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Setting up of a probabilistic neural network for sensor fault detection including operation with component faults

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dc.contributor.author Romessis, C en
dc.contributor.author Mathioudakis, K en
dc.date.accessioned 2014-03-01T02:49:16Z
dc.date.available 2014-03-01T02:49:16Z
dc.date.issued 2002 en
dc.identifier.uri https://dspace.lib.ntua.gr/xmlui/handle/123456789/34457
dc.relation.uri http://www.scopus.com/inward/record.url?eid=2-s2.0-0037002777&partnerID=40&md5=7f7e27d350a66be6be69b00537598b11 en
dc.subject.other Compressors en
dc.subject.other Neural networks en
dc.subject.other Pressure en
dc.subject.other Temperature en
dc.subject.other Vectors en
dc.subject.other Probabilistic neural networks en
dc.subject.other Sensor fault detection en
dc.subject.other Sensor fault identification en
dc.subject.other Gas turbines en
dc.title Setting up of a probabilistic neural network for sensor fault detection including operation with component faults en
heal.type conferenceItem en
heal.publicationDate 2002 en
heal.abstract The diagnostic ability of Probabilistic Neural Networks (PNN) for detecting sensor faults on gas turbines is examined. The structure and the features of a PNN, for sensor fault detection, are presented. It is shown that with the proposed formulation, a powerful tool for sensor fault identification is produced. A particular feature of the PNN produced is the ability to detect sensor faults even in the presence of engine component malfunction, as well as on deteriorated engines. In such situations, the size of bias that can be identified increases. The way to establish the limits of sensor bias that can be detected is presented along with results from application to test cases with realistic noise magnitudes. The diagnostic procedure proposed here is also supported by an engine performance model. The data used for setting up and testing the PNN are generated by such a model. en
heal.journalName American Society of Mechanical Engineers, International Gas Turbine Institute, Turbo Expo (Publication) IGTI en
dc.identifier.volume 2 A en
dc.identifier.spage 101 en
dc.identifier.epage 108 en


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