Fault Diagnosis using Neuro-Fuzzy Systems with Local Recurrent Structure
| Authors: | Mirea Letitia, Technical University of Iasi, Romania
Patton Ron J., University of Hull, United Kingdom |
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| Topic: | 6.4 Safeprocess |
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| Session: | Fault Diagnosis and Fault Tolerant Control: Theory |
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| Keywords: | fault diagnosis, fuzzy multiple-modelling, fuzzy hybrid systems, neural networks, dynamic modelling, neural classifier, actuator systems |
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Abstract
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 gradient-like 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-step-ahead prediction errors. These are further analysed by a neural classifier in order to take the appropriate decision regarding the actual behaviour of the process.
