A framework for probabilistic prediction of remaining useful life in structural materials
For engineers designing high-temperature components, this framework improves reliability by accounting for data variability, but the approach is incremental and domain-specific.
This paper introduces a probabilistic framework for predicting remaining useful life under creep conditions, using robust regression, sensitivity analysis, and Monte Carlo simulation to quantify uncertainties. The framework enables definition of safe operational limits with quantifiable confidence levels.
Accurate prediction of remaining useful life under creep conditions is essential for the structural reliability of high-temperature components in critical engineering systems. Traditional approaches based on deterministic parametric models often overlook the substantial variability inherent in experimental data, compromising the accuracy and robustness of long-term predictions. This study introduces a probabilistic framework to quantify uncertainties in creep rupture time prediction. Robust regression techniques are first applied to mitigate the influence of outliers and enhance the stability of model estimates. Global sensitivity analysis using Sobol indices is then employed to identify the dominant contributors to model uncertainty, followed by Monte Carlo simulations to propagate these uncertainties and estimate the distribution of the remaining useful life. Finally, model selection is guided by statistical criteria, including the Akaike and Bayesian information criteria, to identify the most reliable predictive model. The proposed framework not only enables the definition of safe operational limits with quantifiable confidence levels but is also general and extensible to other time-dependent degradation phenomena, such as fatigue and creep-fatigue interaction.