DocumentCode
2441243
Title
Fault detection using neural networks
Author
Silves, G. ; Verona, F.B. ; Innocenti, M. ; Napolitano, M.
Author_Institution
Dept. of Electr. Syst. & Autom., Pisa Univ., Italy
Volume
6
fYear
1994
fDate
27 Jun- 2 Jul 1994
Firstpage
3796
Abstract
This paper presents a neural network approach for the problem of sensor failure detection and identification for a flight control system without any sensor redundancy. The problem is solved with the introduction of online learning neural network estimators. The online learning of such a neural network is performed by using the extended backpropagation algorithm, a new method which offers several improvements with respect to the standard backpropagation algorithm
Keywords
aerospace computing; aircraft control; backpropagation; fault diagnosis; neural nets; real-time systems; sensors; aircraft control; extended backpropagation; failure identification; fault diagnosis; flight control system; neural networks; online learning; sensor failure detection; Automation; Backpropagation algorithms; Electrical fault detection; Fault detection; Fault diagnosis; Mechanical sensors; Neural networks; Redundancy; Sensor phenomena and characterization; Sensor systems;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks, 1994. IEEE World Congress on Computational Intelligence., 1994 IEEE International Conference on
Conference_Location
Orlando, FL
Print_ISBN
0-7803-1901-X
Type
conf
DOI
10.1109/ICNN.1994.374815
Filename
374815
Link To Document