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.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
