Designing neural-network-based fault detection systems with D-optimum experimental conditions

• Autorzy: Witczak M. , Prętki P.
• Języki publikacji: EN

Abstrakty

EN

The paper deals with an application of the theory of optimum experimental design to the problem of selecting the data set for developing neural models. Another objective is to show how to design a robust fault detection scheme with neural networks and how to increase its fault sensitivity by decreasing model uncertainty. It is also shown that the optimum design is independent of the parameters that enter linearly into the neural network. The final part of this paper shows a comprehensive simulation study regarding modelling and fault detection with the proposed approach. In particular, The DAMADICS benchmark problem is utilized to verify the performance and reliability of the proposed technique.

Słowa kluczowe

EN

neural network; detection systems

PL

sieć neuronowa

• Wydawca: Instytut Podstawowych Problemów Techniki PAN
• Czasopismo: Computer Assisted Mechanics and Engineering Sciences
• Rocznik: 2005
• Tom: Vol. 12, No. 2-3
• Strony: 279--291
• Opis fizyczny: Bibliogr. 19 poz., rys., tab., wykr.

Twórcy

autor

Witczak M.

• University of Zielona Góra, Institute of Control and Computation Engineering, ul. Podgórna 50, 65-246 Zielona Góra

autor

Prętki P.

• University of Zielona Góra, Institute of Control and Computation Engineering, ul. Podgórna 50, 65-246 Zielona Góra

Bibliografia

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• [13] M.F. Metenidis, M. Witczak and J. Korbicz. A novel genetic programming approach to nonlinear system modelling: application to the DAMADICS benchmark problem. Engineering Applications of Artificial Intelligence, 17 (4): 363-370, 2004.
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• [17] Witczak M. Identification and Fault Detection of Non-linear Dynamic Systems. University of Zielona Góra Press. Zielona Góra, 2003.
• [18] M. Witczak and J. Korbicz Observers and genetic programming in the identification and fault diagnosis of non-linear dynamic systems. In: [12], 2004.
• [19] M. Witczak. Locally D-optimum sequential experimental design for neural networks: application to robust fault detection Proc. 10-th IEEE Int. Conf. Methods and Models in Automation and Robotics: MMAR 2004, 2: 798-804, August 28-31, Międzyzdroje Poland 2004.