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Advisor(s)
Abstract(s)
This research paper is concerned with the fault detection and isolation (FDI) problem, or more exactly, with a hierarchical structure of fuzzy neural networks (HFNN) used for fault isolation purposes in industrial processes. The main aim of this research work is to optimise the number of neurons in the hidden layer of all fuzzy neural networks (FNNs) used in the HFNN. Thus, the optimal brain surgeon (OBS) pruning algorithm has been used to prune all FNNs. After the OBS optimisation, the HFNN structure continues to be able to isolate correctly, abrupt and incipient, single and multiple faults. At the same time, the structure became simpler and better generalisation capabilities have been observed. A continuous binary distillation column having several actuated valves with PID control loops has been used as test bed of the proposed approach.
Description
Keywords
Fault diagnosis Fuzzy neural network Optimal Brain Surgeon
Citation
MENDES, Mário J. G. C.; CALADO, João M. F.; COSTA, J. M. G. Sá da – Pruning algorithm applied to a hierarchical structure of fuzzy neural networks: case study. In MMAR’2002 – 8th IEEE International Conference on Methods and Models in Automation and Robotics. Szczecin, Polónia: IEEE, 2002. Vol. 1, pp. 207-212
Publisher
Institute of Electrical and Electronics Engineers