Utilização de redes neurais artificiais para detecção e diagnóstico de falhas

• Ano de defesa: 2011
• Autor(a) principal: Rebouças, Diogo Leite
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal do Rio Grande do Norte; BR; UFRN; Programa de Pós-Graduação em Engenharia Elétrica; Automação e Sistemas; Engenharia de Computação; Telecomunicações
• Palavras-chave em Português: Sistemas Críticos; Detecção de Falhas; Diagnóstico de Falhas; Redes Neurais Artificiais; Critical Systems; Fault Detection; Fault Diagnosis; Artificial Neural Network; CNPQ::ENGENHARIAS::ENGENHARIA ELETRICA
• Link de acesso: https://repositorio.ufrn.br/jspui/handle/123456789/15356

In a real process, all used resources, whether physical or developed in software, are subject to interruptions or operational commitments. However, in situations in which operate critical systems, any kind of problem may bring big consequences. Knowing this, this paper aims to develop a system capable to detect the presence and indicate the types of failures that may occur in a process. For implementing and testing the proposed methodology, a coupled tank system was used as a study model case. The system should be developed to generate a set of signals that notify the process operator and that may be post-processed, enabling changes in control strategy or control parameters. Due to the damage risks involved with sensors, actuators and amplifiers of the real plant, the data set of the faults will be computationally generated and the results collected from numerical simulations of the process model. The system will be composed by structures with Artificial Neural Networks, trained in offline mode using Matlab®