An adaptively fast ensemble empirical mode decomposition method and its applications to rolling element bearing fault diagnosis

2015 ◽  
Vol 62-63 ◽  
pp. 444-459 ◽  
Author(s):  
Xiaoming Xue ◽  
Jianzhong Zhou ◽  
Yanhe Xu ◽  
Wenlong Zhu ◽  
Chaoshun Li
Keyword(s):  
Fault Diagnosis ◽  
Empirical Mode Decomposition ◽  
Decomposition Method ◽  
Ensemble Empirical Mode Decomposition ◽  
Rolling Element Bearing ◽  
Bearing Fault Diagnosis ◽  
Mode Decomposition ◽  
Rolling Element ◽  
Empirical Mode Decomposition Method ◽  
Element Bearing

Study on Rolling Element Bearing Fault Diagnosis Methods Based on Ensemble Empirical Mode Decomposition

2013 ◽  
Vol 457-458 ◽  
pp. 602-607
Author(s):  
Zhong Liang Lv ◽  
Yi Lin Liu ◽  
Xian Wu Han ◽  
Min Liu
Keyword(s):  
Fault Diagnosis ◽  
Correlation Coefficient ◽  
Ensemble Empirical Mode Decomposition ◽  
Rotating Machinery ◽  
Rolling Element Bearing ◽  
Bearing Fault Diagnosis ◽  
Mode Decomposition ◽  
Rolling Element ◽  
Diagnosis Method ◽  
Coefficient Method

For rotating machinery, a fault diagnosis method is proposed on the basis of the EEMD (Ensemle Emperical Mode Decomposition) and Correlation Coefficient Method. In the Vibration Signals and rotate speed of rotating machinery, the fault diagnosis method is achieved by Spectrum Analysis and real-time monitoring. The original signals are decomposed into several IMF components. Each IMF contains the local feature of the signal. Correlation coefficient method is used to select the appropriate Intrinsic Mode Function. Extract the fault feature through its envelope diagram. Experiment proves the feasibility of this method.

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