A Bayesian network dealing with measurements and residuals for system monitoring
Contribuinte(s) |
Laboratoire Angevin de Recherche en Ingénierie des Systèmes (LARIS) ; Université d'Angers (UA) |
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Data(s) |
2015
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Resumo |
International audience <p>The purpose of this paper is to present an original method for system monitoring with Bayesian networks. Our proposal is to associate a data-driven method to another model-based under a common tool. The two methods are first modeled under a Bayesian network (conditional Gaussian network), and then combined to evaluate the system state. In the proposed framework the residuals and measures coexist under a probabilistic framework. This approach is tested on a simulation of a water heater process under some various circumstances and shows better results than the two methods used alone.</p> |
Identificador |
hal-01392071 https://hal.archives-ouvertes.fr/hal-01392071 DOI : 10.1177/0142331215581446 OKINA : ua12308 |
Idioma(s) |
en |
Publicador |
HAL CCSD SAGE Publications |
Relação |
info:eu-repo/semantics/altIdentifier/doi/10.1177/0142331215581446 |
Fonte |
ISSN: 0142-3312 Transactions of the Institute of Measurement and Control https://hal.archives-ouvertes.fr/hal-01392071 Transactions of the Institute of Measurement and Control, SAGE Publications, 2015, pp.0142331215581446 <10.1177/0142331215581446> |
Palavras-Chave | #Bayesian #data-driven #Model #Monitoring #[SPI] Engineering Sciences [physics] |
Tipo |
info:eu-repo/semantics/article Journal articles |