Fault diagnosis of an industrial gas turbine using neuro-fuzzy methods

Vasile Palade, Ron J. Patton, Faisel J. Uppal, Joseba Quevedo, S. Daley

Research output: Contribution to journalArticle

30 Citations (Scopus)

Abstract

The paper focuses on the application of neuro-fuzzy techniques in fault detection and isolation. The objective of this paper is to detect and isolate faults to an industrial gas turbine, with emphasis on faults occurred in the actuator part of the gas turbine. A neuro-fuzzy based learning and adaptation of TSK fuzzy models is used for residual generation, while for residual evaluation a neuro-fuzzy classifier for Mamdani models is used. The paper is concerned on how to obtain an interpretable fault classifier as well as interpretable models for residual generation.

Original languageEnglish
Pages (from-to)471-476
Number of pages6
JournalIFAC papers online
Volume15
Issue number1
DOIs
Publication statusPublished - 2002
Externally publishedYes
Event15th World Congress of the International Federation of Automatic Control: IFAC 2002 - Barcelona, Spain
Duration: 21 Jul 200226 Jul 2002
Conference number: 15

Keywords

  • Fault detection
  • Fault diagnosis
  • Fuzzy models
  • Neural networks
  • Neuro-fuzzy

ASJC Scopus subject areas

  • Control and Systems Engineering

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