Rolling Bearing Fault Diagnosis Using Sample Entropy and 1.5 Dimension Spectrum Based on EMD

Xian You Zhong; Chun Hua Zhao; Hai Jiang Dong; Xian Ming Liu; Liang Cai Zeng

doi:10.4028/www.scientific.net/AMM.278-280.1027

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 278-280Rolling Bearing Fault Diagnosis Using Sample...

Rolling Bearing Fault Diagnosis Using Sample Entropy and 1.5 Dimension Spectrum Based on EMD

Abstract:

An approach of fault diagnosis of bearing based on empirical mode decomposition (EMD), sample entropy and 1.5 dimension spectrum was presented. Firstly, the original vibration signal was decomposed into a number of intrinsic mode functions (IMFs) using EMD. Second, the sample entropies of IMFs were calculated to select the sensitive IMF. Finally, the IMF containing fault infor- mation was analyzed with 1.5 dimension spectrum, The experimental results show the method can be used to effectively diagnose faults of rolling bearing.

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Periodical:

Applied Mechanics and Materials (Volumes 278-280)

Pages:

1027-1031

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.278-280.1027

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Online since:

January 2013

Authors:

Xian You Zhong, Chun Hua Zhao, Hai Jiang Dong, Xian Ming Liu, Liang Cai Zeng

Keywords:

1.5 Dimension Spectrum, Empirical Mode Decomposition (EMD), Rolling Bearing, Sample Entropy

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References

[1] C. James Li and Jun Ma, Wavelet decomposition of vibration for detection of bearing localized defects, NDT&E International, 1997, 30 (3): 143-149.

DOI: 10.1016/s0963-8695(96)00052-7

Google Scholar

[2] Li Hui, Zhang Yuping, Zheng Haiqi. Hilbert-Huang transform and marginal spectrum for detection and diagnosis of localized defects in roller bearings [J]. Journal of Mechanical Science and Technology, 2009, 23(2): 291-30.

DOI: 10.1007/s12206-008-1110-5

Google Scholar

[3] N.E. Huang et al., 'The empirical mode decomposi-tion and the Hilbert spectrum for nonlinear and non-stationary time series analysis, Proc. Royal Soc. Lon-don A, vol. 454, pp.903-995, (1998).

Google Scholar

[4] S. M. Pincus and W. M. Huang, Approximate entropy: Statistical properties and applications, Commun. Statist. Theory Meth., vol. 21, p.3061 –3077, (1992).

Google Scholar

[5] Yan, R., & Gao, R. X. Approximate entropy as a diagnostic tool for machine health monitoring. Mechanical Systems and Signal Processing, 2007, 21(2), 824–839.

DOI: 10.1016/j.ymssp.2006.02.009

Google Scholar

[6] J. S. Richman and J. R. Moorman, Physiological time-series analysis using approximate entropy and sample entropy, Am. J. Physiol., vol. 278, pp. H2039–H2049, (2000).

DOI: 10.1152/ajpheart.2000.278.6.h2039

Google Scholar

[7] The case western reserve university bearing data center website. Bearing data center seeded fault testdata[EB/OL]. [2010-11-05]. http: /csegroups. case. edu/bearingdatacenter/pages/download-data-file.

Google Scholar