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	2014-03-01T01:23:40Z
dc.date.available	2014-03-01T01:23:40Z
dc.date.issued	2006	en
dc.identifier.issn	0742-4795	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/17073
dc.subject	Fault Diagnosis	en
dc.subject	bayesian network	en
dc.subject.classification	Engineering, Mechanical	en
dc.subject.other	Expert systems	en
dc.subject.other	Fault tolerant computer systems	en
dc.subject.other	Mathematical models	en
dc.subject.other	Probability	en
dc.subject.other	Random processes	en
dc.subject.other	Turbofan engines	en
dc.subject.other	Bayesian belief network	en
dc.subject.other	Benchmark fault	en
dc.subject.other	Deterministic model	en
dc.subject.other	Gas path fault diagnosis	en
dc.subject.other	Jet engines	en
dc.subject.other	Expert systems	en
dc.subject.other	Fault tolerant computer systems	en
dc.subject.other	Jet engines	en
dc.subject.other	Mathematical models	en
dc.subject.other	Probability	en
dc.subject.other	Random processes	en
dc.subject.other	Turbofan engines	en
dc.title	Bayesian network approach for gas path fault diagnosis	en
heal.type	journalArticle	en
heal.identifier.primary	10.1115/1.1924536	en
heal.identifier.secondary	http://dx.doi.org/10.1115/1.1924536	en
heal.language	English	en
heal.publicationDate	2006	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. Copyright © 2006 by ASME.	en
heal.publisher	ASME-AMER SOC MECHANICAL ENG	en
heal.journalName	Journal of Engineering for Gas Turbines and Power	en
dc.identifier.doi	10.1115/1.1924536	en
dc.identifier.isi	ISI:000234192600009	en
dc.identifier.volume	128	en
dc.identifier.issue	1	en
dc.identifier.spage	64	en
dc.identifier.epage	72	en