Neuro-fuzzy based fault diagnosis of a Steam Generator

Roozbeh Razavi-Far, Hadi Davilu, Vasile Palade, Caro Lucas

Research output: Chapter in Book/Report/Conference proceedingConference proceeding

16 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationSAFEPROCESS'09 - 7th IFAC International Symposium on Fault Detection, Supervision and Safety of Technical Systems, Proceedings
Pages1180-1185
Number of pages6
DOIs
Publication statusPublished - 2009
Externally publishedYes
Event7th IFAC International Symposium on Fault Detection, Supervision and Safety of Technical Systems, SAFEPROCESS'09 - Barcelona, Spain
Duration: 30 Jun 20093 Jul 2009

Conference

Conference7th IFAC International Symposium on Fault Detection, Supervision and Safety of Technical Systems, SAFEPROCESS'09
CountrySpain
CityBarcelona
Period30/06/093/07/09

Keywords

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

ASJC Scopus subject areas

  • Control and Systems Engineering

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