A study on rolling bearing fault diagnosis method based on hierarchical fuzzy entropy and ISVM-BT

A new rolling bearing fault feature extractor called hierarchical fuzzy entropy (HFE) is proposed in this paper, which is composedcomprises the of hierarchical procedure and the fuzzy entropy calculation. Compared with multi-scale fuzzy entropy (MFE) method, HFE method considers both the low and high frequency components of the vibration signals, which can provide a much more accurate estimation of entropy. Besides, improved support vector machine based binary tree SVM (ISVM-BT) has the priority of high recognition accuracy compared with other classifiers. HenceTherefore, in this paper we proposed a novel rolling bearing fault diagnosis method based on HFE and ISVM-BT is proposed in this paper. Firstly, HFE is utilized to extract fault features and then the fault features are fed into the ISVM-BT to automatically fulfill the fault patterns identifications. The experimental results show the proposed method is effective in recognizing the different categories and severities of rolling bearings.