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LMD能量熵和SVM相结合的滚动轴承故障诊断

徐乐 邢邦圣 郎超男 高钦武

徐乐, 邢邦圣, 郎超男, 高钦武. LMD能量熵和SVM相结合的滚动轴承故障诊断[J]. 机械科学与技术, 2017, 36(6): 915-918. doi: 10.13433/j.cnki.1003-8728.2017.0615
引用本文: 徐乐, 邢邦圣, 郎超男, 高钦武. LMD能量熵和SVM相结合的滚动轴承故障诊断[J]. 机械科学与技术, 2017, 36(6): 915-918. doi: 10.13433/j.cnki.1003-8728.2017.0615
Xu Le, Xing Bangsheng, Lang Chaonan, Gao Qinwu. Fault Diagnosis of Rolling Bearing Combined LMD Energy Entropy and SVM[J]. Mechanical Science and Technology for Aerospace Engineering, 2017, 36(6): 915-918. doi: 10.13433/j.cnki.1003-8728.2017.0615
Citation: Xu Le, Xing Bangsheng, Lang Chaonan, Gao Qinwu. Fault Diagnosis of Rolling Bearing Combined LMD Energy Entropy and SVM[J]. Mechanical Science and Technology for Aerospace Engineering, 2017, 36(6): 915-918. doi: 10.13433/j.cnki.1003-8728.2017.0615

LMD能量熵和SVM相结合的滚动轴承故障诊断

doi: 10.13433/j.cnki.1003-8728.2017.0615
基金项目: 

江苏省"六大人才高峰"高层次人才项目(2012-ZBZZ-038)、江苏省普通高校研究生科研创新计划项目(SJLX_0656)、江苏师范大学博士科研支持项目(14XLR033)及江苏师范大学研究生科研创新计划重点项目(2015YZD018)资助

详细信息
    作者简介:

    徐乐(1990-),硕士,研究方向为旋转机械故障诊断、设备运行状态监测与控制,1183116563@qq.com

    通讯作者:

    邢邦圣(联系人),教授,博士,xbs138@jsnu.edu.cn

Fault Diagnosis of Rolling Bearing Combined LMD Energy Entropy and SVM

  • 摘要: 为实现小样本情况下对滚动轴承进行故障检测和分析,提出了基于局部均值分解(LMD)的能量熵和支持向量机(SVM)相结合的滚动轴承故障诊断方法。利用LMD信号处理方法将滚动轴承振动信号分解成有限个乘积函数(PF)分量,通过计算PF分量的能量熵进行故障特征提取,然后将提取的特征输入到SVM分类器中进行训练及测试,最终实现对滚动轴承的故障诊断。实验数据显示,在仅有少量样本条件下,LMD能量熵和SVM相结合的方法能够精确地对滚动轴承的故障类型进行识别和分类,这表明该方法对滚动轴承故障诊断的有效性。
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出版历程
  • 收稿日期:  2015-09-17
  • 刊出日期:  2017-06-05

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