Made available in DSpace on 2014-12-17T14:55:03Z (GMT). No. of bitstreams: 1 RaphaelaGF.pdf: 1672960 bytes, checksum: 5b6b120f4026f9849183e5f96e363672 (MD5) Previous issue date: 2007-02-08 / ?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 / ?Esta disserta??o de mestrado apresenta o desenvolvimento de um sistema de detec??o e isolamento de falhas (DIF) baseado em redes neurais. O sistema ? dividido em duas etapas: uma de identifica??o neural do sistema e outra de detec??o e classifica??o de falhas. Ambos subsistemas usam t?cnicas de redes neurais com o algoritmoBackpropa- gation para redes Perceptronde M?ltiplas Camadas. Duas abordagens para identifica??o neural foram testadas e uma delas selecionada para fazer parte do sistema DIF. Oclassifi-cador de falhas utiliza apenas valores residuais para a classifica??o das mesmas. Todos os testes foram realizados tanto em ambiente simulado quanto em ambiente real, no intuito de comprovar dificuldades encontradas em testes reais n?o existentes quando se trabalha apenas com simula??es