A novel method for the RUL prediction of power MOSFET using WMA-DELM

Power semiconductor devices, particularly MOSFETs, serve as critical components in contemporary electronic systems. Accurate prediction of their remaining useful life (RUL) is therefore essential for enhancing both the technical and economic value of remanufacturing processes. However, current efforts to predict the RUL of these devices are hindered by limited accuracy in capturing their nonlinear degradation behavior. To overcome this limitation, an experimental framework is first developed. A power cycle accelerated aging platform, operating under controlled constant-amplitude case temperature fluctuations, is employed to collect degradation parameter data. The variation in on-state voltage is selected as the indicator for RUL prediction. Based on the experimental data, a novel RUL prediction model, referred to as WMA-DELM, is proposed. In this model, the Deep Extreme Learning Machine (DELM) is optimized using the Whale Migrating Algorithm (WMA). The predictive performance of the WMA-DELM model is validated through comparative analysis with experimental datasets and alternative predictive approaches. The results confirm the model’s effectiveness and demonstrate its potential application in RUL estimation for MOSFETs. This methodology provides a reliable and practical solution for advancing reliability assessment in power electronic systems.