A Fast Intuitionistic Fuzzy Support Vector Machine Algorithm and Its Application in Wind Turbine Gearboxes Fault Diagnosis

  • Bin Jiao
  • Qing Zhang
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 323)


Support vector machine has been successfully applied to the fault diagnosis field, but there are still some problems in practical applications. In this paper we proposed an improved algorithm which reduces the number of support vectors through the reduction of the sample spae to improve the efficiency of the algorithm. As the traditional fuzzy support vector machine cannot classify the sample with the same membership, so we use intuition index to lower the probability of the sample to get the same membership. Here we improve the accuracy of the algorithm through properly redefine the fuzzy membership and intuition index. Finally, we use the improved algorithm to build a multi-classifier based on one against one principle and the voting rules, and apply the multi-classification algorithm to the wind turbine gearbox fault diagnosis. The diagnose results prove that the improved algorithm we proposed can properly resolve the problem of wind turbine gearboxes fault diagnosis.


wind turbine gearbox fault diagnosis intuitionistic fuzzy support vector machine 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Wang, H.-L., Zhang, Z.-Q., Cui, X.-X., Song, T.: Test optimization of real-time monitoring and fault diagnosis system based on improved particle swarm optimization. Systems Engineering and Electronics 33(4), 958–962 (2011)Google Scholar
  2. 2.
    Mi, J., Ji, G.-Y.: Application of Improved BP Neural Network in Fault Diagnosis of Fans. Noise and Vibration Control 31(2), 94–98 (2011)Google Scholar
  3. 3.
    Qiao, J., Pan, H.: Gear Fault Diagnosis Based on GA-Elman Neutral Network Model. Water Resource and Power 28(6), 106–108 (2010)Google Scholar
  4. 4.
    Wang, Y., Sun, X.F., Li, M.: Training Method for Support Vector Machine Based on Chaos Particle Swarm Optimization. Computer Engineering 36(23), 189–191 (2010)MathSciNetGoogle Scholar
  5. 5.
    Ji, A.-B., Pang, J.-H., Qiu, H.-J.: Support vector machine for classification based on fuzzy training data. Expert Systems with Applications 37(4), 3494–3498 (2010)CrossRefGoogle Scholar
  6. 6.
    Suykens, J.A.K., Vandewalle, J.: Least Squares Support Vector Machine Classifiers. Neural Processing Letters (S1370-4621) 9(3), 293–300 (1999)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Osuna, E., Freund, R., Girosi, F.: An Improved Training Algorithm for Support Vector Machine. In: Proceedings of the 1997 IEEE Workshop on Network for Signal Processing VII, Amelea Island, USA, pp. 276–285. IEEE, USA (1997)CrossRefGoogle Scholar
  8. 8.
    Yang, M.H., Ahuja, N.: Geometric Approach to Train Support Vector Machine. In: Proceeding of IEEE Computer Society Conference on Computer Vision an Pattern Recognition, Hilton Head, SC, USA, pp. 430–437. IEEE, USA (2000)Google Scholar
  9. 9.
    Liu, W.-L., Liu, S.-Y., Du, Z.: Sample Decreasing Method Based on Distance in SVM  23(3), 333–337 (2008)Google Scholar
  10. 10.
    Lin, C., Wang, S.D.: Fuzzy support vector machines. IEEE Transaction on Neural Network 13, 464–471 (2002)CrossRefGoogle Scholar
  11. 11.
    Lin, C., Wang, S.D.: Fuzzy Support Vector Machines with Automatic Membership Setting. In: Wang, L. (ed.) Support Vector Machines: Theory and Applications. STUD FUZZ, vol. 177, pp. 233–254. Springer, Heidelberg (2005)Google Scholar
  12. 12.
    Ha, M.-H., Huang, S., Wang, C., Wang, X.-L.: Intuitionistic Fuzzy Support Vector Machine. Journal of Hebei University 31(3), 225–229 (2011)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Bin Jiao
    • 1
    • 2
  • Qing Zhang
    • 1
    • 2
  1. 1.Shanghai DianJi UniversityShanghaiChina
  2. 2.East China University of Science and TechnologyShanghaiChina

Personalised recommendations