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Damage identification of rolling bearing based on improved hierarchical global fuzzy entropy and MCFS

Published:2023-09-20 author:BAI Shi-bing, LIN Jin-liang, YANG Yu-hua. Browse: 1052 Check PDF documents

Damage identification of rolling bearing based on improved

hierarchical global fuzzy entropy and MCFS

BAI Shi-bing1,2, LIN Jin-liang3, YANG Yu-hua4

(1.School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China;

2.IFLYTEK Big Data School, Chongqing City Vocational College, Chongqing 402160, China; 3.School of Information and

Manufacturing, Minxi Vocational & Technical College, Longyan 364000, China; 4.College of Software,

Liuzhou Institute of Technology, Liuzhou 545006, China)

Abstract: Aiming at the problem that the conventional multiscale feature extraction method could not capture the high frequency fault information of vibration signals, a rolling bearing fault diagnosis method based on improved hierarchical global fuzzy entropy (IHGFE) and multi cluster feature selection (MCFS) was proposed. First of all, an IHGFE nonlinear dynamics method that could capture the global features of the vibration signal from low frequency to high frequency was proposed and used for fault feature extraction of rolling bearing. Then, MCFS was used to reduce and optimize the initial feature vectors to build low dimensional and fault sensitive fault feature vectors. Finally, a multi fault classifier based on support vector machine was established to realize intelligent identification of rolling bearing damage, and two rolling bearing experiments were carried out for comparative analysis. The research results show that the IHGFE has better classification accuracy and recognition stability than the comparison method, which proves that IHGFE can solve the defect that the existing methods can not consider both the high frequency features and the global features of signals to a certain extent in feature extraction, and can provide a reference for further expanding the application of fuzzy entropy method in rolling bearing damage identification.

Key words: bearing fault diagnosis; improved hierarchical global fuzzy entropy(IHGFE); multi cluster feature selection(MCFS); support vector machine(SVM); feature dimension reduction; fault classifier

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