Capturing Uncertainty in Fatigue Life Data.

摘要

Time-to-failure (TTF) data, also referred to as life data, are investigated across a wide range of scientific disciplines and collected mainly through scientific experiments with the main objective of predicting performance in service conditions. Fatigue life data are times, measured in cycles, until complete fracture of a material in response to a cyclical loading. Fatigue life data have large variation, which is often overlooked or not rigorously investigated when developing predictive life models. This research develops a statistical model to capture dispersion in fatigue life data which can be used to extend deterministic life models into probabilistic life models. Additionally, a predictive life model is developed using failure- time regression methods. The predictive life and dispersion models are investigated as dual-response using nonparametric methods. After model adequacy is examined, a Bayesian extension and other applications of this model are discussed.