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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Bibliographic Details
Main Author: Mendes, Mário J. G. C. (author)
Other Authors: Kowal, Marek (author), Korbicz, Józef (author), Costa, José M. G. Sá da (author)
Format: review
Language:eng
Published: 2020
Subjects:
Fault diagnosis
Fuzzy neural networks
Hierarchical structure
Abrupt faults
Incipient faults
Actuators
Online Access:http://hdl.handle.net/10400.21/10985
Country:Portugal
Oai:oai:repositorio.ipl.pt:10400.21/10985
Description
Summary: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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