The Research on System Reliability in Complex External Conditions Based on SVM

  • Yi Wan
  • Yue Xu
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 143)


Analysis method based on support vector machine and finite element combined with Monte Carlo is applied for the parts in complex external conditions or surroundings, it is difficult to built reliability model of the parts in complex the external conditions or surroundings and it is difficult to establish stress and intention distribution and joint probability density because they work in an uncertain environment, the support vector machine has a good generalization ability prediction ability, integration algorithm based on support vector machine, finite element and Monte Carlo can solve the questions and can excellently use for reliability simulation and calculation for complex and certain system. It is used for reliability analysis of catenary parts in the high-speed electrified railway, integration algorithm mathematic model of reliability analysis for location hook is built, and the outside parameter influence on wrist-arm of location hook is analyzed by the model.


Integration algorithm support vector machine location hook complex the external conditions or surroundings Reliability analysis catenary 


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  1. 1.
    Yu, W.J.: High speed electrization railway catenary. Southwest Jiaotong University Press, Chengdu (2002)Google Scholar
  2. 2.
    Zhang, J.R.: Structure reliability theory and application for bridge engineering. Public Jiaotong press, Beijing (2003)Google Scholar
  3. 3.
    Wan, Y.: Connecting bolt reliability analysis based on finite element and machine learning theory. In: 2010 Second ETP/IITA World Congress in Applied Computing, Computer Science, and Computer Engineering/2010 ACC, pp. 323–326. IEEE Computer Society, Los Alamitos (2010)Google Scholar
  4. 4.
    Zhang, Y.G.: Reliability simulation and analysis of messenger wire bearing on electrified railways. In: International Conference on Optics, Photonics and Energy Engineering, vol. (1), pp. 184–187 (2010)Google Scholar
  5. 5.
    Yang, S.K.: Simulation method of structure reliability based on artificial neural network. Mechanical Intension 21(4), 12–16 (2004)Google Scholar
  6. 6.
    Xiao, R., Wang, J.C., Sun, Z.X., et al.: An approach to incremental SVM learning algorithm. Journal of Nanjing University (Natural Sciences) 38(2), 152–157 (2002)Google Scholar
  7. 7.
    Guo, C.D., Li, S.Z.: Control - based Audio Classification and Retrieval by Support Vector Machines. IEEE Trans. on Neural Network 14(1), 115–209 (2003)Google Scholar
  8. 8.
    Cristianini, N., Shawe-Taylor, J.: An Introduction to Support Vector Machines and Other Kernel-based Learning Methods. University Press, Cambridge (2000)CrossRefzbMATHGoogle Scholar
  9. 9.
    Vapnik, V.N.: Statistical Learning Theory. Springer, New York (2000)zbMATHGoogle Scholar
  10. 10.
    Kiyohiro.: Reliability analysis of geometrically nonlinear structures with application to suspension bridges, Dissertation Abstracts International. The University of Michigan, Ann Arbor (1999) Google Scholar
  11. 11.
    Yang, Z.J.: Intensity Analysis of Parts of the Middle Catenary Supporting. Southwest Jiaotong university, Chengdu (2006)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Yi Wan
    • 1
  • Yue Xu
    • 1
  1. 1.College of Physics and Electronic Information EngineeringWenzhou UniversityWenzhouChina

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