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manners金虫 (正式写手)
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[求助]
请老师帮忙查文章检索号！
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如题。Jianchen WANG, et al. An improved decentralized model for SFDI demonstrated on an airplane system [J]. Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering, 2015, 229:423-434. SCI检索。 谢谢老师，感激不尽！ [ 发自手机版 https://muchong.com/3g ] |
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manners
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5楼2015-04-20 10:22:09
baiyuefei
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2楼2015-04-20 09:36:49
baiyuefei
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An improved decentralized model for sensor fault detection and isolation demonstrated on an airplane system 作者:Wang, JC (Wang, Jianchen)[ 1 ] ; Qi, XH (Qi, Xiaohui)[ 1 ] PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART G-JOURNAL OF AEROSPACE ENGINEERING 卷: 229 期: 3 页: 423-434 DOI: 10.1177/0954410014534202 出版年: MAR 2015 查看期刊信息 PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART G-JOURNAL OF AEROSPACE ENGINEERING 出版商 SAGE PUBLICATIONS LTD, 1 OLIVERS YARD, 55 CITY ROAD, LONDON EC1Y 1SP, ENGLAND ISSN: 0954-4100 eISSN: 2041-3025 研究领域 Engineering 摘要 With various modeling technologies applied, the sensor fault detection and isolation scheme based on the decentralized model (also referred to as dedicated observer scheme) becomes a popular approach for sophisticated systems. However, the commonly used modeling approach in many literatures that directly takes measurement values as model inputs may result in residual crosstalks and even false alarms. In this paper, the traditional decentralized model scheme is analyzed and a novel scheme based on the time window interactive prediction structure is proposed. Then, the Elman neural network is applied to model identification due to its nonlinear approximation and online learning properties. Finally, Simulations for comparison using the decoupled longitudinal motion model of some airplane are performed, and the results show that the proposed scheme has higher detection speed, lower false alarm rate and less undetected faults. 关键词 作者关键词:Sensor fault detection and isolation; decentralized model; residual crosstalk; time window interactive prediction; Elman neural network; airplane system KeyWords Plus:NEURAL-NETWORKS; DIAGNOSIS; OBSERVER; SCHEMES; UAV 作者信息 通讯作者地址: Wang, JC (通讯作者) Shijiazhuang Mech Engn Coll, Dept Vehicular Engn, 97 Hepingxi Rd, Shijiazhuang 050003, Peoples R China. 地址: [ 1 ] Shijiazhuang Mech Engn Coll, Dept Vehicular Engn, Shijiazhuang 050003, Peoples R China 电子邮件地址:lichen197@163.com 出版商 SAGE PUBLICATIONS LTD, 1 OLIVERS YARD, 55 CITY ROAD, LONDON EC1Y 1SP, ENGLAND 类别 / 分类 研究方向:Engineering Web of Science 类别:Engineering, Aerospace; Engineering, Mechanical 文献信息 文献类型:Article 语种:English 入藏号: WOS:000350116100003 ISSN: 0954-4100 eISSN: 2041-3025 其他信息 IDS 号: CC1QI Web of Science 核心合集中的 "引用的参考文献": 24 Web of Science 核心合集中的 "被引频次": 0 JCR® 类别 JCR 分区 ENGINEERING, AEROSPACE Q3 ENGINEERING, MECHANICAL Q4 数据来自第 2013 版 Journal Citation Reports® |
3楼2015-04-20 09:37:18
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【答案】应助回帖
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manners: 金币+5, ★★★★★最佳答案 2015-04-20 10:21:49
心静_依然: LS-EPI+1, 感谢应助 2015-04-20 10:25:20
manners: 金币+5, ★★★★★最佳答案 2015-04-20 10:21:49
心静_依然: LS-EPI+1, 感谢应助 2015-04-20 10:25:20
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4楼2015-04-20 09:37:33
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