LAUSR

Fatigue life analysis is an important work in manufacturing of vehicle systems. The traditional method is to assume that stochastic loads are Gaussian type, then fatigue life is calculated by rain-flow counting, S-N curve and Miner linear damage rule. However, it is difficult to acquire accurate results by this means. In this paper, a numerical methodology is used to simulate non-Gaussian loads considering effects of skewness and kurtosis, as well as to estimate fatigue life under non-Gaussian stresses. Firstly, non-Gaussian inputs are represented by polynomial chaos expansion (PCE) and Karhunen-Loeve (KL) expansion when they are characterised using first four moments, i.e. mean, variance, skewness, kurtosis and a given correlation structure. During this process, we propose spectral decomposition to eliminate the influence of potential imaginary numbers, principal component analysis is also proposed to simplify calculating procedure in KL. Besides, original Monte Carlo sampling is replaced by quasi Monte-Carlo (QMC), which could greatly reduce the workload of numerical simulations. In order to get first four moments and correlation structure of outputs, differential equations of motion are numerically integrated by Runge-Kutta method. Meanwhile, response trajectories are represented based on PCE-KL-QMC approach. Eventually, the rain-flow counting is applied into these trajectories to obtain fatigue life variables, and a convenient formula about the saddlepoint approximations (SPA) represented by first four moments is proposed to provide fatigue life PDF. According to the above way, accurate and effective fatigue life estimation results can be presented