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应用多参量和高斯过程分类的故障诊断方法

王斌 崔宝珍

王斌, 崔宝珍. 应用多参量和高斯过程分类的故障诊断方法[J]. 机械科学与技术, 2019, 38(9): 1380-1385. doi: 10.13433/j.cnki.1003-8728.20180320
引用本文: 王斌, 崔宝珍. 应用多参量和高斯过程分类的故障诊断方法[J]. 机械科学与技术, 2019, 38(9): 1380-1385. doi: 10.13433/j.cnki.1003-8728.20180320
Wang Bin, Cui Baozhen. Application of Multi-parameter and Gaussian Process Classification in Gearbox Fault Diagnosis[J]. Mechanical Science and Technology for Aerospace Engineering, 2019, 38(9): 1380-1385. doi: 10.13433/j.cnki.1003-8728.20180320
Citation: Wang Bin, Cui Baozhen. Application of Multi-parameter and Gaussian Process Classification in Gearbox Fault Diagnosis[J]. Mechanical Science and Technology for Aerospace Engineering, 2019, 38(9): 1380-1385. doi: 10.13433/j.cnki.1003-8728.20180320

应用多参量和高斯过程分类的故障诊断方法

doi: 10.13433/j.cnki.1003-8728.20180320
基金项目: 

山西省自然科学基金项目 2009011026-1

山西省研究生创新基金项目 2008072

详细信息
    作者简介:

    王斌(1994-), 硕士研究生, 研究方向为装备系统检测与诊断与控制, 605611881@qq.com

    通讯作者:

    崔宝珍, 副教授, 硕士生导师, 744212889@qq.com

  • 中图分类号: TH17

Application of Multi-parameter and Gaussian Process Classification in Gearbox Fault Diagnosis

  • 摘要: 由于齿轮箱振动信号的非平稳非线性等问题加大了故障诊断的难度,本文提出了一种基于互补集合经验模态分解(CEEMD)和多尺度排列熵(MPE)、样本熵(SE)相结合的故障特征提取方法。首先对齿轮箱振动信号进行互补集合经验模态分解,并根据相关系数原则对各模态分量进行筛选和重构,再利用多尺度排列熵对筛选出的模态分量进行特征提取,同时对重构后的信号提取其样本熵作为特征值;最后将提取出的多种故障特征融合输入到高斯过程分类器中进行实验验证,实验结果表明该方法提取齿轮箱振动信号的故障特征是有效的,高斯过程分类能快速准确地分辨出故障结果。
  • 图  1  故障诊断实验平台

    图  2  实验数据流程图

    图  3  齿轮箱断齿信号CEEMD分解结果

    图  4  高斯过程分类结果

    图  5  RBF分类结果

    表  1  各模态分量与原始信号相关系数

    模态分量IMF1IMF2IMF3IMF4IMF5
    相关系数0.763 80.574 50.258 50.056 10.026 6
    下载: 导出CSV
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出版历程
  • 收稿日期:  2018-09-03
  • 刊出日期:  2019-09-05

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