Probabilistic machine learning for detection of tightening torque in bolted joints

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MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.contributorBesancon-
Autor(es): dc.creatorMiguel, Luccas P-
Autor(es): dc.creatorTeloli, Rafael de O-
Autor(es): dc.creatorda Silva, Samuel-
Autor(es): dc.creatorChevallier, Gaël-
Data de aceite: dc.date.accessioned2025-08-21T15:28:11Z-
Data de disponibilização: dc.date.available2025-08-21T15:28:11Z-
Data de envio: dc.date.issued2022-04-29-
Data de envio: dc.date.issued2022-04-29-
Data de envio: dc.date.issued2021-12-31-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1177/14759217211054150-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/230554-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/230554-
Descrição: dc.descriptionObserving the loss of tightening torque using modal parameters is challenging due to the variability and nonlinear effects in bolted joints. Thus, this paper proposes a combined application of two probabilistic machine learning methods. First, a Gaussian mixture model (GMM) is learned using estimated natural frequencies, assuming the tightening torque in a safe situation. This probabilistic model can assuredly detect the lack of torque using indirect vibration measures in other unknown states by computing a damage index. A Gaussian process regression (GPR) is also learned considering a set of torque and damage index pairs in several conditions. The GPR model interpolates a curve to supply an estimative of the tightening torque for other conditions not used in this learning. An illustrative application is performed considering the Orion beam, an academic-scale specimen composed of a lap-joint configuration that retains the friction surface in contact patches. The structure is subjected to a random vibration with a controlled RMS level and several tightening torque conditions to identify the modal parameters. The probabilistic model learning via the GMM and GPR can detect adequately, with a low number of false diagnoses, the actual state of torque using an indirect measure of vibration, that is, without the need for a torque sensor on each bolt.-
Descrição: dc.descriptionDepartamento de Engenharia Mecânica Universidade Estadual Paulista Julio de Mesquita Filho Faculdade de Engenharia Campus de Ilha Solteira-
Descrição: dc.descriptionDépartement Mécanique Appliquée Université de Bourgogne Franche-Comté Besancon-
Descrição: dc.descriptionDepartamento de Engenharia Mecânica Universidade Estadual Paulista Julio de Mesquita Filho Faculdade de Engenharia Campus de Ilha Solteira-
Idioma: dc.languageen-
Relação: dc.relationStructural Health Monitoring-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectBolted joints-
Palavras-chave: dc.subjectGaussian Mixture Model-
Palavras-chave: dc.subjectGaussian Process Regression-
Palavras-chave: dc.subjectprobabilistic machine learning-
Palavras-chave: dc.subjecttightening torque-
Título: dc.titleProbabilistic machine learning for detection of tightening torque in bolted joints-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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