首页 | 本学科首页   官方微博 | 高级检索  
     

一种基于概率盒-PSO-SVM的滚动轴承故障诊断方法
引用本文:杜奕,唐洪,丁家满,刘力强. 一种基于概率盒-PSO-SVM的滚动轴承故障诊断方法[J]. 上海理工大学学报, 2018, 40(1): 76-83
作者姓名:杜奕  唐洪  丁家满  刘力强
作者单位:昆明理工大学 城市学院, 昆明 650051,昆明理工大学 城市学院, 昆明 650051,昆明理工大学 信息工程与自动化学院, 昆明 650500,昆明理工大学 城市学院, 昆明 650051
基金项目:国家自然科学基金资助项目(51365020,51467007)
摘    要:针对滚动轴承故障诊断的问题,提出了一种基于概率盒理论和粒子群优化支持向量机的故障诊断新方法.在分析故障信号的概率统计特性基础上,利用概率盒直接建模方法获得概率盒,利用证据理论实现了概率盒的融合.不同故障状态下的概率盒特征也不同,采用不同的累积不确定性测量方法提取了概率盒的特征,并构建出用于模式识别的特征向量集,将特征集代入利用粒子群算法优化后的支持向量机中实现故障诊断.通过对滚动轴承振动信号的实验测试与对比分析表明:该方法可以实现对滚动轴承准确的诊断,与传统特征提取方法对比,证明了方法的有效性.

关 键 词:概率盒理论  特征向量  支持向量机  粒子群优化  故障诊断
收稿时间:2017-09-07

Application of P-box Theory and PSO-SVM in the Fault Diagnosis of Rolling Bearings
DU Yi,TANG Hong,DING Jiaman and LIU liqiang. Application of P-box Theory and PSO-SVM in the Fault Diagnosis of Rolling Bearings[J]. Journal of University of Shanghai For Science and Technology, 2018, 40(1): 76-83
Authors:DU Yi  TANG Hong  DING Jiaman  LIU liqiang
Affiliation:City College, Kunming University of Science and Technology, Kunming 650051, China,City College, Kunming University of Science and Technology, Kunming 650051, China,College of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China and City College, Kunming University of Science and Technology, Kunming 650051, China
Abstract:A method for rolling bearing fault diagnosis based on the probability box (p-box) and support vector machine (SVM) with particle swarm optimization (PSO) algorithm was proposed.P-boxes were obtained by using the direct p-box modeling method based on the probability and statistics analysis of fault signals'' characteristics,and the p-boxes fusion was realized by using the evidence theory.P-boxes features are different under different fault conditions,so,the features of p-boxes were extracted by different methods of p-box cumulative uncertainty measurement.A feature vector set for pattern recognition was constructed,which was then brought into the SVM whose key parameters were optimized by the PSO algorithm to realize the fault diagnosis.The experimental results indicate that the method can be used to accurately diagnose the rolling bearing faults.Comparing with the traditional feature extraction methods,the validity of the method was proved.
Keywords:probability box theory  feature vector  support vector machine (SVM)  particle swarm optimization (PSO)  fault diagnosis
本文献已被 CNKI 等数据库收录!
点击此处可从《上海理工大学学报》浏览原始摘要信息
点击此处可从《上海理工大学学报》下载全文
设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司  京ICP备09084417号