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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

邵海东 颜深 肖一鸣 刘翊

邵海东, 颜深, 肖一鸣, 刘翊. 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[J]. 电子与信息学报, 2023, 45(5): 1550-1558. doi: 10.11999/JEIT220303
引用本文: 邵海东, 颜深, 肖一鸣, 刘翊. 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[J]. 电子与信息学报, 2023, 45(5): 1550-1558. doi: 10.11999/JEIT220303
SHAO Haidong, YAN Shen, XIAO Yiming, LIU Yi. Semi-supervised Bearing Fault Diagnosis Using Improved Graph Attention Network under Time-varying Speeds[J]. Journal of Electronics & Information Technology, 2023, 45(5): 1550-1558. doi: 10.11999/JEIT220303
Citation: SHAO Haidong, YAN Shen, XIAO Yiming, LIU Yi. Semi-supervised Bearing Fault Diagnosis Using Improved Graph Attention Network under Time-varying Speeds[J]. Journal of Electronics & Information Technology, 2023, 45(5): 1550-1558. doi: 10.11999/JEIT220303

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

doi: 10.11999/JEIT220303
基金项目: 国家重点研发计划(2020YFB1712100),国家自然科学基金(51905160),湖南省优秀青年科学基金(2021JJ20017),上海市空间导航与定位技术重点实验室开放课题(202105)
详细信息
    作者简介:

    邵海东:男,博士,副教授,博士生导师,研究方向为故障诊断与智能运维、数据挖掘与信息融合

    颜深:男,硕士生,研究方向为图神经网络与半监督学习

    肖一鸣:男,硕士生,研究方向为深度迁移学习与智能故障诊断

    刘翊:男,博士,教授级高级工程师,研究方向为智能制造与工业大数据

    通讯作者:

    邵海东 hdshao@hnu.edu.com

  • 中图分类号: TH133.3; TP183

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

Funds: The National Key Research and Development of China (2020YFB1712100), The National Natural Science Foundation of China (51905160), The Natural Science Fund for Excellent Young Scholars of Hunan Province (2021JJ20017), The Opening Project of Shanghai Key Laboratory of Space Navigation and Positioning Techniques (202105)
  • 摘要: 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。
  • 图  1  CNN和GNN的卷积示意图

    图  2  基于频谱信号的频谱样本KNN图构建流程

    图  3  基于KNN-SA的伪标签传播策略

    图  4  节点级图注意力层的特征更新机制

    图  5  节点级GAT半监督学习模型构建

    图  6  两个案例中4种GNNs的诊断准确率

    图  7  案例1各方法第5次实验的混淆矩阵图

    图  8  案例2各方法第5次实验的特征2维可视化

    图  9  两个案例中不同k值下所提方法的诊断准确率和伪标签准确率

    图  10  案例1中所提方法在不同低标签率下的诊断效果

    算法1 所提方法的算法流程
     输入:含伪标签的训练集(训练输入),无标签的测试集(测试输入)
     (1)设定k值、图注意力层层数、激活函数、学习率、迭代次数
      T等模型超参数
     (2)随机初始化模型的权重等参数
     (3)基于式(3)求解$ {L_{ij}} $,基于式(4)构造边,得到频谱样本KNN图
     (4)基于式(5)得到标签样本集Y
     (5)For epoch in range(T):
     (6)   基于式(8)、式(9)得到输出节点集合Z
     (7)   基于式(10)计算损失函数Loss
     (8)   根据损失函数Loss基于Adam优化器更新模型参数
     (9)End For
     (10)将训练好的模型用于测试集的预测
     输出:测试集预测标签的准确率
    下载: 导出CSV

    表  1  轴承数据集信息

    故障诊断案例时变转速(r/min)轴承状态样本总数标签样本数训练样本数
    案例10-3 000-0正常2 100(300×7)42(6×7)(标签率2%)350(50×7)
    内圈轻度故障
    内圈中度故障
    内圈重度故障
    外圈轻度故障
    外圈中度故障
    外圈重度故障
    案例2882-1 518-1 260正常1 000(200×5)5(1×5)(标签率0.5%)50(10×5)
    906-1 464-1 122内圈故障
    865-1 421-928滚珠故障
    840-1 302-870外圈故障
    795-1 280-766复合故障
    下载: 导出CSV

    表  2  各方法的诊断结果和运行时间

    半监督故障诊断方法准确率和方差 (%)训练时间 (s)测试时间 (s)
    案例1案例2案例1案例2案例1案例2
    改进图注意力网络(KNN-SA-GAT)89.62±3.3699.58±0.455.396.200.010.01
    GAT86.07±4.4298.55±0.815.396.210.010.01
    KNN-SA-GraphSage84.92±0.8782.38±2.224.496.640.010.03
    GraphSage83.82±2.6167.01±12.444.496.630.010.03
    KNN-SA-GCN84.53±2.5984.99±4.444.595.240.010.01
    GCN80.63±5.1174.55±7.584.555.230.010.01
    KNN-SA-ChebyNet86.25±5.5879.85±4.395.407.440.020.05
    ChebyNet80.79±2.1747.85±10.485.397.450.020.05
    下载: 导出CSV
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出版历程
  • 收稿日期:  2022-03-21
  • 修回日期:  2022-06-22
  • 网络出版日期:  2022-06-25
  • 刊出日期:  2023-05-10

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