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基于冲击特征提取的旋转机械智能故障诊断

胡爱军 孙俊豪 邢磊 向玲

胡爱军, 孙俊豪, 邢磊, 等. 基于冲击特征提取的旋转机械智能故障诊断[J]. 航空动力学报, 2023, 38(12):2973-2981 doi: 10.13224/j.cnki.jasp.20220106
引用本文: 胡爱军, 孙俊豪, 邢磊, 等. 基于冲击特征提取的旋转机械智能故障诊断[J]. 航空动力学报, 2023, 38(12):2973-2981 doi: 10.13224/j.cnki.jasp.20220106
HU Aijun, SUN Junhao, XING Lei, et al. Intelligent fault diagnosis of rotating machinery based on impact feature extraction[J]. Journal of Aerospace Power, 2023, 38(12):2973-2981 doi: 10.13224/j.cnki.jasp.20220106
Citation: HU Aijun, SUN Junhao, XING Lei, et al. Intelligent fault diagnosis of rotating machinery based on impact feature extraction[J]. Journal of Aerospace Power, 2023, 38(12):2973-2981 doi: 10.13224/j.cnki.jasp.20220106

基于冲击特征提取的旋转机械智能故障诊断

doi: 10.13224/j.cnki.jasp.20220106
基金项目: 国家自然科学基金(52175092)
详细信息
    作者简介:

    胡爱军(1971-),男,教授、博士生导师,博士,主要从事机械设备状态监测与故障诊断研究。E-mail:bdlaohu@126.com

    通讯作者:

    孙俊豪(1998-),男,硕士,主要从事旋转机械设备状态监测与诊断研究。E-mail:799388772@qq.com

  • 中图分类号: V240.2;TH132;TH133.33

Intelligent fault diagnosis of rotating machinery based on impact feature extraction

  • 摘要:

    针对齿轮、轴承故障,提出了基于冲击特征提取胶囊网络的旋转机械智能故障诊断模型。在胶囊网络的构架基础上,将原始故障振动信号作为输入,通过构造首层小波核卷积层,针对性提取冲击故障特征,提高深度学习网络特征提取的可解释性。在小波核卷积层之后扩展一层卷积层,强化首层小波核卷积层提取的特征,将强化的特征经初级胶囊层、数字胶囊层输出分类结果,从而构造了“端到端”的小波卷积胶囊网络模型。通过对各层提取的特征可视化分析,证明了该模型对故障振动信号的冲击特征具有良好的提取能力。3个不同实验平台的数据集验证结果表明不同故障类型、不同故障程度的齿轮及轴承的识别精度最高可达到100%,并具有良好的泛化能力。

     

  • 图 1  冲击特征提取胶囊网络模型图

    Figure 1.  Impact feature extraction capsule network model diagram

    图 2  智能诊断流程图

    Figure 2.  Intelligent diagnosis flow chart

    图 3  华北电力大学齿轮箱实验台

    Figure 3.  Gear box test bench of North China Electric Power University

    图 4  齿轮故障类型及其时域波形

    Figure 4.  Gear fault type and the time domain waveform

    图 5  华北电力大学齿轮箱数据集CAPS、LW-CAPS、C-CAPS和IF-CAPS模型每层的t-SNE可视化图

    Figure 5.  Visualization diagram of t-SNE for each layer of CAPS, LW-CAPS, C-CAPS and IF-CAPS models based on gearbox dataset of North China Electric Power University

    图 6  凯斯轴承实验台

    Figure 6.  Case bearing test bench

    图 7  不同来源的两组数据在4种模型下准确率对比图

    Figure 7.  Comparison of accuracy of two groups of data from different sources under four models

    图 8  东南大学齿轮箱数据集IF-CAPS模型的t-SNE聚类结果

    Figure 8.  t-SNE clustering diagram of IF-CAPS model based on the gearbox dataset of Southeast University

    图 9  凯斯西储大学轴承数据集IF-CAPS模型的t-SNE聚类结果

    Figure 9.  t-SNE clustering diagram of IF-CAPS model based on the bearing dataset of Case Western Reserve University

    表  1  华北电力大学齿轮箱样本集

    Table  1.   Gearbox sample set of North China Electric Power University

    标签故障类型训练样本数测试样本数
    0正常状态420105
    1大齿轮点蚀故障420105
    2大齿轮断齿故障420105
    3小齿轮磨损故障420105
    4大齿轮断齿+小齿轮磨损420105
    5大齿轮点蚀+小齿轮磨损420105
    下载: 导出CSV

    表  2  IF-CAPS模型具体参数

    Table  2.   Specific parameters of IF-CAPS model

    层类型核尺寸步长输出尺寸
    小波核卷积层15×1164×64×387
    卷积层215×1164×256×373
    初级胶囊层9×125856×8
    数字胶囊层6×16
    下载: 导出CSV

    表  3  3种模型测试精度

    Table  3.   Test accuracy of three models

    方法精度/%收敛迭代次数
    CNN88.5780
    CAPS91.90100
    LW-CAPS98.6445
    IF-CAPS100.0020
    下载: 导出CSV

    表  4  东南大学齿轮箱样本集

    Table  4.   Gearbox sample set of Southeast University

    标签故障类型训练样本数测试样本数
    0正常状态1 000200
    1缺齿故障1 000200
    2齿根断裂故障1 000200
    3齿面磨损故障1 000200
    4裂纹故障1 000200
    下载: 导出CSV

    表  5  美国凯斯西储大学轴承样本集

    Table  5.   Case Western Reserve University bearing sample set

    标签故障类型故障直径训练样本数测试样本数
    0正常状态0480120
    1内圈故障0.007480120
    2内圈故障0.014480120
    3内圈故障0.021480120
    4外圈故障0.007480120
    5外圈故障0.014480120
    6外圈故障0.021480120
    7滚动体故障0.007480120
    8滚动体故障0.014480120
    9滚动体故障0.021480120
    下载: 导出CSV

    表  6  凯斯轴承数据集下不同模型精确度对比

    Table  6.   Accuracy comparison of different models under Case bearing data set

    方法精度/%
    文献[10]95.50
    文献[16]94.10
    文献[24]94.63
    IF-CAPS100.00
    下载: 导出CSV
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  • 收稿日期:  2022-03-04
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