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The location of anomalous spikes on a working rotor: a theorical-experimental test

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
An efficient and efficacious response to the continuous request of "high performance", in terms of qualitative standards, becomes one of the most important factors in determining the profit of a business. The aim of this paper is to check the reliability of a signal analysis method based on a Discrete Wavelet Transform (DWT). This algorithm appears useful in revealing anomalies that are added to a regular signal. Such anomalies, characterized by high frequency, small amplitude and short period, generally are filtered in a data logging or sampling. Moreover, it has been illustrated how, for each level of the DWT, the maximum value of the entropy allows the location as well as the best graphical representation of the phenomenon in study. In this paper the results obtained during the functioning of an experimental equipment will be shown.
Rocznik
Strony
547--556
Opis fizyczny
Bibliogr. 13 poz., rys., tab., wykr.
Twórcy
autor
  • Department of Mechanical Engineering for Energetic University of Naples "Federico II" Via Claudio 21, 80125, Napoli, ITALY
autor
  • University of Naples "Federico II" Via Pansini 5, 80131, Napoli, ITALY
autor
  • Departament of Mathematics for Economic, Financial and Insurance Decisions University of Rome "La Sapienza" Via del Castro Laurenziano 6, 00161, Roma, ITALY
Bibliografia
  • [1] Antoniadis A. and Lavergne C. (1995): Variance function estimation in regression by wavelet methods, In: Lecture Notes in Statistics: Wavelet and Statistics (A. Antoniadis and G. Oppenheim, Eds.). - New York: Springer.
  • [2] Daubechies I. (1988): Orthonormal bases of compactly supported wavelets. - Comms. Pure Appl. Math., No.41 pp.909-996.
  • [3] Dillbechies I. (1992): Ten Lectures on Wavelets. - Philadelphia: SIAM.
  • [4] Donoho D.L. and Johnstone I.M. (1995): Adapting to unknown smoothness via wavelet shrinkage. - Journal of the American Statistical Association, vol.90, pp. 1200-1224.
  • [5] Hardle W., Kerkyacharian G., Picard D. and Tsybakov A. (1998): Lecture Notes in Statistics: Wavelets, Approximation and Statistical Applications. - New York: Springer.
  • [6] Kahane J.P. and Lemarie P.G. (1995): Fourier series and wavelets, In: Studies in the Development of Modem Mathematics. - Amsterdam: Gordon and Breach Science Publishers, vol.3.
  • [7] Kaiser G. (1994): A Friendly Guide to Wavelets. - Boston: Birkhauser.
  • [8] Mallat S.G. (1989): Multiresolution approximations and wavelet orthonormal bases of L . - Trans. Amer. Math. Soc., vol.l, No.315, pp.69-87.
  • [9] Meyer Y. (1992): Wavelets and Operators. - Cambridge: Cambridge University Press.
  • [10] Nason G.P. (1995): Choice of the threshold parameter in wavelet function estimation, In: Lecture Notes in Statistics: Wavelet and Statistics (A. Antoniadis and G. Oppenheim, Eds.). - New York: Springer.
  • [11] Ogden R.T. (1997): Essential Wavelets for Statistical Applications and Data Analysis. - Boston: Birkhauser.
  • [12] Strang G. and Nguyen T. (1996): Wavelets and Filter Banks. - Wellesley: Wellesley-Cambridge Press.
  • [13] Teolis A. (1998): Computational Signal Processing with Wavelets. - Boston: Birkhauser.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BPZ2-0007-0035
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