Research on Gear Fault Feature ExtractionBased on LMD and Sample Entropy
[1]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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Journal of Zhengzhou University (Engineering Science)[ISSN
1671-6833/CN
41-1339/T] Volume:
36
Number of periods:
2015 03
Page number:
44-48
Column:
Public date:
2015-06-30
- Title:
- Research on Gear Fault Feature ExtractionBased on LMD and Sample Entropy
- Keywords:
- non-linear; LMD; sample entropy; fault feature; gear
- CLC:
- TH133
- 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.
- References:
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