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SPA散布熵和GK聚类相结合的滚动轴承故障诊断

葛红平 刘晓波 熊小明

葛红平, 刘晓波, 熊小明. SPA散布熵和GK聚类相结合的滚动轴承故障诊断[J]. 机械科学与技术, 2021, 40(8): 1257-1263. doi: 10.13433/j.cnki.1003-8728.20200199
引用本文: 葛红平, 刘晓波, 熊小明. SPA散布熵和GK聚类相结合的滚动轴承故障诊断[J]. 机械科学与技术, 2021, 40(8): 1257-1263. doi: 10.13433/j.cnki.1003-8728.20200199
GE Hongping, LIU Xiaobo, XIONG Xiaoming. Fault Diagnosis Method of Rolling Bearing Combining with SPA Dispersion Entropy and GK Clustering[J]. Mechanical Science and Technology for Aerospace Engineering, 2021, 40(8): 1257-1263. doi: 10.13433/j.cnki.1003-8728.20200199
Citation: GE Hongping, LIU Xiaobo, XIONG Xiaoming. Fault Diagnosis Method of Rolling Bearing Combining with SPA Dispersion Entropy and GK Clustering[J]. Mechanical Science and Technology for Aerospace Engineering, 2021, 40(8): 1257-1263. doi: 10.13433/j.cnki.1003-8728.20200199

SPA散布熵和GK聚类相结合的滚动轴承故障诊断

doi: 10.13433/j.cnki.1003-8728.20200199
基金项目: 

国家自然科学基金项目 51365040

详细信息
    作者简介:

    葛红平(1993-), 助教, 硕士研究生, 研究方向为信号处理与故障诊断, 974229568@qq.com

    通讯作者:

    刘晓波, 教授, 博士, xbliu0791@126.com

  • 中图分类号: TH17

Fault Diagnosis Method of Rolling Bearing Combining with SPA Dispersion Entropy and GK Clustering

  • 摘要: 为充分利用振动信号的特征信息进行故障辨识, 提出一种平滑先验分析(SPA)散布熵和GK聚类相结合的滚动轴承故障诊断方法。首先对滚动轴承振动信号进行SPA分解得到趋势项和波动项; 然后分别计算趋势项和波动项的散布熵值构建特征向量; 最后将特征向量输入至GK分类器中进行聚类识别。将该方法应用到不同工况下的滚动轴承实验数据中, 分析结果表明, 与传统的基于经验模态分解(EMD)散布熵和GK聚类的故障诊断方法相比, 所提方法能够更加准确地实现轴承的故障判别。
  • 图  1  振动信号在不同嵌入维数下的DE

    图  2  振动信号在不同类别下的DE

    图  3  振动信号在不同数据长度下的DE

    图  4  振动信号在不同时间延迟下的DE

    图  5  轴承各种状态的振动信号时域波形图

    图  6  SPA分解结果

    图  7  不同故障类型的SPA-DE-GK聚类图

    图  8  不同故障类型的EMD-DE-GK聚类图

    图  9  不同损伤程度的SPA-DE-GK聚类图

    图  10  不同损伤程度的EMD-DE-GK聚类图

    表  1  不同λ下趋势项及波动项与原信号的相关系数

    λ 3 4 5 6 7
    趋势项 0.969 0.973 0.976 0.979 0.981
    波动项 0.375 0.333 0.309 0.294 0.283
    下载: 导出CSV

    表  2  不同状态信号的趋势项和波动项的散布熵值

    信号类型 趋势项 波动项
    NR 4.16 3.40
    IRF 4.85 3.60
    ORF 3.87 3.70
    BF 4.63 3.43
    下载: 导出CSV

    表  3  不同故障类型的聚类评价指标

    诊断方法 PC CE
    SPA-DE-GK 0.978 1 0.066 6
    EMD-DE-GK 0.873 4 0.246 9
    下载: 导出CSV

    表  4  不同损伤程度的聚类评价指标

    诊断方法 PC CE
    SPA-DE-GK 0.989 7 0.023 4
    EMD-DE-GK 0.853 3 0.282 4
    下载: 导出CSV
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出版历程
  • 收稿日期:  2020-03-12
  • 刊出日期:  2021-10-09

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