Neuro-fuzzy identification applied to fault detection in nonlinear systems

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.