基于MPE与PSO-SVM的滚动轴承故障诊断  

Fault diagnosis of rolling bearing based on MPE and PSO-SVM

作　　者：刁宁昆 马怀祥[1,2] 王金师 刘帅 Diao Ningkun;Ma Huaixiang;Wang Jinshi;Liu Shuai(Hebei Provincial Collaborative Innovation Center of Large Construction Machinery Manufacturing,Shijiazhuang Tiedao University,Shijiazhuang 050043,China;School of Mechanical Engineering,Shijiazhuang Tiedao University,Shijiazhuang 050043,China)

机构地区：[1]石家庄铁道大学河北省大型工程机械装备制造协同创新中心,石家庄050043 [2]石家庄铁道大学机械工程学院,石家庄050043

出　　处：《电子测量技术》2021年第21期44-48,共5页Electronic Measurement Technology

基　　金：国家自然科学基金(11872254);中铁十四局集团有限公司芜湖长江隧道建设指挥部工程科研项目(ZTSSJ-WHSD-GCKY-2021-002)资助。

摘　　要：滚动轴承是旋转机械的重要部件之一,针对滚动轴承故障诊断问题,提出了一种多尺度排列熵(MPE)与粒子群优化(PSO)的支持向量机(SVM)相结合的算法。利用MPE方法得到轴承故障信号的故障特征,并将其作为特征向量输入PSO-SVM模型中,使用凯斯西储大学轴承故障数据进行验证,发现该方法可以有效进行滚动轴承的故障识别。同时将该方法与多尺度排列熵结合传统的SVM方法以及使用网格搜索优化的SVM方法所得故障分类结果进行比较,发现该方法在滚动轴承故障诊断的时效性以及准确率方面具有一定的优越性。Rolling bearing is one of the important parts of rotating machine.Aiming at the problem of rolling bearing fault diagnosis,this paper proposes an algorithm combining multiscale permutation entropy(MPE)and support vector machine(SVM)optimized by particle swarm optimization(PSO).The fault characteristics of the bearing fault data was obtained by the MPE method,fitting as a feature vector into the PSO-SVM model,using Case Western Reserve University bearing dataset for verification.It is found that this method can effectively identify the fault of the rolling bearing.This method is compared with the fault classification results obtained by combining the multi-scale permutation entropy with the traditional SVM method and the SVM method optimized by grid search.It is found that the method proposed has certain advantages in the efficiency and accuracy of rolling bearing fault diagnosis.

关 键 词：多尺度排列熵 粒子群优化 支持向量机 滚动轴承 

分 类 号：TH133.331[机械工程—机械制造及自动化] TP181[自动化与计算机技术—控制理论与控制工程]