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Detection of Propagating Cracks in Rotors Using Neural Networks

[+] Author Affiliations
S. A. Adewusi, B. O. Al-Bedoor

King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia

Paper No. PVP2002-1518, pp. 71-78; 8 pages
  • ASME 2002 Pressure Vessels and Piping Conference
  • Piping and Component Analysis and Diagnosis
  • Vancouver, BC, Canada, August 5–9, 2002
  • Conference Sponsors: Pressure Vessels and Piping Division
  • ISBN: 0-7918-4658-X
  • Copyright © 2002 by ASME


This paper presents the application of neural networks for rotor cracks detection. The basic working principles of neural networks are presented. Experimental vibration signals of rotors with and without a propagating crack were used to train the Multi-layer Feed-forward Neural Networks using back-propagation algorithm. The trained neural networks were tested with other set of vibration data. A simple two-layer feed-forward neural network with two neurons in the input layer and one neuron in the output layer trained with the signals of a cracked rotor and a normal rotor without a crack was found to be satisfactory in detecting a propagating crack. Trained three-layer networks were able to detect both the propagating and non-propagating cracks. The FFT of the vibration signals showing variation in amplitude of the harmonics as time progresses are also presented for comparison.

Copyright © 2002 by ASME



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