This paper addresses the benefits of pooling data for shared learning in maintenance operations. We consider a set of systems subject to Poisson degradation that are coupled through an a-priori unknown rate. Decision problems involving these systems are high-dimensional Markov decision processes (MDPs). We present a decomposition result that reduces such an MDP to two-dimensional MDPs, enabling structural analyses and computations. We leverage this decomposition to demonstrate that pooling data can lead to significant cost reductions compared to not pooling.

翻译：本文探讨了在维护操作中通过数据池化实现共享学习的优势。我们考虑一组服从泊松退化过程的系统，这些系统通过未知的先验率相互耦合。涉及这些系统的决策问题是高维马尔可夫决策过程（MDPs）。我们提出了一种分解结果，将该高维MDP简化为二维MDP，从而支持结构性分析与计算。我们利用这一分解证明，与不进行数据池化相比，数据池化可显著降低运营成本。