Neural networks and simple models for the fault diagnosis of naval turbochargers

Pantelelis, NG; Kanarachos, AE; Gotzias, N

dc.contributor.author	Pantelelis, NG	en
dc.contributor.author	Kanarachos, AE	en
dc.contributor.author	Gotzias, N	en
dc.date.accessioned	2014-03-01T01:15:44Z
dc.date.available	2014-03-01T01:15:44Z
dc.date.issued	2000	en
dc.identifier.issn	0378-4754	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/13692
dc.subject	neural networks	en
dc.subject	finite element models	en
dc.subject	turbocharger	en
dc.subject	condition monitoring	en
dc.subject	vibrations	en
dc.subject.classification	Computer Science, Interdisciplinary Applications	en
dc.subject.classification	Computer Science, Software Engineering	en
dc.subject.classification	Mathematics, Applied	en
dc.subject.other	IDENTIFICATION	en
dc.title	Neural networks and simple models for the fault diagnosis of naval turbochargers	en
heal.type	journalArticle	en
heal.identifier.primary	10.1016/S0378-4754(99)00131-7	en
heal.identifier.secondary	http://dx.doi.org/10.1016/S0378-4754(99)00131-7	en
heal.language	English	en
heal.publicationDate	2000	en
heal.abstract	The present work deals with the development of simple finite element (FE) models of a turbocharger (rotor, foundation and hydrodynamic bearings) combined with neural networks and identification methods and vibration data obtained from real machines towards the automatic fault diagnosis. The development of this system is based on four sequential steps: the first is the development of simple but realistic FE models based on dynamic simulations of the complete system. The second step is the monitoring of the real turbocharger. The third step is the accurate modelling of the foundations and the excitation from the main engine, which will be done using a robust optimisation method. In the fourth step all the possible faults of the machine are identified using the artificial neural networks (ANN). In this way we can take advantage of the ANN learning capability for the real time diagnosis of potential faults. The application of the proposed system to a real naval turbocharger with vibration data obtained on working conditions show some promising results. (C) 2000 IMACS/Published by Elsevier Science B.V. All rights reserved.	en
heal.publisher	ELSEVIER SCIENCE BV	en
heal.journalName	MATHEMATICS AND COMPUTERS IN SIMULATION	en
dc.identifier.doi	10.1016/S0378-4754(99)00131-7	en
dc.identifier.isi	ISI:000084223700019	en
dc.identifier.volume	51	en
dc.identifier.issue	3-4	en
dc.identifier.spage	387	en
dc.identifier.epage	397	en