A Digital Twin–Based Reliability Prediction Method for Switching Mode Power Supplies via Physics of Failure Models Injection (Reliability Prediction of SMPS via DT and PoF)

ABSTRACT Switching mode power supplies (SMPSs) have been widely used in aerospace, renewable energy, and other fields. However, during long‐term operation, continuous electrical and thermal stresses cause performance degradation in various components, leading to changes in output characteristics and affecting system reliability. Currently, the integration of physics of failure (PoF) models with simulation technologies remains underdeveloped. Many existing methods fail to achieve integrated simulations of component failure modes and lack associated model calibration, leading to insufficient analysis of overall circuit reliability. This limitation restricts the scalability of complex circuit research. This paper proposes a digital twin–based reliability prediction method for SMPS via PoF models injection. Using a representative SMPS as a case study, this study combines electro‐thermal coupled modeling, sensitivity analysis–based failure mode identification, and integrated performance degradation simulation to build an online reliability assessment digital twin system. Based on the principles of PoF, this study achieves coupled degradation analysis across multiple component types and failure mechanisms, and implements dynamic calibration to enhance the accuracy of reliability predictions. This approach significantly improves both the efficiency and precision of reliability forecasting for SMPS.