Sensors´ FDD by quadruple and modified ART-1 ANNs

Author

Khan, Muhammad Rafiq

Author_Institution

Pakistan Atomic Energy Commission, Islamabad, Pakistan

Volume

1

fYear

1998

fDate

4-8 May 1998

Firstpage

262

Abstract

An approach to continuous online detection and diagnosis of sensors multiple simultaneous faults with various degrees of deviations is presented. The FDD system is composed of a feature detector, a novel artificial neural quadruple network capable of performing rule-based operations and a modified ART-1 that can memorize the faults´ history. An additional backward intermediate term flush memory is employed in the ART-1 to memorize faults history to eliminate external disturbances and noise. The feature detector is developed such that it is capable of providing a set of vectors of digital residuals over a full range and for various combinations of simultaneous faults. The system is successfully employed for a nuclear power plant waste treatment system´s sensors FDD

Keywords

ART neural nets; fault diagnosis; feature extraction; multilayer perceptrons; nuclear power stations; recurrent neural nets; sensors; waste disposal; artificial neural quadruple network; backward intermediate term flush memory; continuous online fault detection; continuous online fault diagnosis; digital residuals; feature detector; modified ART-1 ANN; nuclear power plant waste treatment system; rule-based operations; Detectors; Fault detection; Fault diagnosis; Hardware; History; Neural networks; Power generation; Production; Redundancy; Robustness;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks Proceedings, 1998. IEEE World Congress on Computational Intelligence. The 1998 IEEE International Joint Conference on

Conference_Location

Anchorage, AK

ISSN

1098-7576

Print_ISBN

0-7803-4859-1

Type

conf

DOI

10.1109/IJCNN.1998.682274

Filename

682274