A novel real-time fault diagnostic system by using strata hierarchical artificial neural network
Yan, Changfeng1,2; Zhang, Hao2; Wu, Lixiao1
2009
会议名称2009 Asia-Pacific Power and Energy Engineering Conference, APPEEC 2009
会议录名称Asia-Pacific Power and Energy Engineering Conference, APPEEC
页码1375-+
会议日期March 27, 2009 - March 31, 2009
会议地点Wuhan, China
出版地345 E 47TH ST, NEW YORK, NY 10017 USA
出版者IEEE Computer Society
摘要The real-time fault diagnosis system is very great important for steam turbine generator set due to a serious fault results in a reduced amount of electricity supply in power plant. A novel real-time fault diagnosis system is proposed by using strata hierarchical fuzzy CMAC neural network. A framework of the fault diagnosis system is described. Hierarchical fault diagnostic structure is discussed in detail. The model of a novel fault diagnosis system by using fuzzy CMAC are built and analyzed. A case of the diagnosis is simulated. The results show that the real-time fault diagnostic system is of high accuracy, quick convergence, and high noise rejection. It is also found that this model is feasible in real-time fault diagnosis. This electronic document is a "live" template. The various components of your paper [title, text, heads, etc.] are already defined on the style sheet, as illustrated by the portions given in this document. © 2009 IEEE.
关键词Failure analysis Fault tolerant computer systems Fuzzy inference Fuzzy neural networks Steam power plants Steam turbines Turbogenerators Electronic document Fault diagnosis systems FUZZY-CMAC Hierarchical Artificial Neural Networks Hierarchical neural networks Real-time fault diagnosis Real-time fault diagnostics Steam turbine-generator set
DOI10.1109/APPEEC.2009.4918103
收录类别EI
语种英语
WOS研究方向Energy & Fuels
WOS类目Energy & Fuels
WOS记录号WOS:000270497300323
EI入藏号20093512276165
EI主题词Fault detection
ISSN21574839
来源库Compendex
分类代码614 Steam Power Plants - 617.2 Steam Turbines - 705.2 Electric Generators - 722.4 Digital Computers and Systems - 723.4 Artificial Intelligence - 723.4.1 Expert Systems
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被引频次[WOS]:0   [WOS记录]     [WOS相关记录]
文献类型会议论文
条目标识符https://ir.lut.edu.cn/handle/2XXMBERH/117075
专题机电工程学院
通讯作者Yan, Changfeng
作者单位1.Lanzhou Univ Technol, Sch Mech & Elect Engn, Lanzhou, Peoples R China
2.Tongji Univ, CIMS Res Ctr, Shanghai, Peoples R China
第一作者单位兰州理工大学
通讯作者单位兰州理工大学
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Yan, Changfeng,Zhang, Hao,Wu, Lixiao. A novel real-time fault diagnostic system by using strata hierarchical artificial neural network[C]. 345 E 47TH ST, NEW YORK, NY 10017 USA:IEEE Computer Society,2009:1375-+.
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