Gas turbine fault diagnosis using fuzzy-based decision fusion

Kyriazis, A; Mathioudakis, K

dc.contributor.author	Kyriazis, A	en
dc.contributor.author	Mathioudakis, K	en
dc.date.accessioned	2014-03-01T01:30:47Z
dc.date.available	2014-03-01T01:30:47Z
dc.date.issued	2009	en
dc.identifier.issn	0748-4658	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/19630
dc.subject	Decision Fusion	en
dc.subject	Fault Diagnosis	en
dc.subject	Gas Turbine	en
dc.subject.classification	Engineering, Aerospace	en
dc.subject.other	Axial compressors	en
dc.subject.other	Decision fusion	en
dc.subject.other	Decision level fusion	en
dc.subject.other	Diagnostic decisions	en
dc.subject.other	Diagnostic methods	en
dc.subject.other	Diagnostic procedure	en
dc.subject.other	Fast response	en
dc.subject.other	Fault diagnosis	en
dc.subject.other	Final decision	en
dc.subject.other	Fusion techniques	en
dc.subject.other	Information fusion techniques	en
dc.subject.other	Performance data	en
dc.subject.other	Probabilistic neural networks	en
dc.subject.other	Decision theory	en
dc.subject.other	Fuzzy logic	en
dc.subject.other	Fuzzy sets	en
dc.subject.other	Indexing (of information)	en
dc.subject.other	Neural networks	en
dc.subject.other	Turbomachinery	en
dc.subject.other	Gas turbines	en
dc.title	Gas turbine fault diagnosis using fuzzy-based decision fusion	en
heal.type	journalArticle	en
heal.identifier.primary	10.2514/1.38629	en
heal.identifier.secondary	http://dx.doi.org/10.2514/1.38629	en
heal.language	English	en
heal.publicationDate	2009	en
heal.abstract	A two-step information fusion technique allowing the combination of fast response and performance data for the improvement of gas turbines diagnostic procedures is proposed. The proposed technique is derived from the notion of decision level fusion. Different diagnostic methods provide assessments for the condition of an engine, and the final decision is derived from a combination of these assessments. The diagnostic method of probabilistic neural networks acts independently and in parallel on data of a different nature. The conclusions are given for the first step of the fusion technique and are aggregated deriving the probability consensus. In a second step this consensus is classified within a set of examined faults using the fuzzy set theory and fuzzy logic, thus providing the final diagnostic decision. The effectiveness of the proposed technique is demonstrated through the application to data from a radial and an axial compressor. Copyright © 2008 by the American Institute of Aeronautics and Astronautics, Inc. All rights reserved.	en
heal.publisher	AMER INST AERONAUT ASTRONAUT	en
heal.journalName	Journal of Propulsion and Power	en
dc.identifier.doi	10.2514/1.38629	en
dc.identifier.isi	ISI:000264492900009	en
dc.identifier.volume	25	en
dc.identifier.issue	2	en
dc.identifier.spage	335	en
dc.identifier.epage	343	en