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Research and application of new threshold de-noising algorithm for monitoring data analysis in nuclear power plant

  • Yan Cui (崔 妍)
  • Shijun Chen (陈世均)
  • Meng Qu (瞿 勐)
  • Shanhong He (何善红)
Article
  • 25 Downloads

Abstract

Under the complex condition of nuclear power plant, all kinds of influence factors may cause distortion of on-line monitoring data. It is essential that on-line monitoring data should be de-noised in order to ensure the accuracy of diagnosis. Based on the research of wavelet analysis and threshold de-noising, a new threshold denoising method based on Mallat transform is proposed. This method adopts factor weighing method for threshold quantization. Through the specific case of nuclear power plant, it is verified that the algorithm is of validity and superiority.

Keywords

wavelet analysis Mallat transform threshold de-noising factor weighing method 

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References

  1. [1]
    DONOHO D L, JOHNSTONE I M. Ideal spatial adaptation by wavelet shrinkage [J]. Biometrika, 1994, 81(3): 425–455.MathSciNetCrossRefMATHGoogle Scholar
  2. [2]
    BURRUS C S, GOPINATH R A, GUO H T. Introduction to wavelets and wavelet transforms [M]. New Jersey: Prentice Hall, 1998: 18–23.Google Scholar
  3. [3]
    XU Y S, WEAVER J B, HEALY D M, et al. Wavelet transform domain filters: A spatially selective noise filtration technique [J]. IEEE Transactions on Image Processing, 1994, 3(6): 747–758.CrossRefGoogle Scholar
  4. [4]
    JOY J, PETER S, JOHN N. Denoising using soft thresholding [J]. International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, 2013, 2(3): 1027–1032.Google Scholar
  5. [5]
    FU W, XU S H. Improved algorithm for threshold de-noising in wavelet transform domain [J]. Chinese Joural of Sensors and Actuators, 2006, 19(2): 534–540.MathSciNetGoogle Scholar
  6. [6]
    CHENG L Z, WANG H X, LUO Y, et al. Wavelet analysis [M]. Beijing: Science Press, 2004: 87–95.Google Scholar
  7. [7]
    GERONIMO J, HARDIN D, MASSOPUST P. Fractal functions and wavelet expansions based on several scaling funcitons [J]. Journal of Approximation Theory, 1994, 78(3): 373–401.MathSciNetCrossRefMATHGoogle Scholar
  8. [8]
    MALLAT S G. A theory for multiresolution signal decomposition: The wavelet representation [J]. IEEE Transactions on Pattern Analysis and Machine Iintelligence, 1989, 11(7): 674–693.CrossRefMATHGoogle Scholar
  9. [9]
    MALLAT S, HWANG W L. Singularity detection and processing with wavelets [J]. IEEE Transactions on Information Theory, 1992, 38(2): 617–643.MathSciNetCrossRefMATHGoogle Scholar
  10. [10]
    ZHOU W. Wavelet analysis and application based on Matlab [M]. Xi’an: Xidian University Press, 2006: 87–125.Google Scholar

Copyright information

© Shanghai Jiaotong University and Springer-Verlag Berlin Heidelberg 2017

Authors and Affiliations

  • Yan Cui (崔 妍)
    • 1
  • Shijun Chen (陈世均)
    • 1
  • Meng Qu (瞿 勐)
    • 1
  • Shanhong He (何善红)
    • 1
  1. 1.Suzhou Nuclear Power Research InstituteGuangdongChina

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