Neuro-fuzzy systems for fault detection and isolation in nuclear reactors

Author

Evsukoff, Alexandre ; Schirru, Roberto

Author_Institution

Instituto Doris Ferraz de Aragon, ILTC, Niteroi, Brazil

Volume

3

fYear

2001

fDate

6/23/1905 12:00:00 AM

Firstpage

1460

Lastpage

1463

Abstract

This work presents an application of recurrent neuro-fuzzy systems to fault detection and isolation in nuclear reactors. In the adopted framework, a fuzzification module is linked to an inference module, which is actually a neural network adapted to the recognition of the dynamic evolution of process variables. Two different approaches to the neural network inference module are tested over data simulated by a commissioned simulator for the detection and isolation of a number of security related faults in a nuclear reactor

Keywords

fault diagnosis; fuzzy neural nets; inference mechanisms; nuclear reactor maintenance; pattern classification; recurrent neural nets; fault detection; fault isolation; fuzzification module; fuzzy neural network; inference module; neural-fuzzy systems; nuclear reactors; pattern classification; recurrent neural network; recurrent topology; Computational modeling; Fault detection; Fault diagnosis; Fuzzy neural networks; Fuzzy sets; Humans; Monitoring; Network topology; Neural networks; Pattern recognition;

fLanguage

English

Publisher

ieee

Conference_Titel

Fuzzy Systems, 2001. The 10th IEEE International Conference on

Conference_Location

Melbourne, Vic.

Print_ISBN

0-7803-7293-X

Type

conf

DOI

10.1109/FUZZ.2001.1008936

Filename

1008936