摘要
针对滚动轴承的故障诊断问题,提出了基于变分模态分解(Variational modal decomposition, VMD)、改进果蝇算法(Improved fruit fly optimize algorithm, LFOA)和相关向量机(Relevance vector machine, RVM)的滚动轴承故障诊断方法。该方法首先利用VMD将轴承振动信号分解成若干个本征模态分量(Intrinsic mode components, IMF),并计算IMF分量的均方根值和重心频率组成故障特征向量。为提高故障诊断精度,采用LFOA算法对RVM的参数进行优化,建立LFOA-RVM模型,然后对提取的故障特征进行训练和测试,以此来判断轴承的故障类型和故障程度。利用该方法对实测轴承信号进行了分析和诊断,并与其他几种方法进行了对比,验证了该方法的有效性。
Aiming at the fault diagnosis problem of rolling bearing,a fault diagnosis method of rolling bearing based on variational modal decomposition (VMD),improved fruit fly optimize algorithm (LFOA)and relevance vector machine (RVM) was proposed.Firstly,the bearing vibration signals was decomposed into several intrinsic mode components (IMF)and root mean square value and frequency of the center of gravity was calculated as fault feature vectors that could represent the operating conditions of bearings.In order to improve the classification accuracy,the LFOA was used to optimize the parameter of RVM and a LFOA-RVM model was built.And then,the fault feature were extracted for training and testing,so that it might recognize different fault type and different fault degree.The actual signals were analyzed and diagnosed,and compared with some other methods,it proves the validity of the method.
作者
陈婉
CHEN Wan(Information Network Center,ZhengZhou Tourism College,Zhengzhou 450009,China)
出处
《机械强度》
CAS
CSCD
北大核心
2018年第6期1297-1302,共6页
Journal of Mechanical Strength
基金
河南省基础与前沿技术研究计划项目(2016001)资助~~
关键词
变分模态分解
改进果蝇算法
相关向量机
故障诊断
轴承
Variational modal decomposition
Improved fruit fly optimization algorithm
Relevance vector machine
Fault diagnosis
Bearing
作者简介
通信作者:陈婉,女,1979年生,河南原阳人,汉族,郑州旅游职业学院讲师,硕士研究生,主要研究方向为计算机技术、算法分析与设计。E-mail:chenwan19790915@21cn.com,Tel:+86-371-68272076,Fax:+86-371-68272076
