Research on fault feature extraction method of rolling bearing based on improved wavelet threshold and CEEMD

Because bearing fault feature information is not easy to extract in noisy background, this paper proposes an improved rolling bearing fault feature extraction method based on combination of wavelet threshold and complementary ensemble empirical mode decomposition (CEEMD). Firstly, the improved wavel...

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Bibliographic Details
Published inJournal of physics. Conference series Vol. 1449; no. 1; pp. 12003 - 12009
Main Authors Yang, Lixiang, Hu, Qinghe, Zhang, Shuang
Format Journal Article
LanguageEnglish
Published Bristol IOP Publishing 01.01.2020
Subjects
Background noise
Correlation coefficients
Empirical analysis
Feature extraction
Kurtosis
Noise reduction
Physics
Roller bearings
Signal reconstruction
Spectrum analysis
Vibration analysis
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Summary:Because bearing fault feature information is not easy to extract in noisy background, this paper proposes an improved rolling bearing fault feature extraction method based on combination of wavelet threshold and complementary ensemble empirical mode decomposition (CEEMD). Firstly, the improved wavelet threshold denoising method is used to reduce the noise of the vibration signal, and the impact characteristics in the signal are enhanced. Then, the CEEMD decomposition is used to obtain a set of Intrinsic Modal Functions (IMFs), and IMFs with larger kurtosis and correlation coefficients are selected for signal reconstruction. At last, the envelope spectrum analysis of reconstructed signal is carried out to extract fault characteristic information. Through the experimental analysis of the vibration signals of the outer ring and inner ring of the rolling bearing, it is proved that this method can effectively extract the fault characteristics of the bearing.
ISSN:1742-6588
1742-6596
DOI:10.1088/1742-6596/1449/1/012003