Predictive Maintenance on Industrial Data Using Soft Voted Ensemble Classifiers

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dc.contributor.authorDilbaz, Ümit
dc.contributor.authorCingiz, Mustafa Özgür
dc.date.accessioned2026-02-12T21:02:49Z
dc.date.available2026-02-12T21:02:49Z
dc.date.issued2023
dc.departmentBursa Teknik Üniversitesi
dc.descriptionInternational Conference on Computing, Intelligence and Data Analytics, ICCIDA 2022 -- 2022-09-16 through 2022-09-17 -- Kocaeli -- 291929
dc.description.abstractThe IoT sector leads improvements in the Industry 4.0 revolution. Failure-prone machinery puts operations and production costs at risk. A sudden failure results in high downtime expenses and a drop in output. Predictive maintenance is a crucial area to research in order to increase industrial productivity. The purpose of the paper is to present the proper method for implementing predictive maintenance using the soft voting undersampling approach. We also highlight the impact of ensemble learning on an imbalanced dataset that is crucial problem for modelling the failure-prone machinery dataset. This paper suggests a method for enhancing predictive learning by selecting six different machine learning algorithms, including decision tree, random forest, Gradient Boosting Machines (GBM), XGBoost, Light GBM, and CatBoost classifiers. The soft voted model is proposed to enhance the performance of machine learning classifiers, which have produced better results in terms of the Fowlkes-Mallows Index (FMI) and Cohen's Kappa score. The results of our study are close to the results of predictive maintenance studies in the literature. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
dc.identifier.doi10.1007/978-3-031-27099-4_29
dc.identifier.endpage384
dc.identifier.isbn9789819652372
dc.identifier.isbn9783031931055
dc.identifier.isbn9789819662968
dc.identifier.isbn9783031999963
dc.identifier.isbn9783031950162
dc.identifier.isbn9783031947698
dc.identifier.isbn9783032004406
dc.identifier.isbn9783031910074
dc.identifier.isbn9783032083807
dc.identifier.isbn9783032077172
dc.identifier.issn2367-3370
dc.identifier.scopus2-s2.0-85151046204
dc.identifier.scopusqualityQ4
dc.identifier.startpage370
dc.identifier.urihttps://doi.org/10.1007/978-3-031-27099-4_29
dc.identifier.urihttps://hdl.handle.net/20.500.12885/6560
dc.identifier.volume643 LNNS
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherSpringer Science and Business Media Deutschland GmbH
dc.relation.ispartofLecture Notes in Networks and Systems
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanı
dc.snmzKA_Scopus_20260212
dc.subjectEnsemble learning
dc.subjectPredictive maintenance
dc.subjectUndersampling
dc.titlePredictive Maintenance on Industrial Data Using Soft Voted Ensemble Classifiers
dc.typeConference Object

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