面向滚动轴承故障诊断的VBIVA方法
Variational Bayesian independent vector analysis method for rolling bearing fault diagnosis
于洋 1尹钰 2季策 3林峰 1于明月1
作者信息
- 1. 沈阳航空航天大学自动化学院,沈阳 110136
- 2. 沈阳航空航天大学电子信息工程学院,沈阳 110136
- 3. 东北大学 计算机科学与工程学院,沈阳 110169
- 折叠
摘要
在实际工程中,采集到的滚动轴承故障信号往往来自多个源.多个故障信号在传播路径中相互耦合形成了复合故障信号,使故障诊断问题变得更加复杂.如果直接对复合信号进行分析,那么提取到的故障特征中往往存在多源的故障频率,导致无法正确判断故障出现的位置.针对这一问题提出了变分贝叶斯独立向量分析(variational Bayesian independent vector analysis,VBIVA)算法,并将该算法应用于故障诊断.通过与独立向量分析(indepen-dent vector analysis,IVA)算法以及变分贝叶斯独立分量分析(variational Bayesian indepen-dent component analysis,VBICA)算法的仿真对比,证明VBIVA算法有效地解决了复合故障信号的盲源分离及故障诊断问题.
Abstract
In practical engineering occasions,the collected fault signals of rolling bearing are from multiple sources.The multiple fault signals are coupled to form the composite fault signals in the propa-gation path,which makes the fault diagnosis problem more complex.If the composite fault signal is di-rectly analyzed,the extracted fault feature contains multiple source fault frequencies,which may re-sult in a failure to determine the location of the fault.In order to resolve the problem,variational Bayesian independent vector analysis(VBIVA)algorithm was proposed and applied to fault diagnosis.Simulation results show that the proposed algorithm solved the problem of blind source separation and fault diagnosis in the comparison with IVA and VBICA.
关键词
滚动轴承/故障诊断/盲源分离/独立向量分析/变分贝叶斯Key words
rolling bearing/fault diagnosis/blind source separation/independent vector analysis/variational Bayes引用本文复制引用
基金项目
辽宁省自然科学基金(2022-MS-299)
航空科学基金(201933054002)
辽宁省教育厅项目(LJKMZ20220529)
出版年
2024
国家科技期刊平台
NSTL
万方数据