Abstract
Prognostic models, when feasible, are favored for avoiding unexpected maintenance. There is a need for a common language when discussing prognostic performance and behavior. The approach presented here considers model behavior in terms of two optimizable sub-problems for better performance assessment. The first evaluation construct considers how well the model tracks degradation over time and a second construct considers how effectively it improves operations. The right set of cost functions can determine the suitability to both objectives. The combined construct enables evaluation of a class of models which augment degradation physics with data-driven heuristics, supporting a more explainable recommendation.
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