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Development of a machine learning model to predict construction machinery gearboxes’ health status

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dc.contributor.advisor1Silva, Higor Luis
dc.contributor.advisor1Latteshttp://lattes.cnpq.br/2478587933474876pt_BR
dc.contributor.referee1Cavallini Junior, Aldemir Aparecido
dc.contributor.referee1Latteshttp://lattes.cnpq.br/0387727577180664pt_BR
dc.contributor.referee2Monteiro, Nuno Alvares De Paula
dc.contributor.referee2Latteshttp://lattes.cnpq.br/8760622283790204pt_BR
dc.creatorAlmeida, Camila Aparecida Da Silva
dc.creator.Latteshttp://lattes.cnpq.br/7074001417959534pt_BR
dc.date.accessioned2024-07-31T18:26:20Z
dc.date.available2024-07-31T18:26:20Z
dc.date.issued2024-03-22
dc.description.degreenameTrabalho de Conclusão de Curso (Graduação)pt_BR
dc.description.resumoAs industries increasingly turn to data-driven methods in their maintenance strategies, the need for advanced predictive maintenance techniques becomes increasingly evident. This study focuses on the application of Machine Learning (ML) algorithms to enhance the reliability and efficiency of vehicle transmission systems, with a focus on monitoring the health of powershift transmissions for construction machinery, at ZF Friedrichshafen, a global automotive technology company. By utilizing ML algorithms, this project aims to identify patterns in operational data, facilitating the prediction of potential failures and enabling timely interventions. The goal of the study was to compare the ML ML algorithms, Random Forest, K Nearest Neighbors (KNN), and Support Vector Machines (SVM) to identify the most effective in predicting transmission failures. The research involved stages ranging from data collection and pre-processing to model optimization and evaluation, using machine learning metrics. The results revealed that the Random Forest algorithm outperformed the others in terms of precision, recall, and F1 score for the studied use case. Its robust ability to accurately classify positive and negative instances was particularly significant in the analysis of vehicle operational data.pt_BR
dc.identifier.citationALMEIDA, Camila Aparecida. Development of a machine learning model to predict construction machinery gearboxes’ health status. 2024. 66 f. Trabalho de Conclusão de Curso (Graduação em Engenharia Aeronáutica) – Universidade Federal de Uberlândia, Uberlândia, 2024.pt_BR
dc.identifier.urihttps://repositorio.ufu.br/handle/123456789/41976
dc.languageengpt_BR
dc.orcid.putcode164715740
dc.publisherUniversidade Federal de Uberlândiapt_BR
dc.publisher.countryBrasilpt_BR
dc.publisher.courseEngenharia Aeronáuticapt_BR
dc.rightsAcesso Abertopt_BR
dc.sizeorduration66pt_BR
dc.subjectMachine learning algorithmspt_BR
dc.subjectVehicle transmission systemspt_BR
dc.subjectPredictive maintenancept_BR
dc.subjectOperational data analysispt_BR
dc.subjectAlgoritmos de aprendizado de máquinapt_BR
dc.subjectSistemas de transmissão de veículospt_BR
dc.subjectManutenção preditivapt_BR
dc.subjectAnálise de dados operacionaispt_BR
dc.subject.cnpqCNPQ::ENGENHARIASpt_BR
dc.titleDevelopment of a machine learning model to predict construction machinery gearboxes’ health statuspt_BR
dc.title.alternativeDesenvolvimento de um modelo de aprendizado de máquina para prever o estado de saúde das transmissões de máquinas de construçãopt_BR
dc.typeTrabalho de Conclusão de Cursopt_BR
dspace.entity.typePublication

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TCC - Engenharia Aeronáutica