{"title":"SOM神经网络在汽轮机回热系统故障诊断中的应用","authors":"Jun-Fen Wu, Niansu Hu, Sheng Hu, Yu Zhao","doi":"10.1109/ICMLC.2002.1176735","DOIUrl":null,"url":null,"abstract":"The steam turbine regenerative system is one of the most important and complicated thermodynamic systems. The SOM (self-organizing map) neural network is applied to fault diagnosis of the system, which is implemented by the neural network toolbox in MATLAB. The method for fault diagnosis of the regenerative system is effective and it has been verified by simulation results.","PeriodicalId":90702,"journal":{"name":"Proceedings. International Conference on Machine Learning and Cybernetics","volume":"22 1","pages":"184-187 vol.1"},"PeriodicalIF":0.0000,"publicationDate":"2002-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"16","resultStr":"{\"title\":\"Application of SOM neural network in fault diagnosis of the steam turbine regenerative system\",\"authors\":\"Jun-Fen Wu, Niansu Hu, Sheng Hu, Yu Zhao\",\"doi\":\"10.1109/ICMLC.2002.1176735\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The steam turbine regenerative system is one of the most important and complicated thermodynamic systems. The SOM (self-organizing map) neural network is applied to fault diagnosis of the system, which is implemented by the neural network toolbox in MATLAB. The method for fault diagnosis of the regenerative system is effective and it has been verified by simulation results.\",\"PeriodicalId\":90702,\"journal\":{\"name\":\"Proceedings. International Conference on Machine Learning and Cybernetics\",\"volume\":\"22 1\",\"pages\":\"184-187 vol.1\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2002-11-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"16\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings. International Conference on Machine Learning and Cybernetics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICMLC.2002.1176735\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings. International Conference on Machine Learning and Cybernetics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICMLC.2002.1176735","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Application of SOM neural network in fault diagnosis of the steam turbine regenerative system
The steam turbine regenerative system is one of the most important and complicated thermodynamic systems. The SOM (self-organizing map) neural network is applied to fault diagnosis of the system, which is implemented by the neural network toolbox in MATLAB. The method for fault diagnosis of the regenerative system is effective and it has been verified by simulation results.
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