Mestrado em Modelagem e Otimização - PPGMO

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https://repositorio.ufcat.edu.br/handle/tede/40
O Mestrado em Modelagem e Otimização tem um papel essencial no desenvolvimento de novas tecnologias. Atualmente, se faz muito intensamente o uso de modelos matemáticos, simulações avançadas e sofisticados desenvolvimentos computacionais na pesquisa científica em geral.

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Navegando Mestrado em Modelagem e Otimização - PPGMO por Assunto "Aprendizado profundo"

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    Técnicas de aprendizado profundo aplicadas ao monitoramento de integridade estrutural por impedância eletromecânica
    (Universidade Federal de Goiás, 2021-07-12) Rezende, Stanley Washington Ferreira de; Moura Júnior, José dos Reis Vieira de; https://orcid.org/0000-0002-8701-8355; http://lattes.cnpq.br/2479225716217997; Moura Júnior, José dos Reis Vieira de; Rabelo, Marcos Napoleão; Lima, William Júnio de
    In this work, a study of structural health monitoring (SHM) techniques is carried out, with a primary focus on the application of the eletromechanical impedance-based SHM method to characterize structural failures in mechanical systems. This methodology generally combines modern sensors with intelligent algorithms to identify the presence of damage, observing changes in the monitored signals and correlating them with physical phenomena. However, when these variations are sufficiently complex, it becomes necessary to apply more sophisticated techniques, capable of abstracting information pertinent to the condition of structural health. In this sense, innovative machine learning tools (especially Artificial Neural Networks - RNA) have been gaining prominence, being extensively investigated to avoid errors in structural prognosis and focusing mainly on vibration analysis. Therefore, the present work aims to contribute to the SHM area, providing an improvement of the electromechanical impedance technique in terms of structural health analysis, associating it with two neural architectures (CNN and LSTM) to facilitate the decision-making process. Thus, this contribution presents a bibliographical review of some of the main concepts associated with this theme, such as intelligent materials, evaluation methods, the electromechanical impedance-based method itself, the concepts associated with machine learning techniques and the artificial neural networks used. Then, four experimental tests were investigated, employing a combination of the impedance-based method with the respective CNN and LSTM architectures for the identification and/or prediction of structural failures. Thus, it was possible to validate the proposed methodologies, verifying the sensitivity of the networks to some environmental influences (such as temperature and humidity conditions) and operational (operating speed and unbalanced) influences of the mechanical systems under study. As a result, both proposed methodologies are efficient in terms of the structural health monitoring, allowing an optimization of the structural diagnosis.