Combining classification techniques with Kalman filters for aircraft engine diagnostics

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

dc.contributor.author	Dewallef, P	en
dc.contributor.author	Romessis, C	en
dc.contributor.author	Leonard, O	en
dc.contributor.author	Mathioudakis, K	en
dc.date.accessioned	2014-03-01T02:49:44Z
dc.date.available	2014-03-01T02:49:44Z
dc.date.issued	2004	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/34712
dc.relation.uri	http://www.scopus.com/inward/record.url?eid=2-s2.0-10244270744&partnerID=40&md5=a4b0aef27df0049da070e08b562d2e2d	en
dc.subject.other	Algorithms	en
dc.subject.other	Constraint theory	en
dc.subject.other	Data acquisition	en
dc.subject.other	Gas turbines	en
dc.subject.other	Kalman filtering	en
dc.subject.other	Mathematical models	en
dc.subject.other	Matrix algebra	en
dc.subject.other	Optimization	en
dc.subject.other	Redundancy	en
dc.subject.other	Regression analysis	en
dc.subject.other	Turbofan engines	en
dc.subject.other	Bayesian belief network (BBN)	en
dc.subject.other	Gas turbine engines	en
dc.subject.other	Regression algorithms	en
dc.subject.other	Soft-constrained Kalman filter (SCKF)	en
dc.subject.other	Aircraft engines	en
dc.title	Combining classification techniques with Kalman filters for aircraft engine diagnostics	en
heal.type	conferenceItem	en
heal.publicationDate	2004	en
heal.abstract	A diagnostic method consisting of a combination of Kalman filters and Bayesian Belief Networks (BBN) is presented. A softconstrained 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 identification capability in comparison to the stand alone Kalman filter. The paper focuses on the way of combining the information produced by the BBN with the Kalman filter. An extensive set of fault cases is used to test the method on a typical civil turbofan layout. The effectiveness of the method is thus demonstrated and its advantages over individual constituent methods are shown.	en
heal.journalName	Proceedings of the ASME Turbo Expo 2004	en
dc.identifier.volume	2	en
dc.identifier.spage	595	en
dc.identifier.epage	603	en