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[求助] 请老师帮忙查文章检索号！

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。
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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
入藏号: WOS:000350116100003
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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®
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非常感谢！！

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