机械科学与技术2024，Vol.43Issue(6)：962-966,5.DOI:10.13433/j.cnki.1003-8728.20220312
CEEMD与AO-SVM结合的风机轴承故障诊断
Fault Diagnosis of Fan Bearing Combined CEEMD and AO-SVM
摘要
Abstract
Because of the bad operating environment of the fan,when the bearing is faulty,its vibration signal is often disturbed by environmental noise,which leads to the difficulty of fault information extraction for vibration signal.To solve this problem,this paper proposes a feature extraction method based on complementary ensemble empirical mode decomposition(CEEMD)and sample entropy(SE),which combines the Tianying optimization algorithm(AO)and support vector machine(SVM)for fault classification,and realizes the fault diagnosis of fan bearings.In this paper,the bearing data of Case Western Reserve University are used for the experiment,and the real fan bearing data are used for further verification.The experimental results show that the proposed method has high fault identification accuracy when fault vibration signal is disturbed by environmental noise.关键词
特征提取/互补集合经验模态分解/样本熵/天鹰优化算法/支持向量机Key words
feature extraction/complementary ensemble empirical mode decomposition/sample entropy/Tianying optimization algorithm/support vector machine分类
机械制造引用本文复制引用
孙润发,汤占军..CEEMD与AO-SVM结合的风机轴承故障诊断[J].机械科学与技术,2024,43(6):962-966,5.基金项目
国家自然科学基金项目(61962031) （61962031）
