Fault Diagnosis Using Neuro-fuzzy Systems with Local Recurrent Structure
نویسندگان
چکیده
This paper investigates the development of the Adaptive Neuro-Fuzzy Systems with Local Recurrent Structure (ANFS-LRS) and their application to Fault Detection and Isolation (FDI). Hybrid learning, based on a fuzzy clustering algorithm and a gradientlike method, is used to train the ANFS-LRS. The experimental case study refers to an application of fault diagnosis of an electro-pneumatic actuator. A neuro-fuzzy simplified observer scheme is used to generate the residuals (symptoms) in the form of the one-stepahead prediction errors. These are further analysed by a neural classifier in order to take the appropriate decision regarding the actual behaviour of the process. Copyright © 2005 IFAC
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تاریخ انتشار 2005