Y. Tian, Paul Lewin, A. E. Davies, S. Sutton, S. Swingler
{"title":"声发射技术和人工神经网络在局部放电分类中的应用","authors":"Y. Tian, Paul Lewin, A. E. Davies, S. Sutton, S. Swingler","doi":"10.1109/ELINSL.2002.995895","DOIUrl":null,"url":null,"abstract":"Partial discharge (PD) detection, signal analysis and pattern identification, using acoustic emission measurements and the back-propagation (BP) artificial neural network (ANN) have been investigated. The measured signals were processed using three-dimensional patterns and short duration Fourier transforms (SDFT). Investigation indicates that using BP ANN with the SDFT components for classifying different PD patterns provides very good overall results.","PeriodicalId":10532,"journal":{"name":"Conference Record of the the 2002 IEEE International Symposium on Electrical Insulation (Cat. No.02CH37316)","volume":"64 1","pages":"119-123"},"PeriodicalIF":0.0000,"publicationDate":"2002-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"19","resultStr":"{\"title\":\"Application of acoustic emission techniques and artificial neural networks to partial discharge classification\",\"authors\":\"Y. Tian, Paul Lewin, A. E. Davies, S. Sutton, S. Swingler\",\"doi\":\"10.1109/ELINSL.2002.995895\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Partial discharge (PD) detection, signal analysis and pattern identification, using acoustic emission measurements and the back-propagation (BP) artificial neural network (ANN) have been investigated. The measured signals were processed using three-dimensional patterns and short duration Fourier transforms (SDFT). Investigation indicates that using BP ANN with the SDFT components for classifying different PD patterns provides very good overall results.\",\"PeriodicalId\":10532,\"journal\":{\"name\":\"Conference Record of the the 2002 IEEE International Symposium on Electrical Insulation (Cat. No.02CH37316)\",\"volume\":\"64 1\",\"pages\":\"119-123\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2002-04-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"19\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Conference Record of the the 2002 IEEE International Symposium on Electrical Insulation (Cat. No.02CH37316)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ELINSL.2002.995895\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Conference Record of the the 2002 IEEE International Symposium on Electrical Insulation (Cat. No.02CH37316)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ELINSL.2002.995895","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Application of acoustic emission techniques and artificial neural networks to partial discharge classification
Partial discharge (PD) detection, signal analysis and pattern identification, using acoustic emission measurements and the back-propagation (BP) artificial neural network (ANN) have been investigated. The measured signals were processed using three-dimensional patterns and short duration Fourier transforms (SDFT). Investigation indicates that using BP ANN with the SDFT components for classifying different PD patterns provides very good overall results.
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