Neuro-fuzzy identification applied to fault detection in nonlinear systems

L. Blázquez, Luis de Miguel, Fernando Aller and José Perán

International Journal of Systems Science, 2011, vol. 42, issue 10, 1771-1787

Abstract: This article describes a fault detection method, based on the parity equations approach, to be applied to nonlinear systems. The input–output nonlinear model of the plant, used in the method, has been obtained by a neural fuzzy inference architecture and its learning algorithm. The proposed method is able to detect small abrupt faults, even in systems with unknown nonlinearities. This method has been applied to a real industrial pilot plant, and good performance has been obtained for the experimental case of fault detection in the level sensor of a level control process in the said industrial pilot plant.

Date: 2011
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DOI: 10.1080/00207721003653674

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