计算机应用与软件2017，Vol.34Issue(5)：61-67,103,8.DOI:10.3969/j.issn.1000-386x.2017.05.011
基于谐波小波包和DAG-RVM的滚动轴承故障诊断
FAULT DIAGNOSIS OF ROLLING BEARING BASED ON HARMONIC WAVELET PACKET AND DAG-RVM
摘要
Abstract
In view of the traditional rolling bearing fault diagnosis methods is affected by human factors, and the cause of the fault is relatively complex.Based on the existing research, a fault diagnosis method based on wavelet packet analysis and acyclic graph relevance vector machine is proposed in this paper.The vibration signals of the rolling bearing under different fault conditions are decomposed and reconstructed by harmonic wavelet packet, and the frequency band energy is extracted as feature vector.The mapping from feature vector to fault mode is established by using acyclic graph relevance vector machine, finally the fault diagnosis of rolling bearing is solved.The results show that this method can quickly and accurately diagnose rolling bearing faults, and verify the effectiveness and stability of the method.In addition,compared with SVM,it shows the superiority of RVM in intelligent fauk diagnosis application.关键词
谐波小波包/有向无环图/相关向量机/故障诊断Key words
Harmonic wavelet packet/Directed acyclic graph/Relevance vector machine/Fault diagnosis分类
信息技术与安全科学引用本文复制引用
齐磊,王海瑞,李宇芳,李英,任玉卿..基于谐波小波包和DAG-RVM的滚动轴承故障诊断[J].计算机应用与软件,2017,34(5):61-67,103,8.基金项目
国家自然科学基金项目(61263023). （61263023）
