Detecção e isolamento de falhas em sistemas dinâmicos baseados em redes neurais

AUTOR(ES)
DATA DE PUBLICAÇÃO

2007

RESUMO

This master dissertation presents the development of a fault detection and isolation system based in neural network. The system is composed of two parts: an identification subsystem and a classification subsystem. Both of the subsystems use neural network techniques with multilayer perceptron training algorithm. Two approaches for identifica-tion stage were analyzed. The fault classifier uses only residue signals from the identification subsystem. To validate the proposal we have done simulation and real experiments in a level system with two water reservoirs. Several faults were generated above this plant and the proposed fault detection system presented very acceptable behavior. In the end of this work we highlight the main difficulties found in real tests that do not exist when it works only with simulation environments

ASSUNTO(S)

detecção e isolamento de falhas resíduos  diagnóstico de falhas redes neurais residual fault detection and isolation engenharia eletrica neural networks fault diagnosis

ACESSO AO ARTIGO

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