Radial basis and LVQ neural network algorithm for real-time fault diagnosis of bottle filling plant

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dc.authorid0000-0001-9385-9305
dc.authorid0000-0002-0476-4396
dc.authorid0000-0002-4098-7220
dc.contributor.authorDemetgül, Mustafa
dc.contributor.authorYazıcıoğlu, Osman
dc.contributor.authorKentli, Aykut
dc.date.accessioned2024-10-12T19:43:08Z
dc.date.available2024-10-12T19:43:08Z
dc.date.issued2014
dc.departmentİstanbul Ticaret Üniversitesi, Mühendislik Fakültesi, Endüstri Mühendisliği (İngilizce Destekli) Bölümü en_US
dc.description.abstractIn this study, an Artificial Neural Network (ANN) is developed to find faults rapidly on a pneumatic system. The data were saved and evaluated considering system is working perfectly and faults are predetermined. These faults include having no bottle, a nonworking cap closing cylinder B, a nonworking bottle cap closing cylinder C, insufficient air pressure, water not filling and low air pressure faults. The signals of six sensors were collected during the entire sequence and the 18 most descriptive features of the data were encoded to present to the ANNs. Two different ANNs were applied for interpretation of the encoded signals. The ANNs tested in the study were learning vector quantization (LVQ) and radial basis network (RBN). The performance of LVQ and RBN was found to be fine with the presented procedures for a system having very repetitive sequential data. en_US
dc.description.abstractU ovom je radu razvijena umjetna neuronska mreža (ANN) za brzo pronalaženje grešaka na pneumatskom sustavu. Podaci su prikupljeni i procijenjeni smatrajući da sustav radi savršeno, a greške su unaprijed predviđene. Greške uključuju manjak boce, ne funkcioniranje cilindra B za stavljanje poklopca, neispravni cilindar C za stavljanje poklopca na boce, nedovoljan tlak zraka, voda se ne puni i nizak tlak zraka. Tijekom postupka prikupljeni su signali šest senzora te je za ANN kodirano 18 najkarakterističnijih obilježja podataka. Primijenjene su dvije različite umjetne neuronske mreže (ANN) za interpretaciju kodiranih signala. Umjetne neuronske mreže testirane u ispitivanju bile su "learning vector quantization (LVQ)" i "radial basis network (RBN)". Ustanovilo se da te dvije vrste umjetnih neuronskih mreža dobro funkcioniraju u primijenjenim postupcima u sustavu u kojem se sekvencijski podaci ponavljaju.
dc.identifier.endpage695en_US
dc.identifier.issn1330-3651
dc.identifier.issn1848-6339
dc.identifier.issue4en_US
dc.identifier.startpage689en_US
dc.identifier.urihttps://hdl.handle.net/11467/8777
dc.identifier.volume21en_US
dc.identifier.wosWOS:000341262900001en_US
dc.identifier.wosqualityQ3en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.language.isoenen_US
dc.publisherUniv Osijek, Tech Facen_US
dc.relation.ispartofTehnicki Vjesnik-Technical Gazetteen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subject Artificial Neural Networken_US
dc.subjectBottle Filling Planten_US
dc.subjectFault Diagnosisen_US
dc.subjectPneumaticen_US
dc.subjectDijagnoza Greške
dc.subjectPneumatski
dc.subjectPogon Za Punjenje Baca
dc.subjectUmjetna Neuronska Mreža
dc.titleRadial basis and LVQ neural network algorithm for real-time fault diagnosis of bottle filling planten_US
dc.title.alternativeAlgoritam radijalne osnove i LVQ algoritam neuronske mreže za pravovremenu dijagnozu greške pogona za punjenje boca
dc.typeArticleen_US

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