Title: Fusion of heterogeneous data for robust degradation prognostics Show Chinese title

Title: 异构数据融合用于稳健的退化预测

Abstract: Assessing the degradation state of an industrial asset first requires evaluating its current condition and then to project the forecast model trajectory to a predefined prognostic threshold, thereby estimating its remaining useful life (RUL). Depending on the available information, two primary categories of forecasting models may be used: physics-based simulation codes and datadriven (machine learning) approaches. Combining both modelling approaches may enhance prediction robustness, especially with respect to their individual uncertainties. This paper introduces a methodology for fusion of heterogeneous data in degradation prognostics. The proposed approach acts iteratively on a computer model's uncertain input variables by combining kernel-based sensitivity analysis for variable ranking with a Bayesian framework to inform the priors with the heterogeneous data. Additionally, we propose an integration of an aggregate surrogate modeling strategy for computationally expensive degradation simulation codes. The methodology updates the knowledge of the computer code input probabilistic model and reduces the output uncertainty. As an application, we illustrate this methodology on a toy model from crack propagation based on Paris law as well as a complex industrial clogging simulation model for nuclear power plant steam generators, where data is intermittently available over time. Show Chinese abstract

Abstract: 评估工业资产的退化状态首先需要评估其当前状况，然后将预测模型轨迹投影到预定义的预测阈值，从而估计其剩余使用寿命（RUL）。 根据可用的信息，可以使用两种主要的预测模型类别：基于物理的仿真代码和数据驱动（机器学习）方法。 结合这两种建模方法可以提高预测的鲁棒性，尤其是相对于它们各自的不确定性而言。 本文介绍了一种在退化预测中融合异构数据的方法。 所提出的方法通过将基于核的敏感性分析用于变量排序，并与贝叶斯框架结合，以异构数据为先验提供信息，从而对计算机模型的不确定输入变量进行迭代处理。 此外，我们提出了一种集成代理建模策略，用于计算成本高昂的退化仿真代码。 该方法更新了计算机代码输入概率模型的知识并减少了输出不确定性。 作为应用，我们在一个基于巴黎定律的裂纹扩展玩具模型以及一个用于核电站蒸汽发生器的复杂堵塞仿真模型上说明了该方法，其中数据是随时间间歇性可用的。