  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.
