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.

翻译：准确预测蠕变条件下的剩余寿命对于关键工程系统中高温部件的结构可靠性至关重要。基于确定性参数模型的传统方法往往忽略实验数据中固有的显著变异性，从而损害长期预测的准确性和稳健性。本研究提出了一种概率框架，用于量化蠕变断裂时间预测中的不确定性。首先应用稳健回归技术来减轻异常值的影响并增强模型估计的稳定性。随后采用基于Sobol指数的全局敏感性分析来识别模型不确定性的主要贡献因素，并通过蒙特卡洛模拟传播这些不确定性，估算剩余寿命的分布。最后，根据统计准则（包括Akaike和贝叶斯信息准则）指导模型选择，以确定最可靠的预测模型。该框架不仅能够以可量化的置信水平定义安全运行极限，而且具有通用性和可扩展性，可应用于其他时间依赖的退化现象，如疲劳和蠕变-疲劳交互作用。