Improving Computational Efficiency of Prediction in Model-based Prognostics Using the Unscented Transform

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Matthew Daigle Kai Goebel

Abstract

Model-based prognostics captures system knowledge in the form of physics-based models of components, and how they fail, in order to obtain accurate predictions of end of life (EOL). EOL is predicted based on the estimated current state distribution of a component and expected profiles of future usage. In general, this requires simulations of the component using the underlying models. In this paper, we develop a simulation-based prediction methodology that achieves computational efficiency by performing only the minimal number of simulations needed in order to accurately approximate the mean and variance of the complete EOL distribution. This is performed through the use of the unscented transform, which predicts the means and covariances of a distribution passed through a nonlinear transformation. In this case, the EOL simulation acts as that nonlinear transformation. In this paper, we review the unscented transform, and describe how this concept is applied to efficient EOL prediction. As a case study, we develop a physics-based model of a solenoid valve, and perform simulation experiments to demonstrate improved computational efficiency without sacrificing prediction accuracy

How to Cite

Daigle, . M. ., & Goebel , K. . (2010). Improving Computational Efficiency of Prediction in Model-based Prognostics Using the Unscented Transform. Annual Conference of the PHM Society, 2(1). https://doi.org/10.36001/phmconf.2010.v2i1.1902
Abstract 146 | PDF Downloads 18

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Keywords

model-based prognostics, particle filters, unscented transform, solenoid valve

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Technical Papers

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