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Neural Network and Fuzzy Logic Diagnostics of 1X Faults in Rotating Machinery

[+] Author Affiliations
A. El-Shafei, T. A. F. Hassan, A. K. Soliman, Y. Zeyada

RITEC, Cairo, Egypt

N. Rieger

STI Technologies, Inc., Rochester, NY

Paper No. GT2005-68885, pp. 851-860; 10 pages
doi:10.1115/GT2005-68885
From:
  • ASME Turbo Expo 2005: Power for Land, Sea, and Air
  • Volume 4: Turbo Expo 2005
  • Reno, Nevada, USA, June 6–9, 2005
  • Conference Sponsors: International Gas Turbine Institute
  • ISBN: 0-7918-4727-6 | eISBN: 0-7918-3754-8
  • Copyright © 2005 by ASME

abstract

In this paper, the application of Neural Networks and Fuzzy Logic to the diagnosis of Faults in Rotating Machinery is investigated. The Learning-Vector-Quantization (LVQ) Neural Network is applied in series and in parallel to a Fuzzy inference engine, to diagnose 1x faults. The faults investigated are unbalance, misalignment, and structural looseness. The method is applied to a test rig [1], and the effectiveness of the integrated Neural Network and Fuzzy Logic method is illustrated.

Copyright © 2005 by ASME

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