Y. M. Safonov, N. N. Fedortsov, N. Dulnev, D. A. Blagodarov
{"title":"基于人工神经网络的线性电阻相关温度传感器实时诊断","authors":"Y. M. Safonov, N. N. Fedortsov, N. Dulnev, D. A. Blagodarov","doi":"10.1109/ICEPDS47235.2020.9249079","DOIUrl":null,"url":null,"abstract":"This paper proposes a method of diagnosing the state of temperature sensors with linear resistance dependence via an artificial neural network in real-time. The paper considers the relevance of this topic, describes the advantages of the methods, explains the algorithm of work. The necessity of using the ANN is justified, trained on data taken from a real object, and tested by the ANN. Theoretical calculations are confirmed by experimental data.","PeriodicalId":115427,"journal":{"name":"2020 XI International Conference on Electrical Power Drive Systems (ICEPDS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Real-time diagnostic for the temperature sensors with linear resistance dependence via an artificial neural network\",\"authors\":\"Y. M. Safonov, N. N. Fedortsov, N. Dulnev, D. A. Blagodarov\",\"doi\":\"10.1109/ICEPDS47235.2020.9249079\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper proposes a method of diagnosing the state of temperature sensors with linear resistance dependence via an artificial neural network in real-time. The paper considers the relevance of this topic, describes the advantages of the methods, explains the algorithm of work. The necessity of using the ANN is justified, trained on data taken from a real object, and tested by the ANN. Theoretical calculations are confirmed by experimental data.\",\"PeriodicalId\":115427,\"journal\":{\"name\":\"2020 XI International Conference on Electrical Power Drive Systems (ICEPDS)\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-10-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 XI International Conference on Electrical Power Drive Systems (ICEPDS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICEPDS47235.2020.9249079\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 XI International Conference on Electrical Power Drive Systems (ICEPDS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICEPDS47235.2020.9249079","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Real-time diagnostic for the temperature sensors with linear resistance dependence via an artificial neural network
This paper proposes a method of diagnosing the state of temperature sensors with linear resistance dependence via an artificial neural network in real-time. The paper considers the relevance of this topic, describes the advantages of the methods, explains the algorithm of work. The necessity of using the ANN is justified, trained on data taken from a real object, and tested by the ANN. Theoretical calculations are confirmed by experimental data.