Combining Classification Techniques With Kalman Filters for Aircraft Engine Diagnostics

Dewallef, P; Romessis, C; Léonard, O; Mathioudakis, K

dc.contributor.author	Dewallef, P	en
dc.contributor.author	Romessis, C	en
dc.contributor.author	Léonard, O	en
dc.contributor.author	Mathioudakis, K	en
dc.date.accessioned	2014-03-01T01:56:37Z
dc.date.available	2014-03-01T01:56:37Z
dc.date.issued	2008	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/28197
dc.relation.uri	http://www.ltt.mech.ntua.gr/paperfull/GT2004-53541-journal.pdf	en
dc.relation.uri	http://www.ltt.mech.ntua.gr/paperfull/GT2004-53541.pdf	en
dc.subject	a priori information	en
dc.subject	Diagnostic Method	en
dc.subject	bayesian belief network	en
dc.subject	kalman filter	en
dc.title	Combining Classification Techniques With Kalman Filters for Aircraft Engine Diagnostics	en
heal.type	journalArticle	en
heal.publicationDate	2008	en
heal.abstract	A diagnostic method consisting of a combination of Kalman filters and Bayesian Belief Networks (BBN) is presented. A soft- constrained Kalman filter uses a priori information derived by a BBN at each time step, to derive estimations of the unknown health parameters. The resulting algorithm has improved identi- fication capability in comparison to the stand alone Kalman filter. The paper	en