On the Fatigue Analysis of Non-Gaussian Stress Processes With Asymmetric Distribution

Abstract

This paper presents a multistage regression method to obtain an empirical joint probability density function (PDF) for rainflow amplitudes and means of non-Gaussian stress processes with asymmetric distribution. The proposed PDF model captures a wide range of non-Gaussian stress processes characterized by five parameters: Standard deviation, skewness, kurtosis, irregularity factor and mean frequency. It is shown that the fatigue prediction from the closed-form empirical PDF based on the multistage regression analysis agrees well with that from extensive Monte Carlo simulations. The ultimate purpose of the method is to provide a tool for engineers to predict the remaining fatigue life of a structure with direct measurements of stress responses at critical locations on a structure or stress responses at various hot-spots on a structure from the finite element analysis without conducting further Monte Carlo simulations.