摘要
针对相关向量机(RVM)在电机轴承故障识别中的性能受参数选择影响较大的问题,提出了基于反向认知果蝇优化算法(RCFOA)优化RVM的电机轴承故障诊断方法。为提高FOA算法的寻优能力,引入反向学习策略,对原始果蝇优化算法进行了改进。利用RCFOA进行RVM参数的优化,可以有效地提高RVM的分类性能。电机轴承不同类型、不同程度故障诊断的实例表明,RCFOA算法能够获得更优的参数,提高了RVM的故障诊断准确率,相比于其他一些方法更有优势,可有效应用于故障诊断。
Aiming at the fact that the fault diagnosis performance of relevance vector machine(RVM)in motor bearing highly depends on the parameters selection,a motor bearing fault diagnosis method based on RVM optimized by fruit fly optimization algorithm with reverse cognition(RCFOA)was proposed.In order to improve search ability of FOA,reverse cognition strategy was introduced and improved the original FOA algorithm.Use the RCFOA to optimize RVM parameters can effectively improve the classification performance of RVM.Different fault type and different fault degree of motor bearing fault diagnosis experiment results show that the RCFOA can obtain better parameter when compared with some other methods,improved the fault diagnosis accuracy of RVM and can applied to fault diagnosis efficiently.
作者
王汉章
WANG HanZhang(Department of Locomotive and Rolling Stock,Baotou Railway Vocation&Technical College,Baotou 014060,China)
出处
《机械强度》
CAS
CSCD
北大核心
2019年第4期814-820,共7页
Journal of Mechanical Strength
关键词
果蝇优化算法
反向认知
相关向量机
故障诊断
轴承
Fruit fly optimization algorithm
Reverse cognition
Relevance vector machine
Fault diagnosis
Bearing
