Abstract
The contribution addressed by this paper refers to the development of a new dynamic neuro-fuzzy system and its application to fault detection and isolation of an evaporation station. Hybrid learning based on the fuzzy c-means clustering and steepest-descent method algorithms are used to train the neuro-fuzzy system. The experimental case study refers to the sensor and actuator fault diagnosis of an evaporation station from a sugar factory. An extended neuro-fuzzy generalised observer scheme is used to generate the residuals (symptoms) in the form of the one-step-ahead prediction errors. These are then analysed by a neural classifier in order to take the appropriate decision regarding the actual behaviour of the process.
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