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Stochastic Modelling of Fatigue: Methodical Background

  • K. Sobczyk
Part of the International Centre for Mechanical Sciences book series (CISM, volume 334)

Abstract

Fatigue of engineering materials is a complicated and intriguing phenomenon that takes place in components and structures subjected to time — varying external loadings and that manifests itself in the deterioration of the material’s ability to carry the intended loadings.

Keywords

Stochastic Process Stochastic Modelling Stochastic Differential Equation Wiener Process Sample Function 
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References

  1. 1.
    Wigner E., The Unreasonable Effectiveness of Mathematics in the Natural Sciences, Comm. Pure Appl. Math. Vol 13, 1, 1960, 1–14.MATHGoogle Scholar
  2. 2.
    Schlegel R. “Do We Know The World Through Science” in: Mind, Science and History (Eds. H. E. Kiefer, M. K. Munitz ), State Univ. of N. York Press, Albany, 1970.Google Scholar
  3. 3.
    Blekhman I. I, Myshkis A. D., Panovoko J.G., Mechanics and Applied Mathematics; Logic and Features of Mathematical Applications (in Russian), Moskow, Nauka 1983.Google Scholar
  4. 4.
    Bunge M., Theory Meets Experience in: Mind, Science and History (Eds. Kiefer H. E., Munitz M.K. ), State Univ. of N. York, Albany, New York, 1970.Google Scholar
  5. 5.
    Sobczyk K., Spencer B. F., Random Fatigue: Form Data to Theory, Academic Press, Boston, N. York, 1992.Google Scholar
  6. 6.
    Goodwin G. C., Payne R. I., Dynamic System Identification: Experiment Design and Data Analysis, Acad. Press, N. York, 1977.Google Scholar
  7. 7.
    Littlewood J. E., Dillema of Probability Theory, in: A Mathematical Miscellany, London 1957.Google Scholar
  8. 8.
    Nalimov V., Faces of Science, ISI Press, 1981.Google Scholar
  9. 9.
    Fine T. L., Theories of Probability. An Examination of Foundations, Acad. Press, 1973.MATHGoogle Scholar
  10. 10.
    Csiszar L, Information — Type Distance Measures and Indirect Observations, Studia Scient. Math. Hungarica, Vol. 2, 299–318, 1967.MathSciNetMATHGoogle Scholar
  11. 11.
    Kozin F., Bogdanoff J. L., Role of Third Order Statistics in Discriminating among Models of Fatigue Crack Growth, Metallurgical Trans. A, Vol. 18 A, N. 11, Nov. 1987.Google Scholar
  12. 12.
    Ang A.H-S, Tang W.H., Probability Concepts in Engineering Planning and Design, vol. I. Basic Principles, J. Willey & Sons, New York, 1975.Google Scholar
  13. 13.
    Soong T.T., Probabilistic Modelling and Analysis in Science and Engineering, J. Willey Si Sons, New York, 1981.Google Scholar
  14. 14.
    Cox D.R., Miller H.D., The Theory of Stochastic Processes, Chapman & Hall, London, 1977.MATHGoogle Scholar
  15. 15.
    Gumbel E.J., Statistic of Extremes, Columbia Univ. Press, New York, 1958.Google Scholar
  16. 16.
    Cramer H., Leadbetter M.R., Stationary and Related Stochastic Processes, Willey, New York, 1967.MATHGoogle Scholar
  17. 17.
    Sobczyk K., Stochastic Differential Equations with Applications to Physics and Engineering, Kluwer Acad. Publ., Dordrecht, Boston, 1991.Google Scholar

Copyright information

© Springer-Verlag Wien 1993

Authors and Affiliations

  • K. Sobczyk
    • 1
  1. 1.Polish Academy of SciencesWarsawPoland

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