Implementation of stochastic methods for industrial gas turbine fault diagnosis

Romessis, C; Mathioudakis, K

dc.contributor.author	Romessis, C	en
dc.contributor.author	Mathioudakis, K	en
dc.date.accessioned	2014-03-01T02:43:22Z
dc.date.available	2014-03-01T02:43:22Z
dc.date.issued	2005	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/31366
dc.relation.uri	http://www.scopus.com/inward/record.url?eid=2-s2.0-27744510711&partnerID=40&md5=13035a9e10b26dc4fcea3149f5aa3f59	en
dc.relation.uri	http://www.ltt.mech.ntua.gr/paperfull/GT2005-68739.pdf	en
dc.subject	Diagnostic Method	en
dc.subject	Diagnostic Tool	en
dc.subject	Fault Diagnosis	en
dc.subject	Gas Turbine	en
dc.subject	Probabilistic Neural Network	en
dc.subject	Stochastic Method	en
dc.subject	bayesian belief network	en
dc.subject.other	Neural networks	en
dc.subject.other	Probability	en
dc.subject.other	Sensors	en
dc.subject.other	Stochastic control systems	en
dc.subject.other	Bayesian Belief Networks (BBN)	en
dc.subject.other	Component faults	en
dc.subject.other	Diagnostic problem	en
dc.subject.other	Gas turbines	en
dc.title	Implementation of stochastic methods for industrial gas turbine fault diagnosis	en
heal.type	conferenceItem	en
heal.identifier.secondary	GT2005-68739	en
heal.publicationDate	2005	en
heal.abstract	Implementation of stochastic diagnostic methods for diagnosis of sensor or component faults is presented. Two industrial gas turbines are considered as test cases, one twin and one single shaft arrangement. Methods based on Probabilistic Neural Networks (PNN) and Bayesian Belief Networks (BBN), are implemented. The ability for successful diagnosis is demonstrated on specific cases of sensor malfunctions, as well as on two types of compressor deterioration, fouling and variable vane mistuning. The examined diagnostic problem and the methods of PNN for sensor fault diagnosis and BBN for the diagnosis of component faults are first described. For each gas turbine case, the implementation of the diagnostic methods is shown and application to fault cases that occurred is presented. The effectiveness of the stochastic diagnostic methods demonstrates that they offer a powerful alternative diagnostic tool. Copyright © 2005 by ASME.	en
heal.journalName	Proceedings of the ASME Turbo Expo	en
dc.identifier.volume	1	en
dc.identifier.spage	723	en
dc.identifier.epage	730	en