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

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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).

Description

EOL is predicted based on the esti- mated current state distribution of a component and ex- pected profiles of future usage. In general, this requires simulations of the component using the underlying mod- els.

In this paper, we develop a simulation-based pre- diction methodology that achieves computational effi- ciency by performing only the minimal number of sim- ulations needed in order to accurately approximate the mean and variance of the complete EOL distribution. This is performed through the use of the unscented trans- form, which predicts the means and covariances of a distribution passed through a nonlinear transformation.

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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 com- putational efficiency without sacrificing prediction accu- racy.

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