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• W2040666870 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." @default.
• W2040666870 created "2016-06-24" @default.
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• W2040666870 date "2011-10-01" @default.
• W2040666870 modified "2023-09-28" @default.
• W2040666870 title "Neuro-fuzzy identification applied to fault detection in nonlinear systems" @default.
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• W2040666870 doi "https://doi.org/10.1080/00207721003653674" @default.
• W2040666870 hasPublicationYear "2011" @default.
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