Remaining Useful Lifetime Estimation for Power MOSFETs Under Thermal Stress With RANSAC Outlier Removal

Reliability of power converters is crucial for mission critical systems. Among the components that are susceptible to failure, power semiconductor devices are one of the major causes of the power converter failures. This paper focuses on the remaining useful lifetime (RUL) estimation of degraded power MOSFETs, which are stressed by thermal cycling. The relative change in on-state resistance is identified as the fault signature. A data-driven RUL estimation algorithm based on a linear approximation model is proposed. The empirical coefficients are estimated by the classical least squares, where the outliers are removed by random sample consensus (RANSAC) algorithm. A sliding window approach is used to track the nonlinearities. The window size, threshold value, and number of samples used by RANSAC are optimized with the genetic algorithm. The accuracy of the proposed RUL estimation tool is verified on a number of thermally aged discrete power MOSFET data.