Remaining useful life prognostic of power metal oxide semiconductor field effect transistor based on improved particle filter algorithm

Lifeng Wu,Hongmin Wang,Zhen Peng,Jing Yang

doi:10.1177/1687814017749324

Abstract

The power metal oxide semiconductor field effect transistor is used extensively in analog circuits and digital circuits. However, it is also the highest failure rate component in the power electron...

Highlights

Metal oxide semiconductor field effect transistor (MOSFET) is an important power semiconductor device and is widely applied in analog circuits and digital circuits.[1]
At least 34% of failures in analog circuits and digital circuits are caused by power electronics fault subjected to high switching speed and/or overheating
The remaining useful life (RUL) prognostic of power electronics has received much more attention, which can take effective measures to reduce the loss before failure

Summary

Introduction

Metal oxide semiconductor field effect transistor (MOSFET) is an important power semiconductor device and is widely applied in analog circuits and digital circuits.[1]. Based on the mechanism of the hot carrier degradation, the least-squares method was used to predict the lifetime of MOSFETs, considering the effect of temperature an electric stress.[20] the model is accurate, it is difficult to establish in a complex dynamic system. Zheng et al.[23] studied the RUL of power MOSFETs based on the data-driven model, using the relevance vector machine to update the degradation model for the prediction accuracy. The PF algorithm is a recursive Bayesian filtering algorithm based on a large number of random particles and the corresponding weights to estimate the posterior probability distribution.[26] As it is suitable for the nonlinear and non-Gaussian system, and it gives the uncertain representation of the probability density function (PDF), there are many studies about the PF algorithm.[6] The improved resampling method helps to resolve degeneracy phenomenon in PF. The initial state x0 has the property p(x0) and N is the number of particles

Updating the particles and the corresponding weights based on STKF27

Resampling used the MH algorithm23

State estimation

Findings

Conclusion