{"title":"基于人工神经网络理论LM算法的风力发电机组故障预测诊断","authors":"Lincang Ju, Dekuan Song, Beibei Shi, Qiang Zhao","doi":"10.1109/ICNC.2011.6021921","DOIUrl":null,"url":null,"abstract":"This paper analyses the main fault factors on wind turbine, and presents three general faults: gear box fault, leeway system fault and generator fault. After the analysis and research of the basic principle of Back-Propagation Neural Network based on LM arithmetic, a three-layer Back-Propagation Network faults predictive diagnosis model is built. Data from two wind turbines are used to test the effectiveness of this method.","PeriodicalId":299503,"journal":{"name":"2011 Seventh International Conference on Natural Computation","volume":"153 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2011-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"9","resultStr":"{\"title\":\"Fault predictive diagnosis of wind turbine based on LM arithmetic of Artificial Neural Network theory\",\"authors\":\"Lincang Ju, Dekuan Song, Beibei Shi, Qiang Zhao\",\"doi\":\"10.1109/ICNC.2011.6021921\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper analyses the main fault factors on wind turbine, and presents three general faults: gear box fault, leeway system fault and generator fault. After the analysis and research of the basic principle of Back-Propagation Neural Network based on LM arithmetic, a three-layer Back-Propagation Network faults predictive diagnosis model is built. Data from two wind turbines are used to test the effectiveness of this method.\",\"PeriodicalId\":299503,\"journal\":{\"name\":\"2011 Seventh International Conference on Natural Computation\",\"volume\":\"153 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2011-07-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"9\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2011 Seventh International Conference on Natural Computation\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICNC.2011.6021921\",\"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 Seventh International Conference on Natural Computation","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICNC.2011.6021921","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Fault predictive diagnosis of wind turbine based on LM arithmetic of Artificial Neural Network theory
This paper analyses the main fault factors on wind turbine, and presents three general faults: gear box fault, leeway system fault and generator fault. After the analysis and research of the basic principle of Back-Propagation Neural Network based on LM arithmetic, a three-layer Back-Propagation Network faults predictive diagnosis model is built. Data from two wind turbines are used to test the effectiveness of this method.
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