Robust sensor and actuator fault diagnosis with GMDH neural networks

Authors:
Marcin Witczak;Marcin Mrugalski;Józef Korbicz
Affiliations:
Institute of Control and Computation Engineering, University of Zielona Góra, Zielona Góra, Poland;Institute of Control and Computation Engineering, University of Zielona Góra, Zielona Góra, Poland;Institute of Control and Computation Engineering, University of Zielona Góra, Zielona Góra, Poland
Venue:
IWANN'13 Proceedings of the 12th international conference on Artificial Neural Networks: advances in computational intelligence - Volume Part I
Year:
2013

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Abstract

The uncertainty of neural model influences the effectiveness of the neural model-based FDI and FTC systems. The application of the GMDH approach to the state-space neural model structure selection allows reducing the model uncertainty. The state-space representation of the neural model enables to develop a new technique of estimation of the neural model inputs based on the RUIF. This result enables performing robust fault detection and isolation of the actuators.