Application of Pearson Parametric Distribution Model in Fatigue Life Reliability Evaluation
The aim of this paper is to introduce a parametric
distribution model in fatigue life reliability analysis dealing with
variation in material properties. Service loads in terms of responsetime
history signal of Belgian pave were replicated on a multi-axial
spindle coupled road simulator and stress-life method was used to
estimate the fatigue life of automotive stub axle. A PSN curve was
obtained by monotonic tension test and two-parameter Weibull
distribution function was used to acquire the mean life of the
component. A Pearson system was developed to evaluate the fatigue
life reliability by considering stress range intercept and slope of the
PSN curve as random variables. Considering normal distribution of
fatigue strength, it is found that the fatigue life of the stub axle to
have the highest reliability between 10000 – 15000 cycles. Taking
into account the variation of material properties associated with the
size effect, machining and manufacturing conditions, the method
described in this study can be effectively applied in determination of
probability of failure of mass-produced parts.
[1] R.I. Stephens, A. Fatemi, R.R. Stephens and H.O. Fuchs, Metal Fatigue
in Engineering, 2nd Edition, Wiley Interscience, New York, 2001.
[2] A guide for fatigue testing and statistical analysis of fatigue data.
American Society for Testing and Materials, Philadelphia, ASTM STP
No. 91-A; 1963
[3] J.D. Booker, M. Raines and K.G. Swift, Designing Capable and
Reliable Products, Butterworth-Heinemann, Oxford, 2001.
[4] P.W. Hovey, A.P. Berens and D.A. Skinn, "Risk analysis for aging
aircraft", Flight Dynamic Directorate, vol. 1, Wright Laboratory, Ohio,
October 1991.
[5] R.E. Melchers, Structural Reliability Analysis and Prediction, 2nd
Edition, John Wiley & Sons Ltd, Chichester, 1999
[6] J. Schijve, "Statistical distribution functions and fatigue of structures",
International Journal of Fatigue, vol. 7, no. 9, pp. 1031-1039, 2005.
[7] K.J. Jun, T.W. Park, S.H. Lee, S.P. Jung and J.W. Yoon, "Prediction of
fatigue life and estimation of its reliability on the parts of an air
suspension system", International Journal of Automotive Technology,
vol. 9, no. 6, pp. 741-747, 2008.
[8] J.A. Bannantine, J.J. Comer and J.L. Handrock, Fundamentals of Metal
Fatigue Analysis, Prentice Hall, New Jersey, 1989.
[9] B. Sudret, Z. Guede, P. Hornet, J. Stephan and M. Lemaire,
"Probabilistic assessment of fatigue life including statistical
uncertainties in the SN curve", in Transactions of the 17th International
Conference on Structural Mechanics in Reactor Technology, Prague,
Czech Republic, August 2003.
[10] G. Genet, A Statistical Approach to Multi-Input Equivalent Fatigue
Loads for the Durability of Automotive Structures, Chalmers University
of Technology and Goteborg University, Goteborg, Sweden, 2006.
[11] G.J. Hahn and S.S. Shapiro, Statistical Models in Engineering, John
Wiley and Sons, New York, 1967.
[1] R.I. Stephens, A. Fatemi, R.R. Stephens and H.O. Fuchs, Metal Fatigue
in Engineering, 2nd Edition, Wiley Interscience, New York, 2001.
[2] A guide for fatigue testing and statistical analysis of fatigue data.
American Society for Testing and Materials, Philadelphia, ASTM STP
No. 91-A; 1963
[3] J.D. Booker, M. Raines and K.G. Swift, Designing Capable and
Reliable Products, Butterworth-Heinemann, Oxford, 2001.
[4] P.W. Hovey, A.P. Berens and D.A. Skinn, "Risk analysis for aging
aircraft", Flight Dynamic Directorate, vol. 1, Wright Laboratory, Ohio,
October 1991.
[5] R.E. Melchers, Structural Reliability Analysis and Prediction, 2nd
Edition, John Wiley & Sons Ltd, Chichester, 1999
[6] J. Schijve, "Statistical distribution functions and fatigue of structures",
International Journal of Fatigue, vol. 7, no. 9, pp. 1031-1039, 2005.
[7] K.J. Jun, T.W. Park, S.H. Lee, S.P. Jung and J.W. Yoon, "Prediction of
fatigue life and estimation of its reliability on the parts of an air
suspension system", International Journal of Automotive Technology,
vol. 9, no. 6, pp. 741-747, 2008.
[8] J.A. Bannantine, J.J. Comer and J.L. Handrock, Fundamentals of Metal
Fatigue Analysis, Prentice Hall, New Jersey, 1989.
[9] B. Sudret, Z. Guede, P. Hornet, J. Stephan and M. Lemaire,
"Probabilistic assessment of fatigue life including statistical
uncertainties in the SN curve", in Transactions of the 17th International
Conference on Structural Mechanics in Reactor Technology, Prague,
Czech Republic, August 2003.
[10] G. Genet, A Statistical Approach to Multi-Input Equivalent Fatigue
Loads for the Durability of Automotive Structures, Chalmers University
of Technology and Goteborg University, Goteborg, Sweden, 2006.
[11] G.J. Hahn and S.S. Shapiro, Statistical Models in Engineering, John
Wiley and Sons, New York, 1967.
@article{"International Journal of Mechanical, Industrial and Aerospace Sciences:63212", author = "E. A. Azrulhisham and Y. M. Asri and A. W. Dzuraidah and A. H. Hairul Fahmi", title = "Application of Pearson Parametric Distribution Model in Fatigue Life Reliability Evaluation", abstract = "The aim of this paper is to introduce a parametric
distribution model in fatigue life reliability analysis dealing with
variation in material properties. Service loads in terms of responsetime
history signal of Belgian pave were replicated on a multi-axial
spindle coupled road simulator and stress-life method was used to
estimate the fatigue life of automotive stub axle. A PSN curve was
obtained by monotonic tension test and two-parameter Weibull
distribution function was used to acquire the mean life of the
component. A Pearson system was developed to evaluate the fatigue
life reliability by considering stress range intercept and slope of the
PSN curve as random variables. Considering normal distribution of
fatigue strength, it is found that the fatigue life of the stub axle to
have the highest reliability between 10000 – 15000 cycles. Taking
into account the variation of material properties associated with the
size effect, machining and manufacturing conditions, the method
described in this study can be effectively applied in determination of
probability of failure of mass-produced parts.", keywords = "Stub axle, Fatigue life reliability, Stress-life, PSN
curve, Weibull distribution, Pearson system", volume = "4", number = "11", pages = "1307-6", }