计算机与数字工程2025，Vol.53Issue(2)：610-616,7.DOI:10.3969/j.issn.1672-9722.2025.02.053
基于改进的CEEMDAN和BO-SVM轴承故障诊断研究
Research on Bearing Fault Diagnosis Based on Improved CEEMDAN and BO-SVM
王磊 1黄巧亮 1张振涛 2汪煌 3马亦文4
作者信息
- 1. 江苏科技大学自动化学院 镇江 212003
- 2. 中铁天津轨道交通投资建设有限公司 天津 300392
- 3. 苏州鸿哲智能科技有限公司 苏州 215101
- 4. 南京工程学院 南京 211112
- 折叠
摘要
Abstract
In view of the difficulties in extracting fault features,low accuracy of fault identification and slow speed in the pro-cess of rolling bearing fault diagnosis,a rolling bearing fault diagnosis method based on the combination of improved CEEMDAN and Bayesian optimized support vector machine(BO-SVM)is proposed.Firstly,ICEEMDAN is used to decompose the original vi-bration signal to obtain a number of intrinsic mode functions(IMF).The correlation coefficient method is used to screen the useful IMF component reconstruction signal,and the multi-scale permutation entropy of the reconstructed signal is input as the feature vec-tor to the BO-SVM fault diagnosis model for training and testing.The research results show that this method can effectively extract feature information,and ICEEMDAN-BO-SVM fault diagnosis model can realize rapid and accurate diagnosis of rolling bearings.The diagnosis time is 21.26 s,and the accuracy rate reaches 99.38%.Compared with the SVM model optimized by grid search meth-od(GS)and genetic algorithm(GA),this method has certain advantages in diagnosis time and accuracy rate.关键词
改进的CEEMDAN/故障诊断/贝叶斯优化/支持向量机Key words
improved CEEMDAN/fault diagnosis/Bayesian optimization/support vector machine分类
机械制造引用本文复制引用
王磊,黄巧亮,张振涛,汪煌,马亦文..基于改进的CEEMDAN和BO-SVM轴承故障诊断研究[J].计算机与数字工程,2025,53(2):610-616,7.
