Fault detection with Conditional Gaussian Network
| Contribuinte(s) |
Laboratoire Angevin de Recherche en Ingénierie des Systèmes (LARIS) ; Université d'Angers (UA) |
|---|---|
| Data(s) |
2015
|
| Resumo |
International audience <p>The main interest of this paper is to illustrate a new representation of the Principal Component Analysis (PCA) for fault detection under a Conditional Gaussian Network (CGN), a special case of Bayesian networks. PCA and its associated quadratic statistics such as T2 and SPE are integrated under a sole CGN. The proposed framework projects a new observation into an orthogonal space and gives probabilities on the state of the system. It could do so even when some data in the sample test are missing. This paper also gives the probabilities thresholds to use in order to match quadratic statistics decisions. The proposed network is validated and compared to the standard PCA scheme for fault detection on the Tennessee Eastman Process and the Hot Forming Process.</p> |
| Identificador |
hal-01392077 https://hal.archives-ouvertes.fr/hal-01392077 DOI : 10.1016/j.engappai.2015.07.020 OKINA : ua13842 |
| Idioma(s) |
en |
| Publicador |
HAL CCSD Elsevier |
| Relação |
info:eu-repo/semantics/altIdentifier/doi/10.1016/j.engappai.2015.07.020 |
| Fonte |
ISSN: 0952-1976 Engineering Applications of Artificial Intelligence https://hal.archives-ouvertes.fr/hal-01392077 Engineering Applications of Artificial Intelligence, Elsevier, 2015, 45, pp.473 - 481. <10.1016/j.engappai.2015.07.020> |
| Palavras-Chave | #Conditional Gaussian Networks #fault detection #Hot Forming Process #PCA #Statistical inference #Tennessee Eastman Process #[SPI] Engineering Sciences [physics] |
| Tipo |
info:eu-repo/semantics/article Journal articles |
