Neuro-fuzzy based fault diagnosis of a Steam Generator

Document Type

Conference Proceeding

Publication Date

12-1-2009

Publication Title

IFAC Proceedings Volumes (IFAC-PapersOnline)

First Page

1180

Keywords

Fault Diagnosis, Locally Linear Model Tree, Locally Linear Neuro Fuzzy model, Neuro-fuzzy networks, Steam Generator

Last Page

1185

Abstract

This paper focuses on the development and application of a Neuro-Fuzzy (NF) networks-based scheme for Fault Detection and Isolation (FDI) in a U-tube Steam Generator (UTSG). First, a NF network is trained with data collected from a full scale UTSG simulator, and residuals are generated for fault detection. To identify the UTSG, a Locally Linear Neuro-Fuzzy (LLNF) model is used. This model is trained using the Locally Linear Model Tree (LOLIMOT) algorithm which is an incremental tree structure algorithm. Then, an evolutionary algorithm is used to train a Mamdani type NF network to classify the residuals. The residuals are analyzed by using this NF classifier for fault isolation purposes. © 2009 IFAC.

DOI

10.3182/20090630-4-ES-2003.0120

ISSN

14746670

ISBN

9783902661463

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