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

页码：1518 / 1522

页数：4

共 14 条

[1]

Mba D., Rao B.K.N., Development of acoustic emission technology for condition monitoring and diagnosis of rotating machines: Bearings, pumps, gearboxes, engines, and rotating structures, The Shock and Vibration Digest, 38, pp. 3-16, (2006)

[2]

Tandon N., Yadava G.S., Ramakrishna K.M., A comparison of some condition monitoring techniques for the detection of defect in induction motor ball bearings, Mechanical Systems and Signal Processing, 21, pp. 244-256, (2007)

[3]

Alghamdi A.M., Mba D., A Comparative experimental study on the use of acoustic emission and vibration analysis for bearing defect identification and estimation of defect size, Mechanical Systems and Signal Processing, 20, pp. 1537-1571, (2012)

[4]

Jiang C.H., Wang L.S., You W., Research on acoustic emission signal de-noising based on translation invariant wavelet, Chinese Journal of Scientific Instrument, 27, pp. 2004-2010, (2006)

[5]

Li H., Zheng H., Tang L.W., “Application acoustic emission and empirical mode decomposition to faults diagnosis of bearing, Proceedings of the CSEE, 26, pp. 24-128, (2006)

[6]

Wu Z.H., Huang N.E., Ensemble empirical mode decomposition a noise assisted data analysis method, Advances in Adaptive Data Analysis, 1, pp. 1-41, (2009)

[7]

Lei Y.G., Machinery fault diagnosis based on improved hilberthuang transform, Journal of Mechanical Engineering, 47, pp. 71-77, (2011)

[8]

Zhou Z., Zhu Y.S., Zhang Y.Y., Adaptive fault diagnosis of rolling bearings based on EEMD and demodulated resonance, Journal of Vibration and Shock, 32, pp. 76-80, (2012)

[9]

Shen Y., Wang L., Zhao Z., Fault diagnosis for rolling bearing of wind turbine based on improved HHT, Measurement and Control Technology, 32, pp. 40-44, (2013)

[10]

Li D., Yi D., Artificial Intelligence with Uncertainty, pp. 143-185, (2005)

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