Rolling Bearing Fault Diagnosis Method based on EEMD Denoising and Correlation Coefficient Identification - Pei Junfeng; Sun Jianhua; Song Chuanzhi; Song Yupeng; Ge Huizhong (Jixie chuandong)
In view of the nonlinear,non-stationary and massive noise features of reciprocating pump power of rolling bearing vibration signal,a fault diagnosis method of rolling bearing based on EEMD,distance factor,correlation coefficient and wavelet packet decomposition is proposed. By measuring the vibration signals of bearings in the bearing life test and decomposing the signals by using the method of EEMD,a number of IMF components is got. Then,the energy characteristic signal vectors is obtained through wavelet packet decomposing and constructing of reconstructed vibration signals,which got from the selecting and reconstructing of IMF components using the method of combination of distance factor and correlation coefficient. At last,the type of failure is determined based on the absolute value of the difference in the correlation coefficient of the energy characteristic signal vectors. Compared with the direct correlation coefficient analysis of the bearing vibration signal,this method has a high fault recognition rate,and doesn’t require a large amount of data training needed by neural networks,it is a better bearing fault identification method.