An Overproduce-and-Choose Strategy to Create Classifier Ensembles with Tuned SVM Parameters Applied to Real-World Fault Diagnosis

  • Estefhan Dazzi Wandekokem
  • Flávio M. Varejão
  • Thomas W. Rauber
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6419)

Abstract

We present a supervised learning classification method for model-free fault detection and diagnosis, aiming to improve the maintenance quality of motor pumps installed on oil rigs. We investigate our generic fault diagnosis method on 2000 examples of real-world vibrational signals obtained from operational faulty industrial machines. The diagnostic system detects each considered fault in an input pattern using an ensemble of classifiers, which is composed of accurate classifiers that differ on their predictions as much as possible. The ensemble is built by first using complementary feature selection techniques to produce a set of candidate classifiers, and finally selecting an optimized subset of them to compose the ensemble. We propose a novel ensemble creation method based on feature selection. We work with Support Vector Machine (SVM) classifiers. As the performance of a SVM strictly depends on its hyperparameters, we also study whether and how varying the SVM hyperparameters might increase the ensemble accuracy. Our experiments show the usefulness of appropriately tuning the SVM hyperparameters in order to increase the ensemble diversity and accuracy.

Keywords

Fault diagnosis feature selection feature extraction classifier ensemble Support Vector Machine multi-label classification 

References

  1. 1.
    Angelo, C.H.D., Bossio, G.R., Giaccone, S.J., Valla, M.I., Solsona, J.A., Garcia, G.O.: Online model-based stator-fault detection and identification in induction motors. IEEE Transactions on Industrial Electronics 56 (November 2009)Google Scholar
  2. 2.
    Bellini, A., Filippetti, F., Tassoni, C., Capolino, G.A.: Advances in diagnostic techniques for induction machines. IEEE Transactions on Industrial Electronics 55 (December 2008)Google Scholar
  3. 3.
    Bishop, C.M.: Pattern Recognition and Machine Learning. Springer, Berlin (2007)MATHGoogle Scholar
  4. 4.
    Cortes, C., Vapnik, V.: Support-vector network. Machine Learning 20, 273–297 (1995)MATHGoogle Scholar
  5. 5.
    Evgeniou, T., Pontil, M., Elisseeff, A.: Leave-one-out error, stability, and generalization of voting combinations of classifiers. Machine Learning (2002)Google Scholar
  6. 6.
    Guyon, I., Elisseeff, A.: An introduction to variable and feature selection. J. Mach. Learn. Res. 3, 1157–1182 (2003)MATHGoogle Scholar
  7. 7.
    Kuncheva, L.I.: Combining Pattern Classifiers: Methods and Algorithms. Springer, Heidelberg (2004)CrossRefMATHGoogle Scholar
  8. 8.
    Li, X., Wang, L., Sung, E.: Adaboost with svm-based component classifiers. Engineering Applications of Artificial Intelligence 21 (2008)Google Scholar
  9. 9.
    Mendel, E., Mariano, L.Z., Drago, I., Loureiro, S., Rauber, T.W., Varejao, F.M.: Automatic bearing fault pattern recognition using vibration signal analysis. In: Proc. of the IEEE International Symposium on Industrial Electronics (ISIE 2008) (2008)Google Scholar
  10. 10.
    Valentini, G., Dietterich, T.G.: Bias-variance analysis of support vector machines for the development of svm-based ensemble methods. The Journal of Machine Learning Research 5 (January 2004)Google Scholar
  11. 11.
    Zio, E., Baraldi, P., Gola, G.: Feature-based classifier ensembles for diagnosing multiple faults in rotating machinery. Applied Soft Computing 8, 1365–1380 (2008)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Estefhan Dazzi Wandekokem
    • 1
  • Flávio M. Varejão
    • 1
  • Thomas W. Rauber
    • 1
  1. 1.Department of Computer ScienceFederal University of Espírito SantoVitóriaBrazil

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