A Novel Rolling Bearing Fault Diagnosis Method Based on Adaptive Feature Selection and Clustering

Abstract

Rolling bearing is an important part of mechanical equipment. Timely detection of rolling bearing fault is one of the important factors to ensure the safe operation of equipment. In order to diagnose rolling bearing fault accurately, a novel rolling bearing fault diagnosis method based on adaptive feature selection and clustering is proposed. Firstly, the vibration signal obtained from rolling bearing is decomposed by ensemble empirical mode decomposition(EEMD) to extract as much important information as possible. Feature extraction is performed for each intrinsic mode function(IMF) component and the original signal, and finally 240 features are obtained. And the Chi-square Test algorithm, Variance-Relief-F algorithm, and hierarchical clustering algorithm are used to filter all the features in layers to obtain the optimal features. Then the optimal features are input into fuzzy c-means(FCM) clustering to complete fault diagnosis. After the fault diagnosis analysis of four groups of vibration signal data, it is found that whether the characteristic number parameters are set based on engineering experience or adaptive feature selection, good fault diagnosis results are obtained. Furthermore, through comparative experiments, the fault diagnosis effect of the method based on adaptive parameter setting is better. The results indicate that the proposed adaptive parameter fault diagnosis method is feasible and effective for rolling bearing fault diagnosis.