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Intelligent Fault Diagnosis of Fighter Control Surfaces Based on Rough Neural Network(PDF)

南京师范大学学报（工程技术版）[ISSN:1006-6977/CN:61-1281/TN]

Issue:
2004年03期
Page:
1-6
Research Field:
Publishing date:

Info

Title:
Intelligent Fault Diagnosis of Fighter Control Surfaces Based on Rough Neural Network
Author(s):
HU Shousong;  XU Deyou;  ZHANG Min
School of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
Keywords:
fault diagnosis;  neural network;  rough- set theory;  fighter
PACS:
V267
DOI:
-
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.

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.

Memo

Memo:
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Last Update: 2013-04-29