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Technical Briefs

New Feature Extraction Method for the Detection of Defects in Rolling Element Bearings

[+] Author and Article Information
Ling Xiang

Mechanical Engineering Department,  North China Electric Power University, Baoding 071003, Hebei Province, Chinancepuxl@163.com

Aijun Hu

Mechanical Engineering Department,  North China Electric Power University, Baoding 071003, Hebei Province, Chinabdlaohu@126.com

J. Eng. Gas Turbines Power 134(8), 084501 (Jun 29, 2012) (5 pages) doi:10.1115/1.4006713 History: Received February 01, 2012; Revised February 21, 2012; Published June 29, 2012; Online June 29, 2012

This paper proposes a new method based on ensemble empirical mode decomposition (EEMD) and kurtosis criterion for the detection of defects in rolling element bearings. Some intrinsic mode functions (IMFs) are presented to obtain symptom wave by EEMD. The different kurtosis of the intrinsic mode function is determined to select the envelope spectrum. The fault feature based on the IMF envelope spectrum whose kurtosis is the maximum is extracted, and fault patterns of roller bearings can be effectively differentiated. Practical examples of diagnosis for a rolling element bearing are provided to verify the effectiveness of the proposed method. The verification results show that the bearing faults that typically occur in rolling element bearings, such as outer-race and inner-race, can be effectively identified by the proposed method.

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Copyright © 2012 by American Society of Mechanical Engineers
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References

Figures

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Figure 1

The vibration waveform and spectrums for inner ring fault signal

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Figure 2

EEMD decomposition and envelope spectrum of IMF c2 for inner ring fault signal

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Figure 3

The vibration waveform and spectrums for outer ring fault signal

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Figure 4

EEMD decomposition and envelope spectrum of IMF c4 for outer ring fault signal

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