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.
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References
Daubechies I: The Wavelet Transform, Time- Frequency Localization and Signal Analysis. IEEE Transactions on Information Theory, Vol 6, No 5, Pg 961, Sept 1990.
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.
Newland D E: An Introduction to Random Vibrations, Spectral and Wavelet Analysis (3rd Edition), Longman Scientific and Technical Publishers, 1993.
O’Brien J C and Maclntyre J, Wavelets: An Alternative to Fourier Analysis. Seminar: Vibrations in the Power Industry, IMechE, 1994.
Hinton G, How Neural Networks Learn from Experience. Scientific American, September 1992.
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© 1995 Springer-Verlag/Wien
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MacIntyre, J., O’Brien, J.C. (1995). Investigations into the Use of Wavelet Transformations as Input into Neural Networks for Condition Monitoring. In: Artificial Neural Nets and Genetic Algorithms. Springer, Vienna. https://doi.org/10.1007/978-3-7091-7535-4_32
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DOI: https://doi.org/10.1007/978-3-7091-7535-4_32
Publisher Name: Springer, Vienna
Print ISBN: 978-3-211-82692-8
Online ISBN: 978-3-7091-7535-4
eBook Packages: Springer Book Archive