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-based estimation of the long-run expected maintenance cost per unit time, using monitored run-to-failure experiments. The policy evaluation enables the estimation of the proposed metric. We employ the metric as an objective function for optimizing heuristic PdM policies and 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：预测健康管理旨在利用监测数据预测退化组件/系统的剩余使用寿命（RUL）。这些RUL预测为预测性维护（PdM）范式下的维护计划优化奠定了基础。本文提出了一种基于下游PdM决策影响来评估数据驱动预测算法的度量标准。该度量标准与决策设置及相应的PdM策略相关联。我们考虑两种典型的PdM决策设置，即组件订购和/或更换规划，并对文献中常用的PdM策略进行了研究及改进。所有策略均通过基于数据的单位时间长期预期维护成本估计进行评估，利用监测的故障实验数据。策略评估使得所提出的度量标准得以估算。我们将该度量标准作为优化启发式PdM策略和算法超参数的目标函数。通过理论数值示例初步研究了不同PdM策略对度量标准的影响。随后，在模拟涡扇发动机退化问题上应用四种数据驱动预测算法，研究预测算法与PdM策略对度量标准的联合效应，从而对这些算法进行了面向决策的性能评估。