Study on Rolling Element Bearing Fault Diagnosis Methods Based on Ensemble Empirical Mode Decomposition

Zhong Liang Lv; Yi Lin Liu; Xian Wu Han; Min Liu

doi:10.4028/www.scientific.net/AMM.457-458.602

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 457-458Study on Rolling Element Bearing Fault Diagnosis...

Study on Rolling Element Bearing Fault Diagnosis Methods Based on Ensemble Empirical Mode Decomposition

Abstract:

For rotating machinery, a fault diagnosis method is proposed on the basis of the EEMD (Ensemle Emperical Mode Decomposition) and Correlation Coefficient Method. In the Vibration Signals and rotate speed of rotating machinery, the fault diagnosis method is achieved by Spectrum Analysis and real-time monitoring. The original signals are decomposed into several IMF components. Each IMF contains the local feature of the signal. Correlation coefficient method is used to select the appropriate Intrinsic Mode Function. Extract the fault feature through its envelope diagram. Experiment proves the feasibility of this method.

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Periodical:

Applied Mechanics and Materials (Volumes 457-458)

Pages:

602-607

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.457-458.602

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Online since:

October 2013

Authors:

Zhong Liang Lv, Yi Lin Liu, Xian Wu Han, Min Liu

Keywords:

EEMD, Extracting Characteristics, Fault Diagnosis

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References

[1] Huang N E, Shen Z, Long S R, et al. The empirical mode decomposition and the Hilbert spectrum for nonlinear and nonstationary time series analysis [J]. Proceeding of Royal Society London A, 454 (1971): 903－995 (1998).

DOI: 10.1098/rspa.1998.0193

Google Scholar

[2] Yang Yu, Yu Deijie, Cheng Junsheng. Roller Bearing Fault Diagnosis method Based on EMD and Neural Network. Vol. 24 No. 1: 85-88 (2005).

Google Scholar

[3] LIU Dongsheng. Study on Vibration Signal Filter Technic of Ball Screw Based on Empirical Mode Decomposition, Vol. 40 No. 7: 51-55 (2012).

Google Scholar

[4] Zhongliang LV , Peiwen AN, Baoping TANG，Li hui ZHANG. The Application of EEMD to Fault Diagnosis of Rolling Bearing. Advance Information and Computer Technology in Engineering and Manufacturing, Environmental Engineering. Vols. 765-767 : 2073-2077 (2013).

Google Scholar

[5] ZHU Ninghui , BAI Xiaomin, DONG Weijie. Harmonic Detection Method Based on EEMD, China Electric Power Research Institute, Vol. 33 No. 7: 92-98 (2013).

Google Scholar