Rolling Bearing Fault Diagnosis Based on GCMWPE and Parameter Optimization SVM

被引：6

作者：

Ding J. ^{[1
]}

Wang Z. ^{[1
]}

Yao L. ^{[1
]}

Cai Y. ^{[1
]}

机构：

[1] School of Mechanical Engineering and Automation, Fuzhou University, Fuzhou

来源：

Zhongguo Jixie Gongcheng/China Mechanical Engineering | 2021年 / 32卷 / 02期

关键词：

Fault diagnosis; Generalized composite multiscale weighted permutation entropy(GCMWPE); Isometric mapping; Rolling bearing; Support vector machine(SVM);

D O I：

10.3969/j.issn.1004-132X.2021.02.004

中图分类号：

学科分类号：

摘要：

Aiming at the two key links of rolling bearing feature extraction and fault identification, a fault diagnosis was proposed based on GCMWPE and parameter optimization SVM. First, the GCMWPE was applied to comprehensively characterize rolling bearing fault feature information, and a high-dimensional fault feature set was constructed. Then, the S-Isomap(isometric mapping) was utilized for efficient secondary feature extraction. Finally, BAS(beetle antennae search)-SVM was employed to diagnose and identify fault types. The proposed method was applied to the experimental data analysis of rolling bearings, and the results show that the feature extraction effect of GCMWPE is superior than that of multiscale weighted permutation entropy, composite multiscale weighted permutation entropy, and generalized multiscale weighted permutation entropy; the feature extraction method combining GCMWPE and S-Isomap may effectively distinguish different fault types of rolling bearings in low-dimensional space; the recognition accuracy and recognition speed of BAS-SVM is better than that of particle swarm optimization SVM, simulated annealing SVM and artificial fish swarm algorithm support vector machine; the proposed method may effectively and accurately identify each fault types. © 2021, China Mechanical Engineering Magazine Office. All right reserved.

引用

页码：147 / 155

页数：8

共 16 条

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