Sparse Kernel Ridge Regression Assisted Particle Filter Based Remaining Useful Life Estimation of Cascode GaN FET

Abstract

This article proposes a novel sparse kernel ridge regression assisted particle filter (SKRR-PF) based remaining useful life (RUL) estimation method to address the reliability of the cascode gallium nitride field-effect transistors in emerging power electronics systems. The proposed method will overcome three main challenges in this area: first, a large variation in the RUL estimation under the severe noise uncertainty; second, sample degeneracy and sample impoverishment; and third, the low capability of tracing the dynamic and abrupt change in the fault precursor trajectory. The state-of-the-art RUL estimation methods require a significant number of samples to address such issues, including particle degeneracy and sample impoverishment. Also, the state-of-the-art methods mostly fail to apply under a system's dynamic condition changes over a switch's lifetime. The proposed method will significantly enhance the estimation accuracy by introducing SKRR in resampling the posterior probability density function estimation, especially under the dynamically varying system's health condition due to the harsh industrial operation. Thus, the proposed method will offer fast tracing the sudden change in the R <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DS,</sub> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ON</sub> trajectory, leading to a significant accurate RUL estimation. The performance of the proposed method has been rigorously validated through the purposely designed power cycling testbed.