The Application of EEMD to Fault Diagnosis of Rolling Bearing

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Abstract:

The EEDM(Ensemle Empirical Mode Decomposition) combined with correlation coefficient was proposed for identify the fault of rolling bearing. First, the fault of rolling bearing vibration signal will be decomposed into several IMF components, in view of the illusive component may appear in EEDM components, respectively calculate each IMF components and the correlation between the original signal, then carry out spectrum analysis to each IMF components and pick up fault feature. Through the experimental failure data analysis of rolling bearing inner ring, the EEMD method is good for fault identification of rolling bearing.

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Advanced Materials Research (Volumes 765-767)

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2065-2069

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September 2013

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© 2013 Trans Tech Publications Ltd. All Rights Reserved

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

DOI: 10.1098/rspa.1998.0193

Google Scholar

[2] Huang N E,Shen Z,Long S R. A new view of nonlinear water waves: The Hilbert spectrum. Annual Review of Fluid Mechanics, 1999; 31: 417—457.

DOI: 10.1146/annurev.fluid.31.1.417

Google Scholar

[3] Wu Z,Huang N E. Ensemble empirical mode decomposition: a noiseassisted data analysis method. Advances in Adaptive Data Analysis, 2009; 1( 1) : 1—41.

DOI: 10.1142/s1793536909000047

Google Scholar

[4] Flandrin P, Rilling G, Gonc-alves P. Empirical mode decomposition as a filter bank[J]. IEEE Signal Processing Letter, 2004, 11(2): 112-114.

DOI: 10.1109/lsp.2003.821662

Google Scholar

[5] Cheng Junsheng. Research on Fault Diagnosis Methods for Rotating Machinery Based on Hilbert-Huang Transform [D]. Changsha: College of Mechanical and Vehicle engineering, Hunan University, (2005).

Google Scholar

[6] Zheng Tianxiang, Yang Lihua. Discussion and improvement on empirical mode decomposition algorithm[J]. Acta Scifntiarum Naturalium Universitatis Sunyaatseni, 2007, 46(1): 1-6.

Google Scholar

[7] Zhao Jinping. Study on the effects of abnormal events to empirical mode de-composition method and the removal method for abnormal signal[J]. Journal of Ocean University of Qingdao, 2001, 31(6): 805-814.

Google Scholar

[8] Lei Y G, He Z J, Zi Y Y. Application of the EEMD method to rotor fault diagnosis of rotating machinery[J]. Mechanical Systems and Signal Processing, 2009, 23(4): 1327-1338.

DOI: 10.1016/j.ymssp.2008.11.005

Google Scholar