Self-Adaptive RUL Prediction of Power Electronic Devices with Package Failure

Power electronic devices vary in lifetime due to intrinsic device characteristics and extrinsic operational environments, which pose significant challenges in lifetime prediction. Traditional Deep Learning methods often directly map precursor signals to the Remaining Useful Lifetime (RUL), lacking the health state information needed to adapt dynamically to device characteristics. To address this limitation, we propose a stateful, self-adaptive RUL prediction method for package failure of power diodes. It utilizes junction temperature signals as inputs, representing thermal-mechanical fatigue influenced by external operational environments, to adjust the algorithm states, which contain the device characteristics and health state information. The proposed method combines two models, a stateful-LESIT (SLESIT) model and a Kalman Filter (KF). The SLESIT model dynamically adjusts its state using current junction temperature signals to estimate the RUL. The produced estimation is then used to rectify the predictions from an intuitive RUL propagation model in KF, providing a statistically optimal RUL estimation at each cycle. Validated through online simulation with accelerated aging data from power diodes that exhibit significant lifetime variability (68.1%), our approach reduces Mean Absolute Error (MAE) from 44.17% to 84.52% compared to popular Deep Learning methods.