[1]胡寿松,徐德友,张敏.基于粗糙神经网络的歼击机操纵面智能故障诊断[J].南京师范大学学报(工程技术版),2004,04(03):001-6.
　HU Shousong,XU Deyou,ZHANG Min.Intelligent Fault Diagnosis of Fighter Control Surfaces Based on Rough Neural Network[J].Journal of Nanjing Normal University(Engineering and Technology),2004,04(03):001-6.
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基于粗糙神经网络的歼击机操纵面智能故障诊断
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南京师范大学学报（工程技术版）[ISSN:1006-6977/CN:61-1281/TN]

卷:
04卷
期数:
2004年03期
页码:
001-6
栏目:
出版日期:
2004-09-30

文章信息/Info

Title:
Intelligent Fault Diagnosis of Fighter Control Surfaces Based on Rough Neural Network
作者:
胡寿松; 徐德友; 张敏
南京航空航天大学自动化学院, 江苏南京210016
Author(s):
HU Shousong;  XU Deyou;  ZHANG Min
School of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
关键词:
故障诊断;  神经网络;  粗集理论;  歼击机
Keywords:
fault diagnosis;  neural network;  rough- set theory;  fighter
分类号:
V267
摘要:
提出了一种基于粗糙神经网络的歼击机操纵面故障诊断方法 .给出并证明了可利用粗集方法对故障信息进行快速特征提取的方法 ,用其作为神经网络的前置系统进行信息预处理 ,减少了所需样本数目 ,从而简化了神经网络结构 ,减少了网络训练时间 ,并且充分利用了神经网络容错及抗干扰能力 ,有效地降低了故障诊断中的误报率和漏报率 .该方法可以进行组合故障的诊断 ,且具有较好的鲁棒性 .仿真实验说明了该方法的有效性和实用性
Abstract:
A fault diagnosis method for the fighter control surfaces is presented, which is based on rough neural network. The feature extraction based on the rough-set method is given and proven, and can be utilized to pre- process the fault information. Therefore, the needed training samples can be reduced, the neural network structure can be simplified, and the training time of the network can be shortened. The method takes full advantage of the neural network‘ s capability of faul-t tolerance and ant-i disturbance, reduces the false alarming rate and omission alarming rate, can diagnose the composed faults and can retain good robustness.

参考文献/References:

[ 1] Arabshahi P, Finley S G, Pham T, et al. An Intelligent Fault Detection and Isolation Architecture for Antenna Arrays [ A] . JPL TDA Progress Report, 1998. 124 -132.
[ 2] Rahnamai K, Caglayan A K, Allen SM. Detection, Identif-i cation and Estimation of Surface Damage/ Actuator Failure For High Performance Aircraft[ A] . Proc American Control Conference[ C] . Atlanta, 1988. 15- 17.
[ 3] Guglielmi G, Parisini T, Rossi G. Fault Diagnosis and Neural Networks, A Power Plant Application[ J] . Control Engineering Practic, 1995, 3( 5) : 601- 620.
[ 4] Pawlak Z. Rough set[ J] . International Journal of Information and Computer Science, 1982, 11( 5) : 341- 356.

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备注/Memo

备注/Memo:
基金项目: 国家自然科学基金重点资助项目( 60234010) ; 航空科学基金资助项目(02E52025) ; 国防基础科研资助项目(K1603060318) .
作者简介: 胡寿松( 1937- ) , 教授, 博士生导师, 主要从事故障诊断、自适应及自修复控制以及鲁棒控制等方面的教学与研究.E-mail: hushousong-nuaa@ 163. com
更新日期/Last Update: 2013-04-29