A hybrid machine learning model based on ensemble methods for devices fault prediction in the wood industry

Basit Öğe Kaydı
dc.authoridTavakkoli-Moghaddam, Reza/0000-0002-6757-926X
dc.authorwosidTavakkoli-Moghaddam, Reza/P-1948-2015
dc.contributor.authorDahesh, Arezoo
dc.contributor.authorTavakkoli-Moghaddam, Reza
dc.contributor.authorWassan, Niaz
dc.contributor.authorTajally, AmirReza
dc.contributor.authorDaneshi, Zahra
dc.contributor.authorErfani-Jazi, Aseman
dc.date.accessioned2024-05-19T14:41:17Z
dc.date.available2024-05-19T14:41:17Z
dc.date.issued2024
dc.departmentİstinye Üniversitesien_US
dc.description.abstractIn manufacturing industries, including the wood industry, devices, and equipment are considered the basic elements and the main capital for production. That is why managers are trying to maintain and use these devices and equipment optimally. On the other hand, repurchasing device parts or repairing equipment in case of major damage can cause more damage than planned costs. Therefore, a model that can determine the fault class based on the signs seen in the equipment would prevent major damage to the device and save on repair costs. In this regard, using the registered features for equipment and with the help of machine learning algorithms, a model can be created that can classify devices in the appropriate class based on their observed features. The present study uses nine machine learning algorithms to make this model, trains each model on three sets of selected features, and finally compares them. It is worth mentioning that after evaluating the models, based on the features selected from the embedded techniques, permutation feature importance methods, and genetic algorithm, the best models are considered as categorical boosting with the training and testing accuracy of 0.895 and 0.909, random forest with the training and testing accuracy of 0.905 and 0.893, and extreme gradient boosting with the training and testing accuracy of 0.884 and 0.885.en_US
dc.identifier.doi10.1016/j.eswa.2024.123820
dc.identifier.issn0957-4174
dc.identifier.issn1873-6793
dc.identifier.scopus2-s2.0-85188861052en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org10.1016/j.eswa.2024.123820
dc.identifier.urihttps://hdl.handle.net/20.500.12713/5089
dc.identifier.volume249en_US
dc.identifier.wosWOS:001215444400001en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherPergamon-Elsevier Science Ltden_US
dc.relation.ispartofExpert Systems With Applicationsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.snmz20240519_kaen_US
dc.subjectEnsemble Methoden_US
dc.subjectFault Predictionen_US
dc.subjectGenetic Algorithmen_US
dc.subjectMachine Learningen_US
dc.subjectPermutation Feature Importanceen_US
dc.titleA hybrid machine learning model based on ensemble methods for devices fault prediction in the wood industryen_US
dc.typeArticleen_US

Dosyalar

Koleksiyon

WoS İndeksli Yayınlar Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu