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Investigations into the Use of Wavelet Transformations as Input into Neural Networks for Condition Monitoring

  • J MacIntyre
  • J C O’Brien
Conference paper

Abstract

Condition monitoring has become widely accepted in industry, with vibration providing the most useful condition parameter. Analysis of these vibration signals usually involves Fourier transforms and, occasionally, neural networks. However, a problem when using Fourier transforms as input into a neural network is the size of the input data set. Wavelet transforms provide an alternative which allows for a dramatic reduction in the size of the data set. This paper will explore the feasibility of using wavelet transforms as input into neural networks for condition monitoring.

Keywords

Neural Network Condition Monitoring Wavelet Coefficient Vibration Signal Radial Basis Function Network 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

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    Daubechies I: The Wavelet Transform, Time- Frequency Localization and Signal Analysis. IEEE Transactions on Information Theory, Vol 6, No 5, Pg 961, Sept 1990.MathSciNetGoogle Scholar
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    Mallat S G: A Theory for Multiresolution Signal Decomposition: The Wavelet Representation. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol 11. No 7, Pg 674, July 1989.MATHCrossRefGoogle Scholar
  3. [3]
    Newland D E: An Introduction to Random Vibrations, Spectral and Wavelet Analysis (3rd Edition), Longman Scientific and Technical Publishers, 1993.Google Scholar
  4. [4]
    O’Brien J C and Maclntyre J, Wavelets: An Alternative to Fourier Analysis. Seminar: Vibrations in the Power Industry, IMechE, 1994.Google Scholar
  5. [5]
    Hinton G, How Neural Networks Learn from Experience. Scientific American, September 1992.Google Scholar

Copyright information

© Springer-Verlag/Wien 1995

Authors and Affiliations

  • J MacIntyre
    • 1
  • J C O’Brien
    • 2
  1. 1.School of Computing and Information SystemsUniversity of SunderlandEngland
  2. 2.School of Mathematical and Information SciencesCoventry UniversityEngland

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