An Efficient Approach for Automatic Fault Classification Based on Data Balance and One-Dimensional Deep Learning

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dc.authoridALTUN, Yusuf/0000-0002-2099-0959en_US
dc.authorscopusid56780370400en_US
dc.authorscopusid25031391400en_US
dc.authorscopusid55293271600en_US
dc.authorwosidALTUN, Yusuf/AAA-9929-2020en_US
dc.contributor.authorIleri, Ugur
dc.contributor.authorAltun, Yusuf
dc.contributor.authorNarin, Ali
dc.date.accessioned2024-08-23T16:03:37Z
dc.date.available2024-08-23T16:03:37Z
dc.date.issued2024en_US
dc.departmentDüzce Üniversitesien_US
dc.description.abstractPredictive maintenance (PdM) is implemented to efficiently manage maintenance schedules of machinery and equipment in manufacturing by predicting potential faults with advanced technologies such as sensors, data analysis, and machine learning algorithms. This paper introduces a study of different methodologies for automatically classifying the failures in PdM data. We first present the performance evaluation of fault classification performed by shallow machine learning (SML) methods such as Decision Trees, Support Vector Machines, k-Nearest Neighbors, and one-dimensional deep learning (DL) techniques like 1D-LeNet, 1D-AlexNet, and 1D-VGG16. Then, we apply normalization, which is a scaling technique in which features are shifted and rescaled in the dataset. We reapply classification algorithms to the normalized dataset and present the performance tables in comparison with the first results we obtained. Moreover, in contrast to existing studies in the literature, we generate balanced dataset groups by randomly selecting normal data and all faulty data for all fault types from the original dataset. The dataset groups are generated with 100 different repetitions, recording performance scores for each one and presenting the maximum scores. All methods utilized in the study are similarly employed on these groups. From these scores, the use of 1D-LeNet deep learning classifiers and feature normalization resulted in achieving the highest overall accuracy and F1-score performance of 98.50% and 98.32%, respectively. As a result, the goal of this study was to develop an efficient approach for automatic fault classification, leveraging data balance, and additionally, to provide an analysis of one-dimensional deep learning and shallow machine learning-based classification methods. In light of the experimentation and comparative analysis, this study successfully achieves its stated goal by demonstrating that one-dimensional deep learning and data balance collectively emerge as the optimal approach, offering good prediction accuracy.en_US
dc.identifier.doi10.3390/app14114899
dc.identifier.issn2076-3417
dc.identifier.issue11en_US
dc.identifier.scopus2-s2.0-85195962715en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.urihttps://doi.org/10.3390/app14114899
dc.identifier.urihttps://hdl.handle.net/20.500.12684/13829
dc.identifier.volume14en_US
dc.identifier.wosWOS:001245511100001en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherMdpien_US
dc.relation.ispartofApplied Sciences-Baselen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectpredictive maintenance (PdM)en_US
dc.subjectfault classificationen_US
dc.subjectdata balanceen_US
dc.subjectshallow machine learningen_US
dc.subjectone-dimensional deep learningen_US
dc.subjectbalanced dataseten_US
dc.subjectimbalanced dataseten_US
dc.subjectfeature normalizationen_US
dc.subjectNeural-Networken_US
dc.subjectMaintenanceen_US
dc.subjectDiagnosisen_US
dc.subjectSelectionen_US
dc.titleAn Efficient Approach for Automatic Fault Classification Based on Data Balance and One-Dimensional Deep Learningen_US
dc.typeArticleen_US

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