Prognostic Health Management aims to predict the Remaining Useful Life (RUL) of degrading components/systems utilizing monitoring data. These RUL predictions form the basis for optimizing maintenance planning in a Predictive Maintenance (PdM) paradigm. We here propose a metric for assessing data-driven prognostic algorithms based on their impact on downstream PdM decisions. The metric is defined in association with a decision setting and a corresponding PdM policy. We consider two typical PdM decision settings, namely component ordering and/or replacement planning, for which we investigate and improve PdM policies that are commonly utilized in the literature. All policies are evaluated via the data-driven estimation of the long-run expected maintenance cost per unit time, relying on available monitoring data from run-to-failure experiments. The policy evaluation enables the estimation of the proposed metric. The latter can further serve as an objective function for optimizing heuristic PdM policies or algorithms' hyperparameters. The effect of different PdM policies on the metric is initially investigated through a theoretical numerical example. Subsequently, we employ four data-driven prognostic algorithms on a simulated turbofan engine degradation problem, and investigate the joint effect of prognostic algorithm and PdM policy on the metric, resulting in a decision-oriented performance assessment of these algorithms.

翻译：预测与健康管理旨在利用监测数据预测退化部件/系统的剩余使用寿命。这些剩余使用寿命预测构成了在预测性维护范式下优化维护规划的基础。本文提出了一种基于下游预测性维护决策影响的数据驱动预测算法评估指标。该指标与特定的决策场景及其对应的预测性维护策略相关联。我们考虑了两种典型的预测性维护决策场景，即部件订购和/或更换规划，并针对文献中常用的预测性维护策略进行了研究与改进。所有策略均通过数据驱动方法，基于运行至失效实验的可用监测数据，估计单位时间的长期预期维护成本。策略评估使得所提出指标的估算成为可能，后者可进一步作为优化启发式预测性维护策略或算法超参数的目标函数。我们首先通过一个理论数值示例研究了不同预测性维护策略对指标的影响。随后，在模拟涡扇发动机退化问题上采用了四种数据驱动预测算法，并联合考察了预测算法与预测性维护策略对该指标的影响，从而实现了对这些算法的面向决策的性能评估。