An ordinal multi-dimensional classification (OMDC) for predictive maintenance

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dc.authoridYILDIRIM TASER, Pelin/0000-0002-5767-2700
dc.authorwosidYILDIRIM TASER, Pelin/O-6422-2019
dc.contributor.authorTaşer, Pelin Yıldırım
dc.date.accessioned2023-03-22T19:47:21Z
dc.date.available2023-03-22T19:47:21Z
dc.date.issued2023
dc.departmentBelirleneceken_US
dc.description.abstractPredictive Maintenance is a type of condition-based maintenance that assesses the equipment's states and estimates its failure probability and when maintenance should be performed. Although machine learning techniques have been frequently implemented in this area, the existing studies disregard to the natural order between the target attribute values of the historical sensor data. Thus, these methods cause losing the inherent order of the data that positively affects the prediction performances. To deal with this problem, a novel approach, named Ordinal Multi-dimensional Classification (OMDC), is proposed for estimating the conditions of a hydraulic system's four components by taking into the natural order of class values. To demonstrate the prediction ability of the proposed approach, eleven different multi-dimensional classification algorithms (traditional Binary Relevance (BR), Classifier Chain (CC), Bayesian Classifier Chain (BCC), Monte Carlo Classifier Chain (MCC), Probabilistic Classifier Chain (PCC), Classifier Dependency Network (CDN), Classifier Trellis (CT), Classifier Dependency Trellis (CDT), Label Powerset (LP), Pruned Sets (PS), and Random k-Labelsets (RAKEL)) were implemented using the Ordinal Class Classifier (OCC) algorithm. Besides, seven different classification algorithms (Multilayer Perceptron (MLP), Support Vector Machine (SVM), k-Nearest Neighbour (kNN), Decision Tree (C4.5), Bagging, Random Forest (RF), and Adaptive Boosting (AdaBoost)) were chosen as base learners for the OCC algorithm. The experimental results present that the proposed OMDC approach using binary relevance multi-dimensional classification methods predicts the conditions of a hydraulic system's multiple components with high accuracy. Also, it is clearly seen from the results that the OMDC models that utilize ensemble-based classification algorithms give more reliable prediction performances with an average Hamming score of 0.853 than the others that use traditional algorithms as base learners.en_US
dc.identifier.doi10.32604/csse.2023.028083
dc.identifier.endpage1516en_US
dc.identifier.issn0267-6192
dc.identifier.issue2en_US
dc.identifier.scopus2-s2.0-85133154950en_US
dc.identifier.scopusqualityQ3en_US
dc.identifier.startpage1499en_US
dc.identifier.urihttps://doi.org/10.32604/csse.2023.028083
dc.identifier.urihttps://hdl.handle.net/20.500.14034/640
dc.identifier.volume44en_US
dc.identifier.wosWOS:000818927200021en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherTech Science Pressen_US
dc.relation.journalComputer Systems Science And Engineeringen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectMachine learningen_US
dc.subjectmulti-dimensional classificationen_US
dc.subjectordinal classificationen_US
dc.subjectpredictive maintenanceen_US
dc.subjectNeural-Networken_US
dc.titleAn ordinal multi-dimensional classification (OMDC) for predictive maintenanceen_US
dc.typeArticleen_US

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