Structural Engineering and Mechanics

Techno-Press (테크노프레스)
권호 표지
DOI QR코드

DOI QR Code

Load spectra growth modelling and extrapolation with REBMIX

  • Volk, Matej (University of Ljubljana, Faculty of Mechanical Engineering) ;
  • Fajdiga, Matija (University of Ljubljana, Faculty of Mechanical Engineering) ;
  • Nagode, Marko (University of Ljubljana, Faculty of Mechanical Engineering)
  • Received : 2009.06.15
  • Accepted : 2009.09.23
  • Published : 2009.11.30
⟨ Previous Next ⟩

Abstract

In the field of predicting structural safety and reliability the operating conditions play an essential role. Since the time and cost limitations are a significant factors in engineering it is important to predict the future operating conditions as close to the actual state as possible from small amount of available data. Because of the randomness of the environment the shape of measured load spectra can vary considerably and therefore simple distribution functions are frequently not sufficient for their modelling. Thus mixed distribution functions have to be used. In general their major weakness is the complicated calculation of unknown parameters. The scope of the paper is to investigate the load spectra growth for actual operating conditions and to investigate the modelling and extrapolation of load spectra with algorithm for mixed distribution estimation, REBMIX. The data obtained from the measurements of wheel forces and the braking moment on proving ground is used to generate load spectra.

Keywords

References

  1. Akaike, H. (1974), 'A new look at statistical model identification', IEEE T. Automat. Contr., 19, 716-723 https://doi.org/10.1109/TAC.1974.1100705
  2. Amzallag, C., Gerey, J.P., Robert, J.L. and Bahuaud, J. (1994), 'Standardization of the rainflow counting method for fatigue analysis', Int. J. Fatigue, 16(4), 287-293 https://doi.org/10.1016/0142-1123(94)90343-3
  3. Balakrishnan, N. and David, H.A. (2001), 'A note on the variance of a lightly trimmed mean when multiple outliers are present in the sample', Stat. Probabil. Lett., 55(4), 339-343 https://doi.org/10.1016/S0167-7152(00)00238-8
  4. Bucar, T. (2006), Structural Reliability Modelling Depending on Operation Conditions. Thesis (PhD). University of Ljubljana, Faculty of Mechanical Engineering, Ljubljana
  5. Buxbaum, O. and Zaschel, J.M. (1979), Beschreibung Stochasticher Beanspruchungs-Zeit-Funktionen. Verhalten von Stahl bei schwingender Beanspruchung. Kontaktstudium Werkstoffkunde Eisen und Stahl III. Verein Deutscher Eisenhüttenleute. D$\ddot{u}$sseldorf, 208-222
  6. Ebeling, C.E. (1997), An Introduction to Reliability and Maintainability Engineering, McGraw-Hill, New York
  7. Farahmand, B., Bockrath, G. and Glassco, J. (1997), Fatigue and Fracture Mechanics of High Risk Parts, Chapman & Hall, New York
  8. Figueiredo, M.A.T. and Jain, A.K. (2002), 'Unsupervised learning of finite mixture models', IEEE T. Pattern Anal., 24(3), 381-396 https://doi.org/10.1109/34.990138
  9. Fonseca, J.R.S. (2008), 'The application of mixture modeling and information criteria for discovering patterns of coronary heart disease', J. Appl. Quantitative Meth., 3(4), 292-303
  10. Grubisic, V. and Fischer, G. (1997), 'Methodology for effective design evaluation and durability approval of car suspension components' SAE technical paper series 970094
  11. Johannesson, P. (2006), 'Extrapolation of load histories and spectra', Fatigue Fract. Eng. M., 29(3), 201-207 https://doi.org/10.1111/j.1460-2695.2005.00981.x
  12. Klemenc, J. and Fajdiga, M. (2006), 'Predicting smoothed loading spectra using a combined multilayer perceptron neural network', Int. J. Fatigue, 28(7), 777-791 https://doi.org/10.1016/j.ijfatigue.2005.08.004
  13. Nagode, M. and Fajdiga, M. (1998a), 'A general multi-modal probability density function suitable for the rainflow ranges of stationary random processes', Int. J. Fatigue, 20(3), 211-223 https://doi.org/10.1016/S0142-1123(97)00106-0
  14. Nagode, M. and Fajdiga, M. (1998b), “On a new method for prediction of the scatter of loading spectra”, Int. J. Fatigue, 20(4), 271-277 https://doi.org/10.1016/S0142-1123(97)00135-7
  15. Nagode, M. and Fajdiga, M. (1999), 'The influence of variable operating conditions upon the general multimodal Weibull distribution', Reliab. Eng. Syst. Safe, 64(3), 383-389 https://doi.org/10.1016/S0951-8320(98)00085-4
  16. Nagode, M. and Fajdiga, M. (2000), 'An improved algorithm for parameter estimation suitable for mixed Weibull distributions', Int. J. Fatigue, 22(1), 75-80 https://doi.org/10.1016/S0142-1123(99)00112-7
  17. Nagode, M., Klemenc, J. and Fajdiga, M. (2001), 'Parametric modelling and scatter prediction of rainflow matrices', Int. J. Fatigue, 23(6), 525-532 https://doi.org/10.1016/S0142-1123(01)00007-X
  18. Nagode, M. and Fajdiga, M. (2006), 'An alternative perspective on the mixture estimation problem', Reliab. Eng. Syst. Safe, 91(4), 388-397 https://doi.org/10.1016/j.ress.2005.02.005
  19. Schimek, M.G. (2000), Smoothing and Regression: Approaches, Computation and Application, Wiley, New York
  20. Socie, D. (2001), 'Modelling expected service usage from short-term loading measurements', Int. J. Mater. Prod. Tech., 16(4), 295-303
  21. Stehlík, M. (2006), 'Exact likelihood ratio scale and homogeneity testing of some loss processes', Stat. Probabil. Lett. , 76(1), 19-26 https://doi.org/10.1016/j.spl.2005.06.005
  22. Stehlík, M. (2008), 'Homogeneity and scale testing of generalized gamma distribution', Reliab. Eng. Syst. Safe, 93(12), 1809-1813 https://doi.org/10.1016/j.ress.2008.03.012
  23. Tovo, R. (2000), 'A damage-based evaluation of probability density distribution for rain-flow ranges from random processes', Int. J. Fatigue, 22(5), 425-429 https://doi.org/10.1016/S0142-1123(00)00006-2
  24. Zhao, W. and Baker, M.J. (1992), 'On the probability density function of rainflow stress range for stationary Gaussian processes', Int. J. Fatigue, 14(2), 121-135 https://doi.org/10.1016/0142-1123(92)90088-T

Cited by

  1. Fatigue life prediction of multiple site damage based on probabilistic equivalent initial flaw model vol.38, pp.4, 2009, https://doi.org/10.12989/sem.2011.38.4.443