Advanced Methods for Determining Prediction Uncertainty in Model-Based Prognostics with Application to Planetary Rovers
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Abstract
Prognostics is centered on predicting the time of and time un- til adverse events in components, subsystems, and systems. It typically involves both a state estimation phase, in which the current health state of a system is identified, and a pre- diction phase, in which the state is projected forward in time. Since prognostics is mainly a prediction problem, prognostic approaches cannot avoid uncertainty, which arises due to several sources. Prognostics algorithms must both characterize this uncertainty and incorporate it into the predictions so that informed decisions can be made about the system. In this paper, we describe three methods to solve these problems, including Monte Carlo-, unscented transform-, and first-order reliability-based methods. Using a planetary rover as a case study, we demonstrate and compare the different methods in simulation for battery end-of-discharge prediction.
How to Cite
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model-based prognostics, uncertainty estimation, input uncertainty, planetary rover
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