In prognostics and health management (PHM) of engineered systems, maintenance decisions are ideally informed by predictions of a system's remaining useful life (RUL) based on operational data. Model-based prognostics algorithms rely on a parametric model of the system degradation process. The model parameters are learned from real-time operational data collected on the system. However, there can be valuable information in data from similar systems or components, which is not typically utilized in PHM. In this contribution, we propose a hierarchical Bayesian modeling (HBM) framework for PHM that integrates both operational data and run-to-failure data from similar systems or components. The HBM framework utilizes hyperparameter distributions learned from data of similar systems or components as priors. It enables efficient updates of predictions as more information becomes available, allowing for increasingly accurate assessments of the degradation process and its associated variability. The effectiveness of the proposed framework is demonstrated through two experimental applications involving real-world data from crack growth and lithium battery degradation. Results show significant improvements in RUL prediction accuracy and demonstrate how the framework facilitates uncertainty management through predictive distributions.

翻译：在工程系统的预测与健康管理（PHM）中，维护决策的理想依据是基于运行数据对系统剩余使用寿命（RUL）的预测。基于模型的预测算法依赖于系统退化过程的参数化模型，其模型参数通过从系统收集的实时运行数据学习得到。然而，来自相似系统或部件的数据中可能存在有价值的信息，这些信息通常在PHM中未被充分利用。本研究提出了一种用于PHM的层次贝叶斯建模（HBM）框架，该框架整合了运行数据以及来自相似系统或部件的全寿命周期数据。HBM框架利用从相似系统或部件数据中学习得到的超参数分布作为先验分布，能够在获得更多信息时高效更新预测，从而实现对退化过程及其相关变异性的日益精确评估。通过涉及裂纹扩展和锂电池退化实际数据的两个实验应用，验证了所提框架的有效性。结果表明，该框架显著提升了RUL预测精度，并通过预测分布展示了其如何促进不确定性管理。