A modification in Weibull parameters to achieve a more accurate probability distribution function in fatigue applications

Abstract

Risk evaluation for fatigue failure of the engineering components is an important aspect of the engineering design. Weibull distributions are often used in preference to the log-normal distribution to analyze probability aspects of fatigue results. This study presents a probabilistic model for calculating Weibull distribution parameters to reduce the effect of percentage discretization error of experimental fatigue life and R–S–N curves for three reliability levels. By considering any result of standard fatigue test as an equivalent Weibull distribution, artificial data are generated and the accuracy of common Weibull distribution model can be improved. The results show error reduction in the Kolmogorov–Smirnov test and R-square values. Also, the Basquin model is used for different reliability levels with the same error order for risk evaluation of fatigue failure. The coefficient of variation for fatigue life increases at higher stress levels and has a linear relation with stress level for a high-cycle fatigue regime.

A new probabilistic model: Theory, simulation and applications to sports and failure times data

“…The Weibull distribution has certain advantages over the other classical distributions because of its different monotonic hazard shapes and closed-form distribution function. For reliability and healthcare data analyses, most often the utilization/employment of the Weibull model is considered [12] , [13] , [14] , [15] , [16] , [17] , [18] , [19] .…”