Online remaining-useful-life (RUL) estimation is an effective method with respect to ensuring the safety of complex-huge systems. Generally, current methods assume a specific degradation model when degradation values are observed in the initial degradation phase. However, this assumption may not always be robust enough owing to the often-ambiguous inherent incipient-degradation characteristic. Therefore, besides model-parameter uncertainty, the uncertainty of the degradation model is worth examining in online RUL estimations. In this paper, a Bayesian-updated expectation-conditional-maximization (ECM) algorithm is adopted to address the uncertainty of prior parameters, and a modified Bayesian-model-averaging method is used to deal with the uncertainty of the degradation model. Then, simulation studies are conducted to analyze the effectiveness of the proposed fusion algorithm. Results suggest that the Bayesian-updated ECM algorithm and modified Bayesian-model-averaging method effectively address the associated uncertainties of model parameters and the degradation model itself. Finally, we apply the proposed fusion algorithm to predict the RUL of a gyroscope.

在线剩余使用寿命（RUL）估计是确保复杂大型系统安全的有效方法。通常，当在初始降解阶段观察到降解值时，当前方法采用特定的降解模型。但是，由于固有的固有初始降解特性通常不明确，因此该假设可能并不总是足够稳健。因此，除了模型参数不确定性外，退化模型的不确定性还值得在在线RUL估计中进行研究。本文采用贝叶斯更新期望条件最大化算法解决先验参数的不确定性，采用改进的贝叶斯模型平均法处理退化模型的不确定性。然后，仿真研究用于分析所提出的融合算法的有效性。结果表明，贝叶斯更新的ECM算法和改进的贝叶斯模型平均方法可以有效解决模型参数和降级模型本身相关的不确定性。最后，我们将提出的融合算法应用于陀螺仪的RUL预测。