The Fault Diagnosis of Rolling Bearing Based on Ensemble Empirical Mode Decomposition and Random Forest

Accurate diagnosis of rolling bearing fault on the normal operation of machinery and equipment has a very important significance. A method combining Ensemble Empirical Mode Decomposition (EEMD) and Random Forest (RF) is proposed. Firstly, the original signal is decomposed into several intrinsic mode...

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Bibliographic Details
Published inShock and vibration Vol. 2017; no. 2017; pp. 1 - 9
Main Authors Lv, Siqi, Dong, Xiaogang, Li, Qiaoling, Qin, Xiwen
Format Journal Article
LanguageEnglish
Published Cairo, Egypt Hindawi Publishing Corporation 01.01.2017
Hindawi
John Wiley & Sons, Inc
Hindawi Limited
Wiley
Subjects
Bearings
Classification
Comparative analysis
Decision making
Decomposition
Entropy
Fault diagnosis
Fault location (Engineering)
Forests
Fourier transforms
Machinery and equipment
Mechanical properties
Methods
Observations
Roller bearings
Signal processing
Wavelet analysis
China
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Summary:Accurate diagnosis of rolling bearing fault on the normal operation of machinery and equipment has a very important significance. A method combining Ensemble Empirical Mode Decomposition (EEMD) and Random Forest (RF) is proposed. Firstly, the original signal is decomposed into several intrinsic mode functions (IMFs) by EEMD, and the effective IMFs are selected. Then their energy entropy is calculated as the feature. Finally, the classification is performed by RF. In addition, the wavelet method is also used in the proposed process, the same as EEMD. The results of the comparison show that the EEMD method is more accurate than the wavelet method.
ISSN:1070-9622
1875-9203
DOI:10.1155/2017/2623081