Rolling bearing diagnosis method based on improved standardized variable distance fusion hierarchical state space correlation entropy

Abstract

In recent years, the machine learning method has been successfully applied to the intelligent health monitoring of rolling bearings. However, its effectiveness depends heavily on the vibration signal time series collected. For the purpose of abundantly excavate the fault feature information in time series, some new multi-scale entropy methods are used to solve this problem. However, the multi-scale entropy only considers the low frequency component of the signal, neglects the high frequency component of the signal, loses some fault signals with modulation phenomenon, and reduces the accuracy of fault classification. Therefore, in order to overcome the shortcomings of multi-scale entropy. In this paper, proposing a new hierarchical state space correlation entropy (HSSCEn) to extract the fault features contained in the signal. At the same time, in practice, the fault features extracted by HSSCEn are usually high and nonlinear. Directly input to the classifier for training and testing will reduce the accuracy of machine learning method recognition. Therefore, an improved standardized variable distance classifier is proposed in this paper. It effectively reduces the feature redundancy of signal and improves the accuracy of original classifier. Finally, in order to verify the validity of the fault diagnosis method based on HSSCEn fusion standardized variable distance classifier, two sets of fault classification experiments are carried out in this paper. Compared with existing fault identification classifier algorithms, the proposed method has more advantages in accuracy and stability of fault classification.