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Gas turbine fault diagnosis using fuzzy-based decision fusion

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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


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