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dc.contributor.advisor1Aguiar, Eduardo Pestana de-
dc.contributor.advisor1Latteshttp://lattes.cnpq.br/9530065975903052pt_BR
dc.contributor.referee1Fonseca, Leonardo Goliatt da-
dc.contributor.referee1Latteshttp://lattes.cnpq.br/9030707448549156pt_BR
dc.contributor.referee2Zuben, Fernando José Von-
dc.contributor.referee2Latteshttp://lattes.cnpq.br/1756895777404187pt_BR
dc.creatorFonseca, Lara Dutra-
dc.creator.Latteshttp://lattes.cnpq.br/0004406647831452pt_BR
dc.date.accessioned2024-07-23T17:18:59Z-
dc.date.available2024-07-22-
dc.date.available2024-07-23T17:18:59Z-
dc.date.issued2024-04-08-
dc.identifier.urihttps://repositorio.ufjf.br/jspui/handle/ufjf/16957-
dc.description.abstractHot box and hot wheel problems on trains are significant threats in any rail operation because they increase fatigue and wear processes, resulting in failures, costly train stoppages and even derailments. For this reason, there are many hot box and hot wheel detectors to monitor those components distributed along the railway, and when they detect overheated components, a warning is given to alert to the impending failure. However, some situations such as solar incidence, misalignment, or sensor defect can lead to improper warnings, that is, warnings that actually do not exhibit the problem. Therefore, this work develops a new model for the classification of proper and improper warnings in hot box and hot wheel problems, by the analysis of the temperatures measured in the wheels and bearings of the train when the warning was given. To this end, the discussion focuses on the use of a metaheuristic optimization algorithm into the training phase of an upper and lower singleton type-2 fuzzy logic system, in order to increase the convergence speed and the accuracy of this procedure. Thus, the performance of the new model will be based on the real data set composed of train wheel and bearing temperatures, provided by a Brazilian railway transportation company, covering several situations of proper and improper warnings. Additionally, this work presents performance analyses based on well-known data sets provided by Knowledge Extraction based on Evolutionary Learning Repository, aiming to evaluate the proposal. For comparison purposes, the results of the proposed classifier will be compared to well-know deep-learning classifiers as well as the original fuzzy model it is based on. The performance analysis of the new model is discussed in terms of classification ratio and convergence speed. The reported results show that the proposal achieved higher classification ratio and proved to be an appropriate option for the presented problem.pt_BR
dc.description.resumo-pt_BR
dc.languageengpt_BR
dc.publisherUniversidade Federal de Juiz de Fora (UFJF)pt_BR
dc.publisher.countryBrasilpt_BR
dc.publisher.departmentICE – Instituto de Ciências Exataspt_BR
dc.publisher.programPrograma de Pós-graduação em Modelagem Computacionalpt_BR
dc.publisher.initialsUFJFpt_BR
dc.rightsAcesso Abertopt_BR
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 Brazil*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/br/*
dc.subjectType-2 fuzzy logic systemspt_BR
dc.subjectMeta-heuristic optimizationpt_BR
dc.subjectSwarm intelligencept_BR
dc.subjectClassificationpt_BR
dc.subject.cnpqCNPQ::CIENCIAS EXATAS E DA TERRApt_BR
dc.titleSwarm intelligence algorithm combined with type-2 fuzzy logic system for the classification of trends in hot boxes and hot wheelspt_BR
dc.typeDissertaçãopt_BR
Appears in Collections:Mestrado em Modelagem Computacional (Dissertações)



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