Fault diagnosis on material handling system using feature selection and data mining techniques

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dc.contributor.authorDemetgül, Mustafa
dc.contributor.authorYıldız, Kazım
dc.contributor.authorTaşkın, S.
dc.contributor.authorTansel, I.N.
dc.contributor.authorYazıcıoğlu, Osman
dc.date.accessioned2020-11-21T15:53:24Z
dc.date.available2020-11-21T15:53:24Z
dc.date.issued2014en_US
dc.departmentİstanbul Ticaret Üniversitesien_US
dc.description.abstractThe material handling systems are one of the key components of the most modern manufacturing systems. The sensory signals of material handling systems are nonlinear and have unique characteristics. It is very difficult to encode and classify these signals by using multipurpose methods. In this study, performances of multiple generic methods were studied for the diagnostic of the pneumatic systems of the material handling systems. Diffusion Map (DM), Local Linear Embedding (LLE) and AutoEncoder (AE) algorithms were used for future extraction. Encoded signals were classified by using the Gustafson-Kessel (GK) and k-medoids algorithms. The accuracy of the estimations was better than 90% when the LLE was used with GK algorithm. © 2014 Elsevier Ltd. All rights reserved.en_US
dc.identifier.doi10.1016/j.measurement.2014.04.037en_US
dc.identifier.endpage24en_US
dc.identifier.issn0263-2241
dc.identifier.scopus2-s2.0-84900991723en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage15en_US
dc.identifier.urihttps://doi.org/10.1016/j.measurement.2014.04.037
dc.identifier.urihttps://hdl.handle.net/11467/3573
dc.identifier.volume55en_US
dc.identifier.wosWOS:000339814500002en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherElsevier B.V.en_US
dc.relation.ispartofMeasurement: Journal of the International Measurement Confederationen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectData miningen_US
dc.subjectDimension reductionen_US
dc.subjectFault diagnosisen_US
dc.subjectFeature selectionen_US
dc.subjectGustafson-Kesselen_US
dc.subjectk-Medoidsen_US
dc.subjectMaterial handling systemen_US
dc.subjectServo-pneumaticen_US
dc.titleFault diagnosis on material handling system using feature selection and data mining techniquesen_US
dc.typeArticleen_US

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