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NEURO-FUZZY STRUCTURES IN FDI SYSTEM
M. J. G. C. Mendes*, M. Kowal**, J. Korbicz** and
J. M. G. Sá da Costa***
* IDMEC/ISEL - Instituto Superior de Engenharia de Lisboa
Polytechnic Institute of Lisbon, Dept. of Mechanical Engineering
Rua Conselheiro Emidio Navarro, 1949-014 Lisboa, Portugal
fax: +351 21 8317057, e-mail:mmendes@dem.isel.ipl.pt
** University of Zielona Góra, Institute of Control and
Computation Engineering
ul. Podgórna 50, 65-246 Zielona Góra, Poland
fax:+48 68 3254615, e-mail: {M.Kowal,J.Korbicz}@issi.uz.zgora.pl
*** Technical University of Lisbon, Instituto Superior Técnico
Dept. of Mechanical Engineering, GCAR/IDMEC
Av. Rovisco Pais, 1049-001 Lisboa, Portugal
fax:+351 21 8498097, e-mail:sadacosta@dem.ist.utl.pt
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 actuators of one sugar factory.
Keywords: Fault diagnosis, neural network, fuzzy modelling, backpropagation algorithms, hierarchical structure, actuators.
Session slot T-We-A10: Neuro-Fuzzy Applications of Fault Diagnosis/Area code 7e : Fault Detection, Supervision and Safety of Technical Processes
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