The concise description of one- and multidimensional stationary and non stationary 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
and variance content. The stationary variations are represented by a class of time series
referred to as Autoregressive Moving Average (ARMA) models, while a Fourier series is
used to account for the variation of the mean and variance. Due to the use of random
phase angles in the Fourier series, an ensemble of mean and variance variations is
obtained. The methods of nonparametric statistics are used to determine the success of
the modeling of nonstationarity. 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. The method can be used
in a diversity of fields where a concise representation of random loadings is desired.
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