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Neuro-fuzzy techniques in FDI system for sugar factory actuators

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Detalhes bibliográficos
Resumo:Fault diagnosis systems have an important role in industrial plants because the early fault detection and isolation (FDI) can minimize damages in the plants. The main aim of this work is to propose a two-stage neuro-fuzzy approach as a fault diagnosis system in dynamic processes. The first stage of the system is responsible for fault detection and is implemented using a neuro-fuzzy model. The second stage of the system is responsible for fault isolation and is built using an hierarchical structure of fuzzy neural networks. The FDI system is applied to fault diagnosis in the sugar factory actuators.
Autores principais:Mendes, Mário J. G. C.
Outros Autores:Kowal, Marek; Korbicz, Józef; Costa, José M. G. Sá da
Assunto:Fault diagnosis Fuzzy neural networks Hierarchical structure Abrupt faults Incipient faults Actuators
Ano:2001
País:Portugal
Tipo de documento:recensão
Tipo de acesso:acesso aberto
Instituição associada:Universidade Politécnica de Lisboa
Idioma:inglês
Origem:Repositório Científico da Universidade Politécnica de Lisboa
Descrição
Resumo:Fault diagnosis systems have an important role in industrial plants because the early fault detection and isolation (FDI) can minimize damages in the plants. The main aim of this work is to propose a two-stage neuro-fuzzy approach as a fault diagnosis system in dynamic processes. The first stage of the system is responsible for fault detection and is implemented using a neuro-fuzzy model. The second stage of the system is responsible for fault isolation and is built using an hierarchical structure of fuzzy neural networks. The FDI system is applied to fault diagnosis in the sugar factory actuators.

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