De-noising method of improved EEMD algorithm based on cloud similarity measurement

被引:0
|
作者
Han, Long [1 ,2 ]
Li, Chengwei [1 ]
机构
[1] School of Electrical Engineering and Automation, Harbin Institute of Technology, Harbin
[2] School of Electrical and Control Engineering, Heilongjiang University of Science and Technology, Harbin
来源
Open Automation and Control Systems Journal | 2015年 / 7卷 / 01期
基金
中国国家自然科学基金;
关键词
AE signal; Cloud similarity measurement; EEMD; Noise reduction;
D O I
10.2174/1874444301507011518
中图分类号
学科分类号
摘要
EEMD Algorithm is usually applied in noise reduction of rolling bearing signal because of its powerful ability in de-noising. But misjudgment in selecting sensitive IMF exists, it results in the incomplete processing of noise reduction. In order to solve this problem, this paper proposes an improved EEMD algorithm. This algorithm adopts Cloud Similarity Measurement in selecting the sensitive intrinsic mode function component which responses the fault feature. And the sensitive intrinsic mode function component is used to reconstruct signal. The simulation experiment shows that the improved EEMD algorithm has overcome the misjudgment of the original EEMD algorithm during selecting sensitive IMF, and it can do better in filtering the noise of signal. To apply the improved EEMD algorithm in de-noising of factually collected damage AE signal, the experiment results show that it is more effective in reducing the noise interference in Acoustic Emission Signal of rolling bearing. © Han and Li.
引用
收藏
页码:1518 / 1522
页数:4
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