Neuro-fuzzy techniques in FDI system for sugar factory actuators
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 t...
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| Outros Autores: | , , |
| Formato: | review |
| Idioma: | eng |
| Publicado em: |
2020
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| Assuntos: | |
| Texto completo: | http://hdl.handle.net/10400.21/10985 |
| País: | Portugal |
| Oai: | oai:repositorio.ipl.pt:10400.21/10985 |
| 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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