[1]王洪明郝旺身韩捷.基于LMD和样本熵的齿轮故障特征提取方法研究[J].郑州大学学报(工学版),2015,36(03):44-48.[doi:10.3969/ j. issn. 1671 -6833.2015.03.010]
　WANG Hong-min,HAO Wang-shen,HAN Jie,et al.Research on Gear Fault Feature ExtractionBased on LMD and Sample Entropy[J].Journal of Zhengzhou University (Engineering Science),2015,36(03):44-48.[doi:10.3969/ j. issn. 1671 -6833.2015.03.010]
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基于LMD和样本熵的齿轮故障特征提取方法研究()
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《郑州大学学报(工学版)》[ISSN:1671-6833/CN:41-1339/T]

卷:
36
期数:
2015年03期
页码:
44-48
栏目:
出版日期:
2015-06-30

文章信息/Info

Title:
Research on Gear Fault Feature ExtractionBased on LMD and Sample Entropy
作者:
王洪明1郝旺身2韩捷3
郑州大学机械工程学院,河南郑州450001
Author(s):
WANG Hong-min; HAO Wang-shen; HAN Jie; DONG Xin-min; HAO Wei; OU YANG He-long
Institute of Vibration Engineering,Zhengzhou University,Zhengzhou 450001,China
关键词:
非线性; LMD; 样本嫡; 故障特征; 齿轮
Keywords:
non-linear; LMD; sample entropy; fault feature; gear
分类号:
TH133
DOI:
10.3969/ j. issn. 1671 -6833.2015.03.010
文献标志码:
A
摘要:
针对齿轮故障信号的非线性、非平稳特征,采用局部均值分解( LMD)结合样本嫡的方法提取故障特征.采用滑动平均法构造均值函数与包络函数,将原始信号分解得到一系列的PF分量,通过剔出无意义的PF分量,筛选出反映真实状态信息的分量,然后计算筛选出的PF分量的样本嫡.不同故障信号的PF分量的样本嫡的大小不一,规律可寻,据此可以将样本嫡的值作为元素构造故障特征向量.通过实验模拟齿轮正常、齿根裂纹,断齿和缺齿这4种状态,比较LMD-近似嫡与LMD-样本嫡的分类效果,实验模拟表明:LMD-样本嫡比LMD-近似嫡有更好的区分效果.
Abstract:
For the non-linear and the non-stationary characteristics of gear faults signal,this study adopts thelocal mean decomposition ( LMD) combined with the sample entropy method to extract fault features.With themoving average method to construct the mean function and the envelope function,the original signal is decom-posed into a series of components PF. Then by eliminating the meaningless components so that the componentsincluding real status information could be selected to calculate sample entropy.The sample entropy changedregularly with different fault signals’PF,and accordingly the sample entropy could be used as elements offault feature vector. Through experiments simulated under gear normal,tooth root cracked,tooth broken andmissing teeth conditions,then compared the classification results of LMD-approximate entropy with LMD-sam-ple entropy,and eventually it is proved that the LMD-sample entropy is better than the LMD-approximate en-tropy in distinguishing these four typical conditions.

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