基于EEMD的多尺度模糊熵的齿轮故障诊断

杨望灿,张培林,王怀光,陈彦龙,孙也尊

振动与冲击 ›› 2015, Vol. 34 ›› Issue (14) : 163-167.

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振动与冲击 ›› 2015, Vol. 34 ›› Issue (14) : 163-167.
论文

基于EEMD的多尺度模糊熵的齿轮故障诊断

  • 杨望灿,张培林,王怀光,陈彦龙,孙也尊
作者信息 +

Gear fault diagnosis based on multiscale fuzzy entropy of EEMD

  • YANG Wang-can,ZHANG Pei-lin,WANG Huai-guang,CHEN Yan-long,SUN Ye-zun
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文章历史 +

摘要

为准确利用振动信号进行故障诊断,提出基于EEMD多尺度模糊熵的齿轮故障诊断方法。利用集合经验模态分解(EEMD)对振动信号进行自适应分解,获得原始信号的不同尺度分量;据模糊熵能有效区分不同信号的复杂度,计算EEMD分解所得本征模态函数(IMF)分量模糊熵,获得原始信号多个尺度的复杂测度作为齿轮不同状态的特征参数;将该特征参数输入最小二乘支持向量机(LS-SVM)分类器判断齿轮故障。齿轮箱齿轮故障实验结果表明,该方法能提高齿轮故障诊断精度。

Abstract

In order to diagnose fault accurately by using vibration signal, a method of gear fault diagnosis based on multiscale fuzzy entropy of EEMD was proposed. Firstly, the vibration signal was decomposed adaptively with ensemble empirical mode decomposition (EEMD) and then the components in different scales of original signal were gained. Because the fuzzy entropy could distinguish the complexity of different signals effectively, the fuzzy entropy of intrinsic mode functions (IMFs) by EEMD was calculated. Thus the complexity metric in different scales of original signal was gained, which could be the feature parameter that described the different states of gear. At last the feature parameters were put into least square support vector machine (LS-SVM) for diagnosing the gear fault. Results of the gear box fault test indicated that the proposed method diagnosed gear fault with high accuracy.

关键词

多尺度模糊熵 / EEMD / 特征参数 / 齿轮 / 故障诊断

Key words

multiscale fuzzy entropy / EEMD / feature parameter / gear / fault diagnosis

引用本文

导出引用
杨望灿,张培林,王怀光,陈彦龙,孙也尊. 基于EEMD的多尺度模糊熵的齿轮故障诊断[J]. 振动与冲击, 2015, 34(14): 163-167
YANG Wang-can,ZHANG Pei-lin,WANG Huai-guang,CHEN Yan-long,SUN Ye-zun. Gear fault diagnosis based on multiscale fuzzy entropy of EEMD[J]. Journal of Vibration and Shock, 2015, 34(14): 163-167

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