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Comparison of Neural Network Architectures for Machinery Fault Diagnosis

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

Cairo University, Giza, Egypt

N. Rieger

STI Technologies, Inc., Rochester, NY

Paper No. GT2003-38450, pp. 415-424; 10 pages
  • ASME Turbo Expo 2003, collocated with the 2003 International Joint Power Generation Conference
  • Volume 1: Turbo Expo 2003
  • Atlanta, Georgia, USA, June 16–19, 2003
  • Conference Sponsors: International Gas Turbine Institute
  • ISBN: 0-7918-3684-3 | eISBN: 0-7918-3671-1
  • Copyright © 2003 by ASME


This paper provides a comparison of the performance of five different neural network architectures in diagnosing machinery faults. The network architectures include perceptrons, linear filters, feed-forward, self-organizing, and LVQ. The study provides a critical analysis of the performance of each network on a test rig with different faults. The comparison discusses the success rate in network training and identification of faults including: unbalance and looseness. It is shown that the perceptron and LVQ architectures were superior and achieved 100% diagnosis on the cases presented.

Copyright © 2003 by ASME



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