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Operational Reliability Improvement of Plants Through Intelligent Fault Detection and Diagnosis

[+] Author Affiliations
E. Ricky Odoom

SRAM.FUZ Corporation, St. Catherines, Ontario, Canada

Paper No. IJPGC2003-40052, pp. 253-260; 8 pages
doi:10.1115/IJPGC2003-40052
From:
  • International Joint Power Generation Conference collocated with TurboExpo 2003
  • 2003 International Joint Power Generation Conference
  • Atlanta, Georgia, USA, June 16–19, 2003
  • Conference Sponsors: Power Division
  • ISBN: 0-7918-3692-4 | eISBN: 0-7918-3677-0
  • Copyright © 2003 by ASME

abstract

Real-time Fault Detection and Diagnosis of modern dynamic process plants are continuously receiving increasing attention both theoretically and practically. In recent years, attempts have been made to apply Artificial Intelligence techniques to the Fault Detection Diagnosis task for improving the operational reliability of complex dynamic plants. The aim of this paper is to discuss the basic concepts, issues and tools of some of the emerging intelligence technologies for Fault Detection and Diagnosis schemes. The emphasis is given to the methods, which are based on Artificial Intelligent systems and which are appropriate for diagnosing faults in complex dynamic plants.

Copyright © 2003 by ASME

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