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dc.contributor.authorSepulevene, Luis
dc.contributor.authorDrummond, Isabela
dc.contributor.authorKuehne, Bruno Tardiole
dc.contributor.authorFrinhani, Rafael
dc.contributor.authorFilho, Dionisio Leite
dc.contributor.authorPeixoto, Maycon
dc.contributor.authorReiff-Marganiec, Stephan
dc.contributor.authorBatista, Bruno
dc.date.accessioned2021-05-17T11:24:49Z
dc.date.available2021-05-17T11:24:49Z
dc.date.issued2021-05-14
dc.identifier.citationSepulevene, L., Drummond, I., Kuehne, B., Tardiole, F., Rafael F., Dionisio, L., Peixoto, M., Reiff-Marganiec, S., and Batista, B. (2021). 'Performance evaluation of machine learning techniques for fault diagnosis in vehicle fleet tracking modules'. The Computer Journal, pp. 1-13.en_US
dc.identifier.doi10.1093/comjnl/bxab047
dc.identifier.urihttp://hdl.handle.net/10545/625766
dc.description.abstractWith industry 4.0, data-based approaches are in vogue. However, extracting the essential features is not a trivial task and greatly influences the fi nal result. There is also a need for specialized system knowledge to monitor the environment and diagnose faults. In this context, the diagnosis of faults is signi cant, for example, in a vehicle fleet monitoring system, since it is possible to diagnose faults even before the customer is aware of the fault, minimizing the maintenance costs of the modules. In this paper, several models using Machine Learning (ML) techniques were applied and analyzed during the fault diagnosis process in vehicle fleet tracking modules. Two approaches were proposed, "With Knowledge" and "Without Knowledge", to explore the dataset using ML techniques to generate classi fiers that can assist in the fault diagnosis process. The approach "With Knowledge" performs the feature extraction manually, using the ML techniques: Random Forest, Naive Bayes, Support Vector Machine (SVM) and Multi Layer Perceptron (MLP); on the other hand, the approach "Without Knowledge" performs an automatic feature extraction, through a Convolutional Neural Network (CNN). The results showed that the proposed approaches are promising. The best models with manual feature extraction obtained a precision of 99.76% and 99.68% for detection and detection and isolation of faults, respectively, in the provided dataset. The best models performing an automatic feature extraction obtained respectively 88.43% and 54.98% for detection and detection and isolation of failures.en_US
dc.description.sponsorshipN/Aen_US
dc.language.isoenen_US
dc.publisherOxford University Pressen_US
dc.relation.urlhttps://academic.oup.com/comjnl/advance-article-abstract/doi/10.1093/comjnl/bxab047/6275474?redirectedFrom=fulltexten_US
dc.subjectMachine Learningen_US
dc.subjectFault Diagnosisen_US
dc.subjectFeature Extractionen_US
dc.subjectConvolutional Neural Networksen_US
dc.titlePerformance evaluation of machine learning techniques for fault diagnosis in vehicle fleet tracking modulesen_US
dc.typeArticleen_US
dc.identifier.eissn1460-2067
dc.contributor.departmentFederal University of Itajubá, Itajubá, Brazilen_US
dc.contributor.departmentFederal University of Mato Grosso do Sul, Ponta Porã, Brazilen_US
dc.contributor.departmentFederal University of Bahia, Salvador, Brazilen_US
dc.contributor.departmentUniversity of Derbyen_US
dc.identifier.journalThe Computer Journalen_US
dcterms.dateAccepted2021-03-31
dc.author.detail787028en_US


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