{"title":"基于多分辨率分析和支持向量机的电力电子故障诊断","authors":"Jianjun Zhao, Xiao-guang Gu, Heng Yu, W. Yan","doi":"10.1109/CINC.2010.5643877","DOIUrl":null,"url":null,"abstract":"The wavelet multi-resolution analysis (MRA) and support vector machine (SVM) are used in the fault diagnosis of power electronic. First, the paper use the wavelet MRA to deal with the characteristics of power electronic fault signal, and then identifies the fault diagnosis by the multi-class fault classifier based on SVM. The simulation results show the correctness and effectiveness of the method.","PeriodicalId":227004,"journal":{"name":"2010 Second International Conference on Computational Intelligence and Natural Computing","volume":"213 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Fault diagnosis of power electronic based on multi-resolution analysis and support vector machine\",\"authors\":\"Jianjun Zhao, Xiao-guang Gu, Heng Yu, W. Yan\",\"doi\":\"10.1109/CINC.2010.5643877\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The wavelet multi-resolution analysis (MRA) and support vector machine (SVM) are used in the fault diagnosis of power electronic. First, the paper use the wavelet MRA to deal with the characteristics of power electronic fault signal, and then identifies the fault diagnosis by the multi-class fault classifier based on SVM. The simulation results show the correctness and effectiveness of the method.\",\"PeriodicalId\":227004,\"journal\":{\"name\":\"2010 Second International Conference on Computational Intelligence and Natural Computing\",\"volume\":\"213 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2010-11-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2010 Second International Conference on Computational Intelligence and Natural Computing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CINC.2010.5643877\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2010 Second International Conference on Computational Intelligence and Natural Computing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CINC.2010.5643877","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Fault diagnosis of power electronic based on multi-resolution analysis and support vector machine
The wavelet multi-resolution analysis (MRA) and support vector machine (SVM) are used in the fault diagnosis of power electronic. First, the paper use the wavelet MRA to deal with the characteristics of power electronic fault signal, and then identifies the fault diagnosis by the multi-class fault classifier based on SVM. The simulation results show the correctness and effectiveness of the method.