Fault diagnosis on bottle filling plant using genetic-based neural network

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dc.authorid0000-0001-9385-9305
dc.authorid0000-0002-8808-9518
dc.contributor.authorDemetgül, M.
dc.contributor.authorÜnal, M.
dc.contributor.authorTansel, I. N.
dc.contributor.authorYazıcıoğlu, Osman
dc.date.accessioned2024-10-12T19:42:54Z
dc.date.available2024-10-12T19:42:54Z
dc.date.issued2011
dc.departmentİstanbul Ticaret Üniversitesi, Mühendislik Fakültesi, Endüstri Mühendisliği Bölümüen_US
dc.description.abstractTimely detection of the pneumatic system problems is important in industry. Many techniques have been employed to solve this problem. In this paper, Genetic Algorithm (GA) based optimal configuration of neural networks is proposed for fault diagnostic of bottle filling systems. Back-propagation is used for neural networks algorithm. The back-propagation algorithm had six inputs and one output. A fitness function was designed to the minimize execution time of ANN model by keeping the number of hidden layer(s) and nodes as low as possible while the mean square error of estimated output error is minimized. The designed GA-ANN combination and the graphical user interface (GUI) eliminate the trial and error process for selection of the fastest and most accurate configuration. The performance of the proposed system was evaluated by using experimental data collected at a pneumatic work cell which attach caps to the bottles. The sensory data was collected at normal operating conditions and a series of faults were imposed to the system such as missing bottle, attaching nonworking bottle caps at two different cylinders, two air pressure problems (insufficient and low air), and not filling water. The study demonstrated the convenience, accuracy and speed of the proposed GA-NN environment. It may also be used for training for selection of ANN configurations at various applications. (C) 2011 Elsevier Ltd. All rights reserved.en_US
dc.identifier.citationDemetgül, M., Ünal, M., Tansel, I. N., & Yazıcıoğlu, O. (2011). Fault diagnosis on bottle filling plant using genetic-based neural network. Advances in Engineering Software, 42(12), 1051-1058.
dc.identifier.doi10.1016/j.advengsoft.2011.07.004
dc.identifier.endpage1058en_US
dc.identifier.issn0965-9978
dc.identifier.issn1873-5339
dc.identifier.issue12en_US
dc.identifier.startpage1051en_US
dc.identifier.urihttps://doi.org/10.1016/j.advengsoft.2011.07.004
dc.identifier.urihttps://hdl.handle.net/11467/8653
dc.identifier.volume42en_US
dc.identifier.wosWOS:000296309100005en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.language.isoenen_US
dc.publisherElsevier Sci Ltden_US
dc.relation.ispartofAdvances In Engineering Softwareen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectNeural networken_US
dc.subjectGenetic Algorithmen_US
dc.subjectBottle Filling Planten_US
dc.subjectPneumaticen_US
dc.subjectFault Diagnosisen_US
dc.subjectBack-Propagation Algorithmen_US
dc.titleFault diagnosis on bottle filling plant using genetic-based neural networken_US
dc.typeArticleen_US

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