Gear Fault Diagnosis Method Using EEMD Sample Entropy and Grey Incidence

Wen Bin Zhang; Ya Song Pu; Jia Xing Zhu; Yan Ping Su

doi:10.4028/www.scientific.net/AMR.694-697.1151

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 694-697Gear Fault Diagnosis Method Using EEMD Sample...

Gear Fault Diagnosis Method Using EEMD Sample Entropy and Grey Incidence

Abstract:

In this paper, a novel fault diagnosis method for gear was approached based on morphological filter, ensemble empirical mode decomposition (EEMD), sample entropy and grey incidence. Firstly, in order to eliminate the influence of noises, the line structure element was selected for morphological filter to denoise the original signal. Secondly, denoised vibration signals were decomposed into a finite number of stationary intrinsic mode functions (IMF) and some containing the most dominant fault information were calculated the sample entropy. Finally, these sample entropies could serve as the feature vectors, the grey incidence of different gear vibration signals was calculated to identify the fault pattern and condition. Practical results show that this method can be used in gear fault diagnosis effectively.

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Periodical:

Advanced Materials Research (Volumes 694-697)

Pages:

1151-1154

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.694-697.1151

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Online since:

May 2013

Authors:

Wen Bin Zhang, Ya Song Pu, Jia Xing Zhu, Yan Ping Su

Keywords:

Ensemble Empirical Mode Decomposition, Fault Diagnosis, Gear, Grey Incidence, Morphological Filtering, Sample Entropy

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References

[1] W. B. Zhang, X. J. Zhou and F. H. Mu. Application of morphological filter in purification of rotor center's orbit, edited by Proceedings of the 2008 1st International Congress on Image and Signal Processing, pp.136-140. (2008)

DOI: 10.1109/cisp.2008.204

Google Scholar

[2] W. B. Zhang, X. J. Zhou and Y. Lin. Application of morphological filter in pulse noise removing of vibration signal, edited by Proceedings of the 2008 1st International Congress on Image and Signal Processing, pp.132-135. (2008)

DOI: 10.1109/cisp.2008.202

Google Scholar

[3] Z. H. Wu and N. E. Huang. Ensemble empirical mode decomposition: a noise-assisted data analysis method, edited by Advances in Adaptive Data Analysis, Vol.1(2009), pp.1-41.

DOI: 10.1142/s1793536909000047

Google Scholar

[4] R. Alcaraz and J. J. Rieta. Review on sample entropy applications for the non-invasive analysis of atria fibrillation electrocardiograms, edited by Biomedical Signal Processing and Control, Vol. 5(2010), pp.1-14.

DOI: 10.1016/j.bspc.2009.11.001

Google Scholar

[5] L. Shen, X. J. Zhou, W. B. Zhang and Z. G. Zhang. Fault diagnosis of rolling element bearing based on morphological filter and grey incidence, edited by Journal of Vibration and Shock, Vol. 28(2009), pp.17-20.

Google Scholar