FAULT CLASSIFICATION FOR A CLASS OF TIME-VARYING SYSTEMS BY USING OVERLAPPED ART2A NETWORKS

Fault diagnosis currently offers different alternatives to classify faults at early stages, such as model-based and knowledge-based techniques. Nevertheless, fault classification for time-varying systems is still an open problem. Strategies such as self-organizing maps and principal component analys...

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Bibliographic Details
Published inControl and intelligent systems Vol. 36; no. 1; p. 64
Main Authors Benítez-Pérez, H, Solano-González, J, Cárdenas-Flores, F, García-Nocetti, D F
Format Journal Article
LanguageEnglish
Published Calgary ACTA Press 01.01.2008
Subjects
Advantages
Artificial intelligence
Behavior
Case studies
Classification
Fault diagnosis
Methods
Neural networks
Noise
Principal components analysis
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Summary:Fault diagnosis currently offers different alternatives to classify faults at early stages, such as model-based and knowledge-based techniques. Nevertheless, fault classification for time-varying systems is still an open problem. Strategies such as self-organizing maps and principal component analysis ensure fault classification to bounded time-variance faults. The approach presented in this paper proposes the use of three non-supervised neural networks. The first two networks overlapped by certain time shift. The third network performs a comparison between the two networks outputs in the previous stage. As a result, the system classifies the fault even if the system is time-variant. The strategy named as Overlapped ART2A Network, aims to obtain an autonomous performance and on-line fault classification. Results show the effectiveness of the approach considering a case study with fault and fault-free scenarios. [PUBLICATION ABSTRACT]
ISSN:2561-1771
2561-178X
DOI:10.2316/Journal.201.2008.1.201-1820