The concise description of one dimensional vehicle loading histories for fatigue analysis using stochastic process theory is presented in this study. The load history is considered to have stationary random and nonstationary mean content. The stationary variations aremodelled by an Autoregressive Moving Average (ARMA) model, while a Fourier series is used to model the variation of the mean. Due to the use of random phase angles in the Fourier series an ensemble of mean variations is obtained. The methods of nonparametric statistics are used to determine the success of the modelling of nonstationary. Justification of the method is obtained through comparison of rainflow cycle distributions and resulting fatigue lives of original and simulated loadings. Due to the relatively small number of Fourier coefficients needed together with the use of ARMA models, a concise description of complex loadings is achieved. The overall frequency content and sequential information of the load history is statistically preserved. An ensemble of load histories can be constructed on-line with minimal computer storage capacity as used in testing equipment.(A)
Abstract