Rolling element bearing fault diagnosis based on EEMD and improved morphological filtering method
SHEN Chang-qing1, ZHU Zhong-kui2, LIU Fang1, HHUANG Wei-guo2, KONG Fan-rang1
1Department of Precision Machinery and Precision Instrumentation, University of Science and Technology of China, Hefei 230027, China2School of Urban Rail Transportation, Soochow University, Suzhou 215123, China
Abstract：Localized defects in bearings tend to arouse periodical impulsive vibration, and the diagnosis of the bearing can be realized by detecting and extracting the impulsive components. However, under the practical environment, the fault related impacts are usually overwhelmed by the noise. Based on the analysis of Ensemble empirical mode decomposition (EEMD) and morphological filtering, a hybrid method which combines the EEMD method and an improved morphological filtering is proposed. A new structure element decision strategy is proposed to analysis the Intrinsic Mode Function (IMF) to extract the periodical impulsive signal feature extraction. The performance of the proposed method is validated by vibration signals of defective rolling bearing with outer and inner faults. The result shows that the proposed method is effective in extracting periodic impulses and suppressing the noises of vibration signals.
2013, Vol. 32 
