Application of Data Association and Perceptron Artificial Neural Networks (AR-ANN) in Fault Detection in Dynamic Systems: Gears

Registro completo de metadados
MetadadosDescriçãoIdioma
Autor(es): dc.contributorAraçatuba College of Technology-
Autor(es): dc.contributorLins College of Technology-
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.creatorTebon, Paulo Roberto-
Autor(es): dc.creatorOuta, Roberto-
Autor(es): dc.creatorChavarette, Fábio Roberto-
Autor(es): dc.creatorGonçalves, Aparecido Carlos-
Autor(es): dc.creatorda Silva Pinto, Sandro-
Autor(es): dc.creatorStabile, Samuel-
Data de aceite: dc.date.accessioned2025-08-21T22:26:10Z-
Data de disponibilização: dc.date.available2025-08-21T22:26:10Z-
Data de envio: dc.date.issued2025-04-29-
Data de envio: dc.date.issued2022-12-31-
Fonte completa do material: dc.identifierhttps://hdl.handle.net/11449/302914-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/302914-
Descrição: dc.descriptionThis work demonstrates a study of identification, classification and grouping of different signals, whose objective is the detection of failures between a pair of gears. Therefore, it is a multidisciplinary work, as it promotes an application of low-cost embedded systems and methodologies of computer science in the area of mechanical engineering. For this to be done, the concept of perceptron artificial neural networks (ANN) associated with the data association rules (AR) theorem belonging to the concept of data-mining was used. This association was developed because it is easy to access and has great potential in identification and classification. We named these different theorems AR-ANN. The result of the application of AR-ANN to the reference and faulty signs was successful, whose classification demonstrated a high rate of correct and in the training phase of the perceptron network, the balance of the adjustment line was obtained, demonstrated by linear regression and weights (variables).-
Descrição: dc.descriptionConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)-
Descrição: dc.descriptionAraçatuba College of Technology, Av. Prestes Maia, 1764, Ipanema, SP-
Descrição: dc.descriptionLins College of Technology, Estrada Mário Covas Junior, km 1 - Vila Guararapes, SP-
Descrição: dc.descriptionUNESP - Paulista State University Department of Engineering Physics and Mathematics of the Institute of Chemistry, Av. Prof. Francisco Degni, 55 - Jardim Quitandinha, SP-
Descrição: dc.descriptionUNESP - Paulista State University Faculty of Engineering of Ilha Solteira Department of Mechanical Engineering, Avenida Brasil, 56 - Centro, SP-
Descrição: dc.descriptionUNESP - Paulista State University Department of Engineering Physics and Mathematics of the Institute of Chemistry, Av. Prof. Francisco Degni, 55 - Jardim Quitandinha, SP-
Descrição: dc.descriptionUNESP - Paulista State University Faculty of Engineering of Ilha Solteira Department of Mechanical Engineering, Avenida Brasil, 56 - Centro, SP-
Descrição: dc.descriptionCNPq: 301401/2022-5-
Formato: dc.format531-540-
Idioma: dc.languageen-
Relação: dc.relationInternational Journal of Computer Information Systems and Industrial Management Applications-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectArtificial Neural Network-ANN-
Palavras-chave: dc.subjectAssociation Rules-AR-
Palavras-chave: dc.subjectBioengineering-
Palavras-chave: dc.subjectData-Mining-
Palavras-chave: dc.subjectFault Detection-
Palavras-chave: dc.subjectVibration-
Título: dc.titleApplication of Data Association and Perceptron Artificial Neural Networks (AR-ANN) in Fault Detection in Dynamic Systems: Gears-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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