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A platform for research: civil engineering, architecture and urbanism
Aiming at the no stationary characteristic of a gear fault vibration signal,a method based on Ensemble local mean decomposition and Kernel density estimation is proposed in this paper. First,the vibration signal is decomposed to be a series PF component by ELMD,calculating RMS、kurtosis、skewness coefficient of PF components,which contains main fault information,then they are combined into a feature vector,the Classification based on kernel density estimation is proposed,multiple sets of vibration signal feature vectors are used to train and test,identify their fault condition. The results showed that this method can effectively identify the fault of rolling bearing,and it is better than the LMD method
Aiming at the no stationary characteristic of a gear fault vibration signal,a method based on Ensemble local mean decomposition and Kernel density estimation is proposed in this paper. First,the vibration signal is decomposed to be a series PF component by ELMD,calculating RMS、kurtosis、skewness coefficient of PF components,which contains main fault information,then they are combined into a feature vector,the Classification based on kernel density estimation is proposed,multiple sets of vibration signal feature vectors are used to train and test,identify their fault condition. The results showed that this method can effectively identify the fault of rolling bearing,and it is better than the LMD method
FAULT DIAGNOSIS METHOD OF ROLLING BEARINGS BASED ON ELMD AND KERNEL DENSITY ESTIMATION
2017
Article (Journal)
Electronic Resource
Unknown
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