机械科学与技术2024，Vol.43Issue(3)：402-408,7.DOI:10.13433/j.cnki.1003-8728.20220290
CEEMD-VMD与参数优化SVM结合的托辊轴承故障诊断
Roller Bearing Fault Diagnosis Combined CEEMD-VMD and Parameter Optimization SVM
摘要
Abstract
In order to solve the difficulty in extracting fault features of roller bearings under complex working environment,a noise reduction method was proposed based on the combination of complementary ensemble empirical mode decomposition(CEEMD)and variational modal decomposition(VMD).Firstly,the collected signals are decomposed by CEEMD,and the components are screened and reconstructed according to the correlation coefficient and kurtosis to generate new signals.Then,VMD was used to decompose the new signal,and the intrinsic mode functions(IMF)were optimized based on the composite index of the combination of envelope entropy and envelope spectrum kurtosis.Finally,the corresponding features were extracted and input into salp swarm optimized support vector machine(SSO-SVM)model to complete the fault diagnosis.The experimental results show that the diagnosis accuracy of normal bearing,bearing inner ring fault and bearing outer ring fault is up to 97.78%.Compared with the single noise reduction method,this method can effectively improve the signal noise ratio of fault signal,and the noise reduction effect is obvious.关键词
变分模态分解/托辊轴承/樽海鞘群算法/支持向量机/故障诊断Key words
variational modal decomposition(VMD)/rolling bearing/salp swarm algorithm(SSO)/support vector machines(SVM)/fault diagnosis分类
机械制造引用本文复制引用
贺志军,李军霞,刘少伟,秦志祥..CEEMD-VMD与参数优化SVM结合的托辊轴承故障诊断[J].机械科学与技术,2024,43(3):402-408,7.基金项目
国家自然基金面上项目(52174147)、中央引导地方科技发展资金项目(YDZJSX2021A023)及晋中市科技重点研发项目(Y211017) （52174147）
