Fault diagnosis via local neural networks

Skoundrianos, EN; Tzafestas, SG

dc.contributor.author	Skoundrianos, EN	en
dc.contributor.author	Tzafestas, SG	en
dc.date.accessioned	2014-03-01T01:17:55Z
dc.date.available	2014-03-01T01:17:55Z
dc.date.issued	2002	en
dc.identifier.issn	0378-4754	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/14699
dc.subject	Fault diagnosis	en
dc.subject	Local neural networks	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	Algorithms	en
dc.subject.other	Computer simulation	en
dc.subject.other	Fuzzy sets	en
dc.subject.other	Problem solving	en
dc.subject.other	Fault diagnosis	en
dc.subject.other	Neural networks	en
dc.title	Fault diagnosis via local neural networks	en
heal.type	journalArticle	en
heal.identifier.primary	10.1016/S0378-4754(02)00012-5	en
heal.identifier.secondary	http://dx.doi.org/10.1016/S0378-4754(02)00012-5	en
heal.language	English	en
heal.publicationDate	2002	en
heal.abstract	This article deals with the fault diagnosis problem of plants with unknown description. The problem is approached via a modeling technique which is based on the local model network (LMN) structure. Local models (LMs) perform local linearization and their structure is quite similar to the Takagi-Sugeno fuzzy models. Local neural models (LNMs) function as linear estimators, giving a satisfying estimation for the plant's output within parts of the operating regime. Their training is performed off-line, which ensures a reliable method with false alarm avoidance. This is critical since false alarms may cause a production line to pause. Plant modeling is followed by an on-line comparison between plant and model behavior. The model is created on a healthy plant, so any mismatch leads to suspicions concerning the presence of faults. The comparison leads to the creation of residuals. Residuals are signals that trigger a decision mechanism to conclude for presence, size and cause of possible faults. Change detection algorithms are used at this point, to avoid misinterpretation of plant model mismatches not caused by faults. The method is tested via simulation on the three-tank system which is a well-known benchmark. (C) 2002 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(02)00012-5	en
dc.identifier.isi	ISI:000178952300004	en
dc.identifier.volume	60	en
dc.identifier.issue	3-5	en
dc.identifier.spage	169	en
dc.identifier.epage	180	en