Bayesian remaining useful lifetime prediction of thermally aged power MOSFETs

The demand for more reliable power conversion is ever increasing. This necessitates smart gate drivers or smart system controllers that monitor the components that are susceptible to failure. Together with the electrolytic capacitors, power semiconductor devices are among the weakest components in a power converter. In an effort to predict power MOSFET aging, this paper proposes an remaining useful lifetime (RUL) estimation algorithm for degraded power MOSFETs, which are exposed to high amplitude thermal cycles. The relative change in on-state resistance is identified as the fault signature. A data-driven RUL estimation algorithm based on a linear model approximation is proposed. The outliers present particularly in the beginning part of the data decrease the accuracy of the estimation with classical least-squares method. In this paper, a Bayesian Interference estimator is proposed to improve the accuracy through incorporating prior knowledge to estimation. The accuracy of the proposed RUL estimation tool is verified on the collected experimental data of thermally aged discrete power MOSFETs.