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EWT-多尺度模糊熵-VPMCD融合算法的轴承故障识别分类应用

车守全 江伟 包从望 朱广勇

车守全,江伟,包从望, 等. EWT-多尺度模糊熵-VPMCD融合算法的轴承故障识别分类应用[J]. 机械科学与技术,2021,0(0):1-7 doi: 10.13433/j.cnki.1003-8728.20200230
引用本文: 车守全,江伟,包从望, 等. EWT-多尺度模糊熵-VPMCD融合算法的轴承故障识别分类应用[J]. 机械科学与技术,2021,0(0):1-7 doi: 10.13433/j.cnki.1003-8728.20200230
CHE Shouquan, JIANG Wei, BAO Congwang, ZHU Guangyong. Bearing Fault Recognition and Classification Method based on EWT and Multiscale Fuzzy Entropy and VPMCD[J]. Mechanical Science and Technology for Aerospace Engineering. doi: 10.13433/j.cnki.1003-8728.20200230
Citation: CHE Shouquan, JIANG Wei, BAO Congwang, ZHU Guangyong. Bearing Fault Recognition and Classification Method based on EWT and Multiscale Fuzzy Entropy and VPMCD[J]. Mechanical Science and Technology for Aerospace Engineering. doi: 10.13433/j.cnki.1003-8728.20200230

EWT-多尺度模糊熵-VPMCD融合算法的轴承故障识别分类应用

doi: 10.13433/j.cnki.1003-8728.20200230
基金项目: 贵州省矿山装备数字化技术工程研究中心项目(黔教合KY字[2017]026号)与六盘水市科研创新平台和人才团队建设项目(52020-2019-5-12)
详细信息
    作者简介:

    车守全(1992−),讲师,研究方向为装备故障检测及机器学习,E-mail:chesq_njtu@163.com

  • 中图分类号: TP39, TP23

Bearing Fault Recognition and Classification Method based on EWT and Multiscale Fuzzy Entropy and VPMCD

  • 摘要: 针对振动信号提取轴承故障特征及识别分类的研究方式,提出了一种结合EWT-多尺度模糊熵-VPMCD的方法。首先,运用经验小波变换提取振动信号的模态分量。其次,引入信息论中的模糊熵算法,并加以多尺度粗粒度划分得到多尺度模糊熵特征描述。然后,用VPMCD对特征向量进行自适应选择预测模型训练。最终通过实验表明:模态分量多尺度模糊熵能够有效描述故障特征;VPMCD在少训练样本情况下获得了最低90%的分类准确率,相较一些常用的分类方法有着更好的性能表现。
  • 图  1  经验小波变换

    图  2  多尺度粗粒度过程

    图  3  信号序列

    图  4  模态频率划分

    图  5  算法流程

    图  6  模型预测结果

    图  7  不同训练样本下不同算法分类准确率

    表  1  模型表达式

    模型预测特征值${f_i}$表达式
    QI${f_i} = {b_0} + \displaystyle \sum\limits_{j = 1}^{p - 1} {b_j^{(1)}{f_n}} + \displaystyle \sum\limits_{t = 1}^{p - 1} {b_j^{(2)}f_{{n_t}}^2 + }$${\displaystyle \sum\limits_{l = 1}^{p - 2} {\displaystyle \sum\limits_{t = l + 1}^{p - 1} {{b_{tl}}{f_{{n_t}}}} } } {f_{{n_l}}},(n,{n_t},{n_l} \ne i,n,{n_t},{n_l} \in [1,p]) $
    Q${f_i} = {b_0} + \displaystyle \sum\limits_{j = 1}^{p - 1} {b_j^{(1)}{f_n}} + \displaystyle \sum\limits_{t = 1}^{p - 1} {b_j^{(2)}f_{{n_t}}^2} ,(n,{n_t} \ne i,n,{n_t} \in [1,p]) $
    LI${f_i} = {b_0} + \displaystyle \sum\limits_{j = 1}^{p - 1} {{b_j}{f_n}} + \displaystyle \sum\limits_{l = 1}^{p - 2} {\displaystyle \sum\limits_{t = l + 1}^{p - 1} {{b_{tl}}{f_{{n_t}}}{f_{{n_l}}}} ,} (n,{n_t},{n_l} \ne i,n,{n_t},{n_l} \in [1,p]) $
    L${f_i} = {b_0} + \displaystyle \sum\limits_{j = 1}^{p - 1} {{b_j}{f_n}} ,(n \ne i,n \in [1,p])$
    下载: 导出CSV

    表  2  不同故障的模态分量模糊熵示例

    故障
    类型
    IMF1
    模糊熵
    IMF2
    模糊熵
    IMF3
    模糊熵
    IMF4
    模糊熵
    IMF5
    模糊熵
    正常0.2490.2730.2480.2230.077
    滚子故障0.1260.3070.2120.1850.133
    内圈故障0.1710.2830.2400.2150.174
    外圈故障0.2570.2680.2950.1660.157
    下载: 导出CSV

    表  3  内圈故障VPM系数及模型类别

    系数(列)模态分量(行)
    IMF1IMF2IMF3IMF4IMF5
    $ {C}_{1} $ 0.649 0.200 1.413 −1.570 −0.383
    $ {C}_{2} $ 0.363 2.732 12.820 −8.682 −1.238
    $ {C}_{3} $ −0.460 −1.585 −8.048 0.576 −1.801
    $ {C}_{4} $ 1.721 −0.790 3.207 −6.847 −5.942
    $ {C}_{5} $ −0.701 1.237 −1.481 11.351 6.232
    $ {C}_{6} $ 0.612 −0.754 4.852 −4.391 0.651
    $ {C}_{7} $ 0.228 0.050 −6.364 2.894 0.107
    $ {C}_{8} $ 5.891 4.123 −11.020 0.899 2.815
    $ {C}_{9} $ −4.092 −0.056 1.278 1.683 −0.510
    $ {C}_{10} $ 0.227 3.931 −1.977 −18.970 −2.766
    $ {C}_{11} $ −3.874 −1.733 1.922 2.281 8.196
    $ {C}_{12} $ 5.367 1.090 25.518 31.895 3.525
    $ {C}_{13} $ 1.485 2.157 9.924 −6.138 −7.819
    $ {C}_{14} $ −0.923 −7.421 −6.590 −23.886 −6.006
    $ {C}_{15} $ 0.771 −0.567 6.054 −1.610 1.795
    模型类别 QI QI QI QI QI
    下载: 导出CSV

    表  4  输出结果示例

    特征向量(列)样本类型(行)
    正常
    样本
    滚子故障
    样本
    内圈故障
    样本
    外圈故障
    样本
    正常   0.0253 1.5615 1.2281 0.5184
    滚子故障 0.2736 0.0045 0.1846 0.4627
    内圈故障 1.0799 0.2244 0.0221 0.1114
    外圈故障 0.2029 0.0101 0.1063 0.0329
    输出结果 正常 滚子故障 内圈故障 外圈故障
    分类结果 正确 正确 正确 正确
    下载: 导出CSV
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  • 收稿日期:  2020-04-07
  • 网络出版日期:  2021-05-27

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