Motor current signature analysis by multi-resolution methods using Support Vector Machine

Y. Moorthy, P. S. Chandran, S. Rishidas
{"title":"Motor current signature analysis by multi-resolution methods using Support Vector Machine","authors":"Y. Moorthy, P. S. Chandran, S. Rishidas","doi":"10.1109/RAICS.2011.6069280","DOIUrl":null,"url":null,"abstract":"This paper presents a method for induction motor fault diagnosis based on rotor current signal analysis using Support Vector Machine. A dynamic model of induction motor developed using SIMULINK/MATLAB environment is used for simulation testing. A rotor fault is incorporated into the developed dynamic model which is mathematically complaint. The simulated model gives rotor currents, the multi-resolution analysis of which is conducted in the wavelet domain for the detection of broken bars. The analyzed data itself is indicative of the incipient faults, but mere human inspection can sometimes lead to unexpected faults. Hence, a classification scheme using Support Vector Machine is adopted. Finally, the results of Support Vector classification is compared against that of Artificial Neural Networks.","PeriodicalId":394515,"journal":{"name":"2011 IEEE Recent Advances in Intelligent Computational Systems","volume":"216 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2011-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"10","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2011 IEEE Recent Advances in Intelligent Computational Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/RAICS.2011.6069280","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 10

Abstract

This paper presents a method for induction motor fault diagnosis based on rotor current signal analysis using Support Vector Machine. A dynamic model of induction motor developed using SIMULINK/MATLAB environment is used for simulation testing. A rotor fault is incorporated into the developed dynamic model which is mathematically complaint. The simulated model gives rotor currents, the multi-resolution analysis of which is conducted in the wavelet domain for the detection of broken bars. The analyzed data itself is indicative of the incipient faults, but mere human inspection can sometimes lead to unexpected faults. Hence, a classification scheme using Support Vector Machine is adopted. Finally, the results of Support Vector classification is compared against that of Artificial Neural Networks.
基于支持向量机的多分辨率电机电流特征分析
提出了一种基于支持向量机转子电流信号分析的异步电动机故障诊断方法。利用SIMULINK/MATLAB环境建立了感应电机的动态模型,并进行了仿真测试。在建立的动力学模型中加入了转子故障,该模型在数学上是合理的。仿真模型给出了转子电流,并在小波域对其进行了多分辨率分析,用于断条检测。被分析的数据本身是早期故障的指示,但仅仅是人工检查有时会导致意想不到的故障。因此,采用支持向量机分类方案。最后,将支持向量的分类结果与人工神经网络的分类结果进行了比较。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信