Bayesian network approach for gas path fault diagnosis

Romessis, C; Mathioudakis, K

dc.contributor.author	Romessis, C	en
dc.contributor.author	Mathioudakis, K	en
dc.date.accessioned	2014-03-01T02:49:43Z
dc.date.available	2014-03-01T02:49:43Z
dc.date.issued	2004	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/34704
dc.relation.uri	http://www.scopus.com/inward/record.url?eid=2-s2.0-10244242606&partnerID=40&md5=10c5ed6c06ffdda6f104dd9b8ba30118	en
dc.subject.other	Benchmarking	en
dc.subject.other	Compressors	en
dc.subject.other	Gas turbines	en
dc.subject.other	Information analysis	en
dc.subject.other	Jet engines	en
dc.subject.other	Probability density function	en
dc.subject.other	Problem solving	en
dc.subject.other	Vectors	en
dc.subject.other	Bayesian networks	en
dc.subject.other	Gas path fault diagnosis	en
dc.subject.other	High pressure compressors (HPC)	en
dc.subject.other	Gas fuel analysis	en
dc.title	Bayesian network approach for gas path fault diagnosis	en
heal.type	conferenceItem	en
heal.publicationDate	2004	en
heal.abstract	A method for solving the gas path analysis problem of jet engine diagnostics based on a probabilistic approach is presented. The method is materialized through the use of a Bayesian Belief Network (BBN). Building a BBN for gas turbine performance fault diagnosis requires information of a stochastic nature expressing the probability of whether a series of events occurred or not. This information can be extracted by a deterministic model and does not depend on hard to find flight data of different faulty operations of the engine. The diagnostic problem and the overall diagnostic procedure are first described. A detailed description of the way the diagnostic procedure is set-up, with focus on building the BBN from an engine performance model, follows. The case of a turbofan engine is used to evaluate the effectiveness of the method. Several simulated and benchmark fault case scenarios have been considered for this reason. The examined cases demonstrate that the proposed BBN-based diagnostic method composes a powerful tool. This work also shows that building a diagnostic tool, based on information provided by an engine performance model, is feasible and can be efficient as well.	en
heal.journalName	Proceedings of the ASME Turbo Expo 2004	en
dc.identifier.volume	2	en
dc.identifier.spage	691	en
dc.identifier.epage	699	en