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Proceedings Paper

Fault diagnosis hybrid system using a Luenberger-based detection filter and neural networks
Author(s): Rocco Tarantino; Kathiusca Cabezas; Francklin Rivas-Echeverria; Eliezer Colina-Morles
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Paper Abstract

The present paper proposes a new layout for failure detection and diagnosis in industrial dynamic systems in which, failure vector decoupling is not always possible, due to the failure intrinsic propagation. In this case diagnosis can be determined due to the existing correlation between the failure vector and residual vector time patterns. The greatest benefit of this study is the failure detection method, Luenberger observer based detection filter, through vectorial residual generation combined with the pattern recognition technique based on neural networks theory. The synergy of both methods offer a wider application range to diagnosis problem solutions, in systems under presence of non-decoupled failures.

Paper Details

Date Published: 21 March 2001
PDF: 12 pages
Proc. SPIE 4390, Applications and Science of Computational Intelligence IV, (21 March 2001); doi: 10.1117/12.421159
Show Author Affiliations
Rocco Tarantino, CRP Paraguana (Venezuela)
Kathiusca Cabezas, Univ. de Los Andes (Venezuela)
Francklin Rivas-Echeverria, Univ. de Los Andes (Venezuela)
Eliezer Colina-Morles, Univ. de Los Andes (Venezuela)


Published in SPIE Proceedings Vol. 4390:
Applications and Science of Computational Intelligence IV
Kevin L. Priddy; Paul E. Keller; Peter J. Angeline, Editor(s)

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