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Automated Diagnostics of Rotating Machinery

[+] Author Affiliations
A. El-Shafei

Cairo University, Giza, Egypt

N. Rieger

STI Technologies, Inc., Rochester, NY

Paper No. GT2003-38453, pp. 491-498; 8 pages
  • ASME Turbo Expo 2003, collocated with the 2003 International Joint Power Generation Conference
  • Volume 4: Turbo Expo 2003
  • Atlanta, Georgia, USA, June 16–19, 2003
  • Conference Sponsors: International Gas Turbine Institute
  • ISBN: 0-7918-3687-8 | eISBN: 0-7918-3671-1
  • Copyright © 2003 by ASME


This paper provides an overview of the current available technologies for automated machinery condition evaluation and fault diagnosis within an overall plant asset management system. The paper presents a basic overview of an integrated plant asset management system, and focuses on the available technologies for automated diagnostics including statistical analysis of data, parametric model diagnosis, non-parametric model diagnosis (artificial neural networks), and rule-based diagnostics including expert systems and fuzzy logic. The current state-of-the-art and the expected realistic future developments are discussed.

Copyright © 2003 by ASME
Topics: Machinery



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