Uncertainty Representation and Interpretation in Model-based Prognostics Algorithms based on Kalman Filter Estimation

This article discusses several aspects of uncertainty represen- tation and management for model-based prognostics method- ologies based on our experience with Kalman Filters when applied to prognostics for electronics components. In par- ticular, it explores the implications of modeling remaining useful life prediction as a stochastic process and how it re- lates to uncertainty representation, management, and the role of prognostics in decision-making. A distinction between the interpretations of estimated remaining useful life probability density function and the true remaining useful life probabil- ity density function is explained and a cautionary argument is provided against mixing interpretations for the two while considering prognostics in making critical decisions.