{"title":"基于模糊模型库的诊断系统","authors":"Yurii A. Korablev","doi":"10.1109/SCM.2017.7970585","DOIUrl":null,"url":null,"abstract":"In this paper the approach on the base bank of fuzzy diagnostic models for fault detections is proposed on example with coal mill used at a power plant. A model bank is constructed to reproduce behaviour of the normal and fault states. Fuzzy reasoning and basic statistical methods are combined to identify changes in normal model residuals. Fault models are fuzzy rules for describing abnormalities in selected time series.","PeriodicalId":315574,"journal":{"name":"2017 XX IEEE International Conference on Soft Computing and Measurements (SCM)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Diagnostic system based on bank of fuzzy models\",\"authors\":\"Yurii A. Korablev\",\"doi\":\"10.1109/SCM.2017.7970585\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper the approach on the base bank of fuzzy diagnostic models for fault detections is proposed on example with coal mill used at a power plant. A model bank is constructed to reproduce behaviour of the normal and fault states. Fuzzy reasoning and basic statistical methods are combined to identify changes in normal model residuals. Fault models are fuzzy rules for describing abnormalities in selected time series.\",\"PeriodicalId\":315574,\"journal\":{\"name\":\"2017 XX IEEE International Conference on Soft Computing and Measurements (SCM)\",\"volume\":\"21 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-05-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2017 XX IEEE International Conference on Soft Computing and Measurements (SCM)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SCM.2017.7970585\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 XX IEEE International Conference on Soft Computing and Measurements (SCM)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SCM.2017.7970585","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
In this paper the approach on the base bank of fuzzy diagnostic models for fault detections is proposed on example with coal mill used at a power plant. A model bank is constructed to reproduce behaviour of the normal and fault states. Fuzzy reasoning and basic statistical methods are combined to identify changes in normal model residuals. Fault models are fuzzy rules for describing abnormalities in selected time series.
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