The risk of fracture associated with high energy rotating components in aircraft gas turbine engines can be sensitive to small changes in applied stress values which are often difficult to measure and predict. Although a parametric approach is often used to characterize random variables, it is difficult to apply to multimodal densities. Nonparametric methods provide a direct fit to the data, and can be used to estimate the multimodal densities often associated with rainflow stress data. In this paper, a comparison of parametric and nonparametric methods is presented for density estimation of rainflow stress profiles associated with military aircraft gas turbine engine usages. A nonparametric adaptive kernel density estimator algorithm is illustrated for standard parametric probability density functions and for rainflow stress pairs associated with F-16/F100 engine usages. The kernel estimates are compared to parametric estimates, including a hybrid approach based on separate treatment of maximum stress pairs. The results provide some insight regarding the strengths and weaknesses of parametric and nonparametric density estimation methods for gas turbine engines, and can be used to develop improved stress estimates for probabilistic life predictions.

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