• DocumentCode
    539732
  • Title

    Identification of Metal Crack Signal of Deep Drawing Based on Wavelet Packet and AR Spectrum Analysis

  • Author

    Zhigao, Luo ; Aicheng, Xu ; Xin, He ; Qiang, Chen

  • Author_Institution
    JiangSu Univ. of Mech. Eng., Zhenjiang, China
  • Volume
    2
  • fYear
    2011
  • fDate
    6-7 Jan. 2011
  • Firstpage
    354
  • Lastpage
    358
  • Abstract
    According to the characteristics of AE signals of the metal early micro crack in the process of deep drawing, the parameters to identify the crack signal are selected. The wavelet packet are adopted to resolve the AE signals with complex background noises and feeble crack characteristic, as well as the time series analysis method is applied to established the AR model of the resolved signal and to extract the energy value of the AR spectrum. The characteristic parameters are depended on the ratio of the energy value of the resolved signal bands and the total energy value of the crack AE signal. Finally, the method of fuzzy comprehensive evaluation is used to detect the crack signal by comparing the five models of evaluation results. Experimental results show that the application of the above methods have an unparalleled advantage on identifying the early micro crack signals with short-term impact character.
  • Keywords
    crack detection; fuzzy set theory; production engineering computing; sheet metal processing; spectral analysis; time series; AE signals; AR spectrum analysis; deep drawing; feeble crack characteristic; fuzzy comprehensive evaluation; metal crack signal; time series analysis method; wavelet packet; Fault diagnosis; Materials; Metals; Signal resolution; Time frequency analysis; Wavelet analysis; Wavelet packets; AE signals; AR spectrum; Crack identification; Metal drawing parts; Wavelet Packet;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Measuring Technology and Mechatronics Automation (ICMTMA), 2011 Third International Conference on
  • Conference_Location
    Shangshai
  • Print_ISBN
    978-1-4244-9010-3
  • Type

    conf

  • DOI
    10.1109/ICMTMA.2011.375
  • Filename
    5721193