PPG.EGPSA/ITEGAM

URI permanente desta comunidadehttps://rigalileo.itegam.org.br/handle/123456789/1

A comunidade dispõe da produção técnica e científica do Programa de Pós-graduação em Engenharia, Gestão de Processos, Sistema e Ambiental (PPG.EGPSA) do Instituto de Tecnologia e Educação Galileo da Amazônia (ITEGAM), fruto da atividade de pesquisa e desenvolvimento (P&D). É possível acessar os trabalhos de conclusão do programa de pós-graduação, artigos e livros vinculados a pesquisa, desenvolvimento, inovação e extensão.

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Filtros

2019 - 201912020 - 20241

Configurações

Tem arquivos: SimAssunto: search.filters.subject.Fuzzy Logic

Resultados da Pesquisa

Agora exibindo 1 - 2 de 2
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    Fuzzy Logic-Based Compliance Assessment For Display Testing In Industrial Measurement Systems
    (Instituto de Tecnologia e Educação Galileo da Amazônia, 2024-10-01) SOUZA, Márcio André de Oliveira; NASCIMENTO, Manoel Henrique Reis; LEITE, Jandecy Cabral; BRITO JUNIOR, Jorge de Almeida; QUEIROZ JUNIOR, Fernando Cardoso de
    This study presents the development and implementation of a fuzzy logic-based system aimed at automating the compliance evaluation process for display testing in industrial measurement systems. The fuzzy inference system was programmed in Python, using linguistic variables and fuzzy rules to assess performance parameters like accuracy, response time, and error margins. Results showed that the system classified 93% of devices in accordance with established compliance standards, improving the reliability and efficiency of display testing in industrial environments.
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    Maintenance Management with Application of Computational Intelligence Generating a Decision Support System for the Load Dispatch in Power Plants
    (IntechOpen, 2019) LEITE, Jandecy Cabral; NASCIMENTO, Manoel Henrique Reis
    This chapter proposes the development of a computational tool to support load dispatch decisions based on the operational conditions of motors and generators in thermal power plants. The tool uses a fuzzy system to classify failure probabilities, based on indicators such as lubricating oil analysis, vibration analysis, and thermography of power generation equipment. The goal is not only to monitor the equipment's condition but also to take corrective actions to maintain service reliability and quality, considering the operating conditions of the equipment.