Artificial neural networks in the fault diagnosis of technological systems: a case study in chemical engineering process

Tzafestas, SG; Dalianis, PJ

dc.contributor.author	Tzafestas, SG	en
dc.contributor.author	Dalianis, PJ	en
dc.date.accessioned	2014-03-01T01:44:39Z
dc.date.available	2014-03-01T01:44:39Z
dc.date.issued	1996	en
dc.identifier.issn	10631100	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/24442
dc.relation.uri	http://www.scopus.com/inward/record.url?eid=2-s2.0-0030407772&partnerID=40&md5=ea8223319917ab5d804b861480879b62	en
dc.subject.other	Adaptive systems	en
dc.subject.other	Backpropagation	en
dc.subject.other	Cellulose	en
dc.subject.other	Chemical engineering	en
dc.subject.other	Computer simulation	en
dc.subject.other	Expert systems	en
dc.subject.other	Failure analysis	en
dc.subject.other	Mathematical models	en
dc.subject.other	Online systems	en
dc.subject.other	Alkali cellulose preparation process	en
dc.subject.other	Fault diagnosis	en
dc.subject.other	Neural networks	en
dc.title	Artificial neural networks in the fault diagnosis of technological systems: a case study in chemical engineering process	en
heal.type	journalArticle	en
heal.publicationDate	1996	en
heal.abstract	The last decade, a lot of attention is given to adaptive methods based on artificial neural networks, which can significantly improve the symptom interpretation and system performance in case of malfunctioning. Such methods are especially considered in cases where no explicit algorithms or models for the problem under investigation exist. In such problems, automatic interpretation of faulty symptoms with the use of artificial neural network classifiers is recommended. Two different models of artificial neural networks, the extended back-propagation and the radial basis function, integrated in a hybrid system, where an expert mechanism supervises them, are discussed and applied with appropriate simulations to the diagnosis of a subprocess of the alkali-cellulose preparation process.	en
heal.publisher	Gordon & Breach Science Publ Inc, Newark, NJ, United States	en
heal.journalName	Engineering Simulation	en
dc.identifier.volume	13	en
dc.identifier.issue	6	en
dc.identifier.spage	939	en
dc.identifier.epage	954	en