Advertisement

Support vector machine and discrete wavelet transform method for strip rupture detection based on transient current signal

  • S. W. Yang
  • A. Widodo
  • W. Caesarendra
  • J. S. Oh
  • M. C. Shim
  • S. J. Kim
  • B. S. Yang
  • W. H. Lee
Conference paper

Abstract

This paper proposes the fault diagnosis method in 6 high cold rolling mill which consist of 5 stand to assess the normal and fault conditions. The proposed method concerns with the strip rupture fault diagnosis based on transient current signal. Firstly, the signal smoothing technique is performed initially to highlight the fundamental of transient signal at normal and fault condition. Then the smoothed signal is subtracted from the original signal in order to transform the original data become useful data that used for further analysis. Next, discrete wavelet transform (DWT) method is performed to present the detail signal. Moreover, features are calculated from detail signal of DWT and then extracted using principal component analysis (PCA) and kernel principal component analysis (KPCA) for dimensionality reduction purpose. Finally, using support vector machine (SVM) for classification, the results of stand 5 shows more clear classified compare with other stands.

Keywords

Support Vector Machine Discrete Wavelet Transform Fault Diagnosis Transient Signal Kernel Principal Component Analysis 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Mackel J. (1999) Condition monitoring and diagnostic engineering for rolling mills. International congress of COMADEM.Google Scholar
  2. 2.
    Widodo A and Yang BS. (2008) Wavelet support vector machine for induction machine fault diagnosis based on transient current signal. Expert System with Application, 35(1-2), 307-316.CrossRefGoogle Scholar
  3. 3.
    Douglas H, Pillay P and Ziarani A. (2004) A new algorithm for transient motor signature analysis using wavelet. IEEE Transaction on Industry Applications, 40(5), 1361-1368.CrossRefGoogle Scholar
  4. 4.
    Douglas H, Pillay P and Ziarani A. (2004) The impact of wavelet selection on transient motor current signature analysis. IEEE International Conference on Electric Machines and Drives, 1361-1368.Google Scholar
  5. 5.
    Burrus CS, Gopinath RA and Guo H. (1998) Introduction to wavelets and wavelet transforms, a primer. Englewood Cliffs, NJ: Prentice-Hall.Google Scholar
  6. 6.
    Daubechies I. (1992) Ten lectures on wavelets. SIAM, Pennsylvania, USA.MATHGoogle Scholar
  7. 7.
    Hwang WW. (2004) Condition classification and fault diagnosis of rotating machine using support vector machine, Master course thesis, Pukyong National Univ.Google Scholar
  8. 8.
    Han T. (2005) Developement of a feature-based fault diagnostics system and its application to induction motors, Doctor course thesis, Pukyong National Univ.Google Scholar
  9. 9.
    Vapnik VN. (1995) The nature of statistical learning theory. New York: Springer.MATHGoogle Scholar

Copyright information

© Springer-Verlag 2010

Authors and Affiliations

  • S. W. Yang
    • 1
  • A. Widodo
    • 2
  • W. Caesarendra
    • 1
  • J. S. Oh
    • 1
  • M. C. Shim
    • 1
  • S. J. Kim
    • 1
  • B. S. Yang
    • 1
  • W. H. Lee
    • 3
  1. 1.School of Mechanical EngineeringPukyong National UniversityNam-gu, BusanKorea, Republic of
  2. 2.Mechanical Engineering DepartmentDiponegoro UniversityTembalang, SemarangIndonesia
  3. 3.POSCO Technical Research LaboratoriesNam-gu, PohangKorea, Republic of

Personalised recommendations