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CEEMD与卷积神经网络特征提取的故障诊断方法研究

张朝林 范玉刚

张朝林, 范玉刚. CEEMD与卷积神经网络特征提取的故障诊断方法研究[J]. 机械科学与技术, 2019, 38(2): 178-183. doi: 10.13433/j.cnki.1003-8728.20180166
引用本文: 张朝林, 范玉刚. CEEMD与卷积神经网络特征提取的故障诊断方法研究[J]. 机械科学与技术, 2019, 38(2): 178-183. doi: 10.13433/j.cnki.1003-8728.20180166
Zhang Zhaolin, Fan Yugang. Fault Diagnosis of a Bearing using Feature Extraction Method based on CEEMD Algorithm and CNN[J]. Mechanical Science and Technology for Aerospace Engineering, 2019, 38(2): 178-183. doi: 10.13433/j.cnki.1003-8728.20180166
Citation: Zhang Zhaolin, Fan Yugang. Fault Diagnosis of a Bearing using Feature Extraction Method based on CEEMD Algorithm and CNN[J]. Mechanical Science and Technology for Aerospace Engineering, 2019, 38(2): 178-183. doi: 10.13433/j.cnki.1003-8728.20180166

CEEMD与卷积神经网络特征提取的故障诊断方法研究

doi: 10.13433/j.cnki.1003-8728.20180166
基金项目: 

国家自然科学基金项目 61741310

详细信息
    作者简介:

    张朝林(1991-), 硕士研究生, 研究方向为故障诊断, zzldpn@vip.qq.com

    通讯作者:

    范玉刚, 副教授, 硕士生导师, ygfan@qq.com

  • 中图分类号: TH11;TP181

Fault Diagnosis of a Bearing using Feature Extraction Method based on CEEMD Algorithm and CNN

  • 摘要: 轴承动力学行为具有非线性的特点,导致其振动信号特征与运行状态之间存在较强的非线性关系;且振动信号的特征提取与选择往往需要大量的先验知识,导致特征的设计难以准确反映不同的运行状态。针对以上问题,提出一种基于互补集合经验模态分解(Complementary ensemble empirical mode decomposition,CEEMD)与卷积神经网络(Convolutional neural networks,CNN)的特征提取方法,从振动信号时频图中自适应提取其敏感特征,反映设备运行状态。首先采用CEEMD算法分解得到振动信号的固有模态函数(Intrinsic mode function,IMF)分量,构造各个IMF时频图,并采用CNN提取时频图的特征;然后,将提取到的特征与小波包分频带能量值相结合,组建特征指标向量,用于构建轴承故障诊断模型。将该方法应用于不同负载、不同故障深度的轴承试验中,结果表明该方法能够在多种工况下有效地提高故障识别率。
  • 图  1  CNN一次训练过程

    图  2  CEEMD-CNN与ELM的滚动轴承诊断流程图

    图  3  CEEMD分解后的IMF分量与残差

    图  4  IMF分量三维时频图

    图  5  多特征ELM轴承故障类型识别结果

    图  6  小波包分频带能量值为特征的ELM轴承故障类型识别结果

    表  1  轴承样本数分布表

    故障尺寸/
    mils
    负载 正常 滚动体故障 内圈故障 外圈故障
    0 0 300
    1 300
    2 300
    3 300
    7 0 100 100 100
    1 100 100 100
    2 100 100 100
    3 100 100 100
    14 0 100 100 100
    1 100 100 100
    2 100 100 100
    3 100 100 100
    21 0 100 100 100
    1 100 100 100
    2 100 100 100
    3 100 100 100
    合计 1 200 1 200 1 200 1 200
    下载: 导出CSV
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出版历程
  • 收稿日期:  2018-04-23
  • 刊出日期:  2019-02-05

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