DocumentCode
296929
Title
An approach to fault diagnosis in dynamic systems using Kohonen neural networks
Author
Ficola, Antonio ; Cava, Michele La ; Magnino, Fabio
Author_Institution
Istituto di Elettronica, Perugia Univ., Italy
Volume
1
fYear
34881
fDate
10-14 Jul1995
Firstpage
166
Abstract
In this paper, a fault diagnosis system for linear dynamic systems based on Kohonen neural networks is proposed. The technique of pattern recognition is taken into account for the classification of the modes of operation of the system. The pattern is given by the coefficients of the transfer matrix which are estimated by a least squares algorithm; in this way classification can also be achieved under dynamic conditions. The method employs an unsupervised neural network based on competitive learning. An example is proposed to show the effectiveness of this approach
Keywords
fault diagnosis; least squares approximations; linear systems; pattern classification; self-organising feature maps; transfer function matrices; unsupervised learning; Kohonen neural networks; competitive learning; fault diagnosis; least squares algorithm; linear dynamic systems; operation mode classification; pattern recognition; transfer matrix coefficients; unsupervised neural network; Electronic mail; Fault detection; Fault diagnosis; Feature extraction; Intelligent networks; Least squares approximation; Neural networks; Parameter estimation; Pattern recognition; State estimation;
fLanguage
English
Publisher
ieee
Conference_Titel
Industrial Electronics, 1995. ISIE '95., Proceedings of the IEEE International Symposium on
Conference_Location
Athens
Print_ISBN
0-7803-7369-3
Type
conf
DOI
10.1109/ISIE.1995.496495
Filename
496495
Link To Document