Pump Failure Detection Using Support Vector Data Descriptions

  • David M. J. Tax
  • Alexander Ypma
  • Robert P. W. Duin
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1642)


For good classiffication preprocessing is a key step. Good preprocessing reduces the noise in the data and retains most information needed for classiffication. Poor preprocessing on the other hand can make classiffication almost impossible. In this paper we evaluate several feature extraction methods in a special type of outlier detection problem, machine fault detection. We will consider measurements on water pumps under both normal and abnormal conditions. We use a novel data description method, called the Support Vector Data Description, to get an indication of the complexity of the normal class in this data set and how well it is expected to be distinguishable from the abnormal data.


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  1. [1]
    C. M. Bishop. Neural Networks for Pattern Recognition. Oxford University Press, Walton Street, Oxford OX2 6DP, 1995.Google Scholar
  2. [2]
    P. A. Devijver and J. Kittler. Pattern Recognition, A statistical approach. Prentice-Hall International, London, 1982.zbMATHGoogle Scholar
  3. [3]
    R. O. Duda and P. E. Hart. Pattern Classification and Scene Analysis. John Wiley & Sons, New York, 1973.zbMATHGoogle Scholar
  4. [4]
    J. G. Proakis and D. G. Manolakis. Digital signal processing-principles, algorithms and applications, 2nd ed. MacMillan Publ., New York, 1992.Google Scholar
  5. [5]
    P. Pudil, J. Novovicova, and J. Kittler. Floating search methods in feature selection. Pattern Recognition Letters, 15(11):1119–1125, 1994.CrossRefGoogle Scholar
  6. [6]
    D. M. J. Tax, D. de Ridder, and R. P. W. Duin. Support vector Classifiers: a first look. In Proceedings ASCI’97. ASCI, 1997.Google Scholar
  7. [7]
    D. M. J. Tax and R. P. W. Duin. Outlier detection using classifier instability. In Amin, A., Dori, D., Pudil, P., and Freeman, H.,editors, Advances in Pattern Recognition, Lecture notes in Computer Science, volume 1451, pages 593–601, Berlin, August 1998. Proc. Joint IAPR Int. Workshops SSPR’98 and SPR’98 Sydney, Australia, Springer.CrossRefGoogle Scholar
  8. [8]
    D. M. J. Tax and R. P. W Duin. Data domain description using support vectors. In Verleysen M.,editor, Proceedings of the European Symposium on Artificial Neural Networks 1999, pages 251–256. D.Facto, Brussel, April 1999.Google Scholar
  9. [9]
    V. Vapnik. The Nature of Statistical Learning Theory. Springer-Verlag New York, Inc., 1995.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1999

Authors and Affiliations

  • David M. J. Tax
    • 1
  • Alexander Ypma
    • 1
  • Robert P. W. Duin
    • 1
  1. 1.Pattern Recognition Group Dept. of Applied Physics, Faculty of Applied SciencesDelft University of TechnologyDelftThe Netherlands

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