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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.
Complete Metadata
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| description | 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. |
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| identifier | DASHLINK_789 |
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| modified | 2025-03-31 |
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| title | Uncertainty Representation and Interpretation in Model-based Prognostics Algorithms based on Kalman Filter Estimation |
