal diagnosis scenarios. In engineering practice, bearings are often operated under time-varying speeds such as startup and shutdown, and fault label samples become increasingly expensive. In response to the above challenges, a new method called semi-supervised bearing fault diagnosis using improved Graph ATtention network (GAT) under time-varying speeds is proposed. Based on K-Nearest Neighbor (KNN) algorithm and Smoothing Assumption (SA), the pseudo-label propagation strategy is designed to spread the label information to the neighborhood samples with similar distribution along the edge, so that the label information hidden in the limited samples can be fully utilized. Each vibration spectrum sample is considered as a node, and a semi-supervised learning model based on node-level GAN is constructed to explore further representative bearing fault features through the attention mechanism. The proposed method is applied to analyze two sets of bearing fault experimental data under time-varying speed, and the results show that the proposed method is able to diagnose accurately different fault modes of bearings at low label rates of no more than 2%, which is better than other commonly used semi-supervised learning methods of GNN.