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

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Jose ́ R. Celaya Abhinav Saxena Kai Goebel

Abstract

This article discusses several aspects of uncertainty representation and management for model-based prognostics method- ologies based on our experience with Kalman Filters when applied to prognostics for electronics components. In particular, it explores the implications of modeling remaining useful life prediction as a stochastic process and how it relates 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 probability density function is explained and a cautionary argument is provided against mixing interpretations for the two while considering prognostics in making critical decisions.

How to Cite

R. Celaya . J. ́., Saxena, A., & Goebel, K. . (2012). Uncertainty Representation and Interpretation in Model-based Prognostics Algorithms based on Kalman Filter Estimation. Annual Conference of the PHM Society, 4(1). https://doi.org/10.36001/phmconf.2012.v4i1.2110
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Technical Papers

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