Machine Failure Analysis Using Multinomial Logistic Regression

dc.contributor.authorKoçak, Aydın
dc.date.accessioned2023-01-12T20:37:50Z
dc.date.available2023-01-12T20:37:50Z
dc.date.issued2022
dc.departmentN/A/Departmenten_US
dc.description.abstractPurpose: The main purpose of this study is to carry out a failure analysis of a filling machine by applying multinomial logistic regression. Design/methodology/approach: For this purpose, data related to failure mode, product, scrap rate, and shift parameters were collected from the machine and analysis was conducted by establishing two multinomial logistic regression models. Findings: Statistical results suggest that a hydraulic failure must be expected while filling the mix product. Besides, it is highly probable that a final folder failure will occur while filling the cherry product. Paper failure stands out while filling the apple product compared to other products. In addition, it is likely that a final folder failure will occur while filling this product. Photocell failure is common while filling the peach product. Results of the study show that the odd for low-level scrap is high when there is any failure in the machine. Discussion: A more effective analysis can be performed by collecting parameters that may affect the position of machinery such as vibration, humidity, temperature and pressure through the sensors to be installed on various units of the filling machine and adding them into the models developed under the study.en_US
dc.identifier.endpage1445en_US
dc.identifier.issn1309-0712
dc.identifier.issue2en_US
dc.identifier.startpage1428en_US
dc.identifier.trdizinid533211en_US
dc.identifier.urihttps://search.trdizin.gov.tr/yayin/detay/533211
dc.identifier.urihttps://hdl.handle.net/11454/81780
dc.identifier.volume14en_US
dc.indekslendigikaynakTR-Dizinen_US
dc.language.isoenen_US
dc.relation.ispartofİşletme Araştırmaları Dergisien_US
dc.relation.publicationcategoryMakale - Ulusal Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.titleMachine Failure Analysis Using Multinomial Logistic Regressionen_US
dc.typeArticleen_US

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