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

Romessis, C; Mathioudakis, K

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