Development of Dynamic Neural Networks With Application to Observer-Based Fault Detection and Izolation

• Autorzy: Marcu T. , Mirea L. , Frank P. M.
• Języki publikacji: EN

Abstrakty

EN

The paper suggests a neural-network approach to the design of robust fault diagnosis systems. The main emphasis is placed upon the development of neural observer schemes. They are built based on dynamic neural networks, i.e. dynamic multi-layer perceptrons with mixed structure. The goal is to achieve an adequate approximation of process outputs for known classes of the process behaviour. The obtained symptoms are then classified by means of static artificial nets. Appropriate decision mechanisms are designed for each type of observer schemes. An application to a laboratory process is included. It refers to component and instrument fault detection and isolation in a three-tank system.

Słowa kluczowe

EN

fault diagnosis; dynamic neural networks; system identification; static neural classifiers; three-tank system

PL

wykrywanie błędu; dynamiczne sieci neuronowe; identyfikacja systemu

• Wydawca: Oficyna Wydawnicza Uniwersytetu Zielonogórskiego
• Czasopismo: International Journal of Applied Mathematics and Computer Science
• Rocznik: 1999
• Tom: Vol. 9, no 3
• Strony: 547--570
• Opis fizyczny: Bibliogr. 25 poz., rys., tab., wykr.

Twórcy

autor

Marcu T.

autor

Mirea L.

autor

Frank P. M.

• Universityof Iasi Department of Automatic Control and IndustrialInformatics Blvd. D. Mangeron No. 53A, RO-6600 Iasi,Romania,