New statistical moments for the detection of defects in rolling element bearings

2005 ◽  
Vol 26 (11-12) ◽  
pp. 1268-1274 ◽  
Author(s):  
Xinwen Niu ◽  
Limin Zhu ◽  
Han Ding
Keyword(s):  
Statistical Moments ◽  
Rolling Element Bearings ◽  
Rolling Element ◽  
Detection Of Defects

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

2012 ◽  
Vol 134 (8) ◽  
Author(s):  
Ling Xiang ◽  
Aijun Hu
Keyword(s):  
Ensemble Empirical Mode Decomposition ◽  
Rolling Element Bearing ◽  
Rolling Element Bearings ◽  
Intrinsic Mode Functions ◽  
Feature Extraction Method ◽  
Outer Race ◽  
Mode Decomposition ◽  
Rolling Element ◽  
Envelope Spectrum ◽  
Detection Of Defects

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.

New Statistical Moments for Diagnostics of Rolling Element Bearings

1997 ◽  
Vol 119 (3) ◽  
pp. 425-432 ◽  
Author(s):  
F. Honarvar ◽  
H. R. Martin
Keyword(s):  
Statistical Moment ◽  
Moment Analysis ◽  
Statistical Moments ◽  
Rolling Element Bearings ◽  
Effective Technique ◽  
The Third ◽  
Statistical Moment Analysis ◽  
Rolling Element ◽  
Major Parameter ◽  
Central Statistical

Statistical moment analysis has proven to be a very effective technique for diagnosis of rolling element bearings. The fourth normalized central statistical moment, kurtosis, has been the major parameter in this method. In this paper it will be shown that the third normalized statistical moment can be as effective as kurtosis if the data is initially rectified. The advantage of this moment over the traditional kurtosis value is its lesser susceptibility to spurious vibrations, which is considered to be one of the shortcomings of higher statistical moments including kurtosis. The sensitivity of this moment to changes of load and speed is also less than kurtosis. The proposed method can also be applied to higher odd statistical moments.

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