Detection of gas turbines malfunctions from emission concentration distributions

Romessis, C; Mathioudakis, K

dc.contributor.author	Romessis, C	en
dc.contributor.author	Mathioudakis, K	en
dc.date.accessioned	2014-03-01T02:51:04Z
dc.date.available	2014-03-01T02:51:04Z
dc.date.issued	2007	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/35340
dc.relation.uri	http://www.scopus.com/inward/record.url?eid=2-s2.0-34548726860&partnerID=40&md5=9c53183827cab802892abd33b158e603	en
dc.subject.other	Defects	en
dc.subject.other	Exhaust gases	en
dc.subject.other	Fault detection	en
dc.subject.other	Industrial emissions	en
dc.subject.other	Neural networks	en
dc.subject.other	Turbofan engines	en
dc.subject.other	Diagnostic procedures	en
dc.subject.other	Engine emissions	en
dc.subject.other	Malfunctions	en
dc.subject.other	Probabilistic classifiers	en
dc.subject.other	Gas turbines	en
dc.title	Detection of gas turbines malfunctions from emission concentration distributions	en
heal.type	conferenceItem	en
heal.publicationDate	2007	en
heal.abstract	A method for detecting gas turbines malfunctions through engine emissions concentration plots is presented. The method is materialized through the use of a bank of Probabilistic Neural Networks (PNNs). The main idea comes from the fact that specific operating and health conditions of an engine lead to specific concentrations of emissions on the exhaust area. By comparison of an emission concentration plot with emission plots of known engine health conditions, diagnostic conclusions can be extracted. The stochastic nature of emission concentrations can be handled by PNNs, a specific type of Artificial Neural Networks which are known to be efficient probabilistic classifiers. 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 PNNs, follows. The case of an operating family of turbofan engines is used to evaluate the effectiveness of the method. The examined case demonstrates that the proposed method can act as an additional tool on the existing methods for better and safer fault diagnosis. Copyright © 2007 by ASME.	en
heal.journalName	Proceedings of the ASME Turbo Expo	en
dc.identifier.volume	1	en
dc.identifier.spage	515	en
dc.identifier.epage	522	en