{"title":"基于偏最小二乘法的风力发电机组故障诊断方法研究","authors":"Feng Lv, Zeyu Zhang, Kun Zhai, Xiyuan Ju","doi":"10.1109/COMPCOMM.2016.7924839","DOIUrl":null,"url":null,"abstract":"In view of the complex structure of large wind turbine system and the characteristics of the operation process variable, this paper puts forward a fault diagnosis method based on multivariate statistics. The mathematical model based on partial least squares (PLS) without the need of complex model. Through the original data PLS algorithm built the relationship between input and output variables and get the monitoring model. Computer simulation results show this method can effectively reduce the dimension of data and realize the fault diagnosis. Compared with PCA, PLS are more fully use the sample space information, improve the accuracy of fault diagnosis effectively.","PeriodicalId":210833,"journal":{"name":"2016 2nd IEEE International Conference on Computer and Communications (ICCC)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Research on fault diagnosis method of wind turbine based on partial least square method\",\"authors\":\"Feng Lv, Zeyu Zhang, Kun Zhai, Xiyuan Ju\",\"doi\":\"10.1109/COMPCOMM.2016.7924839\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In view of the complex structure of large wind turbine system and the characteristics of the operation process variable, this paper puts forward a fault diagnosis method based on multivariate statistics. The mathematical model based on partial least squares (PLS) without the need of complex model. Through the original data PLS algorithm built the relationship between input and output variables and get the monitoring model. Computer simulation results show this method can effectively reduce the dimension of data and realize the fault diagnosis. Compared with PCA, PLS are more fully use the sample space information, improve the accuracy of fault diagnosis effectively.\",\"PeriodicalId\":210833,\"journal\":{\"name\":\"2016 2nd IEEE International Conference on Computer and Communications (ICCC)\",\"volume\":\"20 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2016 2nd IEEE International Conference on Computer and Communications (ICCC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/COMPCOMM.2016.7924839\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 2nd IEEE International Conference on Computer and Communications (ICCC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/COMPCOMM.2016.7924839","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Research on fault diagnosis method of wind turbine based on partial least square method
In view of the complex structure of large wind turbine system and the characteristics of the operation process variable, this paper puts forward a fault diagnosis method based on multivariate statistics. The mathematical model based on partial least squares (PLS) without the need of complex model. Through the original data PLS algorithm built the relationship between input and output variables and get the monitoring model. Computer simulation results show this method can effectively reduce the dimension of data and realize the fault diagnosis. Compared with PCA, PLS are more fully use the sample space information, improve the accuracy of fault diagnosis effectively.