MODELING OF RANDOM VEHICLE LOADING HISTORIES FOR FATIGUE ANALYSIS

The concise description of one dimensional vehicle loading histories f or fatigue analysis using stochastic proces theory is presented in thi s study. The load history is considered to have stationary random and nonstationary mean content. The stationary variations are modelled 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 p hase angles in Fourier series an ensemble of mean variations is obtain ed. 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 re sulting fatigue lives of original and simulated loadings. Due to the r elatively small number of Fourier coefficients needed together with th e use of ARMA models, a concise description of complex loadings is ach ieved. The overall frequency content and sequential information of the load history is statistically preserved. An ensemble of load historie s can be constructed on-line with minimal computer storage capacity as used in testing equipment.