• DocumentCode
    3388507
  • Title

    Sigma-Sampling Wavelet Denoising for Structural Health Monitoring

  • Author

    Medda, Alessio ; Chicken, Eric ; DeBrunner, Victor

  • Author_Institution
    Department of Electrical and Computer Engineering, FAMU-FSU College of Engineering, Florida State University
  • fYear
    2007
  • fDate
    26-29 Aug. 2007
  • Firstpage
    119
  • Lastpage
    122
  • Abstract
    Structural Health Monitoring (SHM) techniques are non-destructive evaluation methods that try to detect, locate and assess the structural damage of a structure. The presence of damage in a structure often results in very small changes in the vibration response of the structure. It is very difficult to detect these changes due to their low order of magnitude relative to the vibration signal. A wavelet method is proposed to preprocess the signal prior to damage analysis in order to improve its signal to noise ratio and bring the vibration signal damage signature to a detectable level. Because of the characteristics of the vibration signal, standard thresholding techniques do not yield useful results. Our proposed method estimates the variance from automatically selected, structure-free portions of the vibration response and then uses this value to threshold the wavelet coefficients prior to reconstruction. This novel technique has been called sigma-sampling thresholding.
  • Keywords
    Background noise; Computerized monitoring; Discrete wavelet transforms; Educational institutions; Noise reduction; Power engineering and energy; Signal processing; Statistics; Wavelet coefficients; Wavelet packets; Array signal processing; Wavelet transforms;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Statistical Signal Processing, 2007. SSP '07. IEEE/SP 14th Workshop on
  • Conference_Location
    Madison, WI, USA
  • Print_ISBN
    978-1-4244-1198-6
  • Electronic_ISBN
    978-1-4244-1198-6
  • Type

    conf

  • DOI
    10.1109/SSP.2007.4301230
  • Filename
    4301230