Fusion of gas turbines diagnostic inference - The dempster-schafer approach

Romessis, C; Kyriazis, A; Mathioudakis, K

dc.contributor.author	Romessis, C	en
dc.contributor.author	Kyriazis, A	en
dc.contributor.author	Mathioudakis, K	en
dc.date.accessioned	2014-03-01T02:44:38Z
dc.date.available	2014-03-01T02:44:38Z
dc.date.issued	2007	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/31922
dc.relation.uri	http://www.scopus.com/inward/record.url?eid=2-s2.0-34548723983&partnerID=40&md5=a8d6260c536cf5be78377f8cbde8af40	en
dc.relation.uri	http://www.ltt.mech.ntua.gr/paperfull/GT2007-27043.pdf	en
dc.subject	Diagnostic Method	en
dc.subject	Gas Turbine	en
dc.subject	Probabilistic Neural Network	en
dc.subject	Thermodynamics	en
dc.subject	bayesian belief network	en
dc.subject.other	Accident prevention	en
dc.subject.other	Bayesian networks	en
dc.subject.other	Compressors	en
dc.subject.other	Neural networks	en
dc.subject.other	Probability	en
dc.subject.other	Thermodynamics	en
dc.subject.other	Dempster-Schafer theory	en
dc.subject.other	Diagnostic	en
dc.subject.other	Fusion technique	en
dc.subject.other	Probabilistic Neural Networks	en
dc.subject.other	Gas turbines	en
dc.title	Fusion of gas turbines diagnostic inference - The dempster-schafer approach	en
heal.type	conferenceItem	en
heal.publicationDate	2007	en
heal.abstract	This paper proposes a fusion technique allowing the merge of conclusions provided by diagnostic methods that act independently for the detection of gas turbine faults. The proposed technique adopts the principles of Dempster-Schafer theory for the fusion of two diagnostic methods output; these are the method of Bayesian Belief Networks (BBN) and the method of Probabilistic Neural Networks (PNN). The proposed technique has been applied for the detection of thermodynamic as well as mechanical faults on gas turbines. First, the case of a turbofan engine of civil aviation is examined. The proposed technique allows the fusion of diagnostic inference on the presence of several faults of thermodynamic nature. Then the case of a radial and an axial compressor are examined, where several mechanical faults are deliberately implemented. In all cases, the effectiveness of the proposed fusion technique demonstrates that the merge of diagnostic information from different sources leads to better and safer diagnosis. Copyright © 2007 by ASME.	en
heal.journalName	Proceedings of the ASME Turbo Expo	en
dc.identifier.volume	1	en
dc.identifier.spage	505	en
dc.identifier.epage	514	en