{"title":"基于支持向量机的多分辨率电机电流特征分析","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":"{\"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}","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}
Motor current signature analysis by multi-resolution methods using Support Vector Machine
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.