Probabilistic neural networks for validation of on-board jet engine data

Mathioudakis, K; Romessis, C

dc.contributor.author	Mathioudakis, K	en
dc.contributor.author	Romessis, C	en
dc.date.accessioned	2014-03-01T02:42:55Z
dc.date.available	2014-03-01T02:42:55Z
dc.date.issued	2004	en
dc.identifier.issn	0954-4100	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/31141
dc.relation.uri	http://www.scopus.com/inward/record.url?eid=2-s2.0-8644224949&partnerID=40&md5=372f765de7fbb46e380882eb5ab56016	en
dc.subject	Artificial intelligence	en
dc.subject	Gas turbine diagnostics	en
dc.subject	Probabilistic neural networks	en
dc.subject	Probabilistic pattern recognition	en
dc.subject	Sensor faults	en
dc.subject.classification	Engineering, Aerospace	en
dc.subject.classification	Engineering, Mechanical	en
dc.subject.other	Backpropagation	en
dc.subject.other	Functions	en
dc.subject.other	Kalman filtering	en
dc.subject.other	Neural networks	en
dc.subject.other	Pattern recognition	en
dc.subject.other	Pressure effects	en
dc.subject.other	Probability distributions	en
dc.subject.other	Sensors	en
dc.subject.other	Turbomachinery	en
dc.subject.other	Gas turbine diagnostics	en
dc.subject.other	Probabilistic neural networks	en
dc.subject.other	Probabilistic pattern recognition	en
dc.subject.other	Sensor faults	en
dc.subject.other	Jet engines	en
dc.title	Probabilistic neural networks for validation of on-board jet engine data	en
heal.type	conferenceItem	en
heal.language	English	en
heal.publicationDate	2004	en
heal.abstract	A method is presented for identification of faults in the readings of sensors used to monitor the performance and the condition of jet engines. Probabilistic neural networks are used to detect the presence and identify the location and magnitude of faults (biases) in sensor readings. The faults can be detected on sets comprising a limited number of instruments, typical of those available for on-board monitoring of jet engines. An engine performance model is used to support the constitution of a network. Training information is built using the model to produce data for a comprehensive set of healthy and faulty situations. The network performance in detecting and quantifying sensor faults is validated on a large number of fault cases, also generated by a model, which are used for testing the network and cover a wide range of conditions that can be encountered in practice. An engine, representative of current large civil engine designs (large bypass, partially mixed turbofan), serves as the test vehicle for demonstration of the way the method is materialized.	en
heal.publisher	PROFESSIONAL ENGINEERING PUBLISHING LTD	en
heal.journalName	Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering	en
dc.identifier.isi	ISI:000221725300005	en
dc.identifier.volume	218	en
dc.identifier.issue	1	en
dc.identifier.spage	59	en
dc.identifier.epage	72	en