21 March 2001 Fault diagnosis hybrid system using a Luenberger-based detection filter and neural networks
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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.
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Rocco Tarantino, Rocco Tarantino, Kathiusca Cabezas, Kathiusca Cabezas, Francklin Rivas-Echeverria, Francklin Rivas-Echeverria, Eliezer Colina-Morles, Eliezer Colina-Morles, } "Fault diagnosis hybrid system using a Luenberger-based detection filter and neural networks", Proc. SPIE 4390, Applications and Science of Computational Intelligence IV, (21 March 2001); doi: 10.1117/12.421159; https://doi.org/10.1117/12.421159
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