Neural Network Fault Detection System for Dynamic Processes

• Autorzy: Korbicz J. , Patan K. , Obuchowicz A.
• Języki publikacji: EN

Abstrakty

EN

The neural model-based Fault Detection and Isolation (FDI) system for dynamic non-linear processes is considered. The emphasis is placed upon the use of Artificial Neural Networks (ANN's) for residual generation. The proposed network is constructed with the Dynamic Neuron Model (DNM) which contains local memory. Similar to server based schemes, a network is applied to build the nominal and fault models of the investigated system. The output residuals between the process and the models bank are use to detect and identify faults in the system. The modelling efficiency based on the multilayer feedforward Network of Dynamic Neurons (NDN) is compared with the Elman and recurrent network with outside feedbacks. Finally, the NDN and the cascade NDN architectures are applied to build Neural-Residual Generators (NRG) of the two tank system.

Słowa kluczowe

EN

fault detection system; dynamic neural networks; observer based scheme; cascade network architectures optimization; two-tank system

• Wydawca: Polska Akademia Nauk, Wydział IV Nauk Technicznych
• Czasopismo: Bulletin of the Polish Academy of Sciences. Technical Sciences
• Rocznik: 2001
• Tom: vol. 49, nr 2
• Strony: 301--321
• Opis fizyczny: rys., tab., bibliogr. 30 poz.

Twórcy

autor

Korbicz J.

autor

Patan K.

autor

Obuchowicz A.

• Institute of Control and Computation Engineering, Technical University of Zielona Góra, ul. Podgórna 50, 65-246 Zielona Góra, Poland (Politechnika Zielonogórska, Wydział Robotyki i Technik Programowania),