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：在关键工程系统中，高温构件的结构可靠性评估依赖于蠕变条件下剩余使用寿命的精确预测。基于确定性参数模型的传统方法常忽略实验数据固有的显著变异性，从而影响长期预测的准确性与鲁棒性。本研究提出一种概率框架以量化蠕变断裂时间预测中的不确定性。首先采用稳健回归技术抑制异常值影响并提升模型估计的稳定性；继而通过基于Sobol指数的全局敏感性分析识别模型不确定性的主要来源，并利用蒙特卡洛模拟传播这些不确定性以估计剩余使用寿命的分布特征；最后基于赤池信息准则和贝叶斯信息准则等统计指标指导模型选择，从而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界限，还具有普适性，可扩展至疲劳及蠕变-疲劳交互作用等其他时间相关退化现象的分析。