A Lifetime Prediction Method of IGBT Based on Phased Nonlinear Wiener Process

The Insulated Gate Bipolar Transistor (IGBT) is a critical component of power electronic devices, and its reliability significantly impacts the overall system performance. To enhance IGBT reliability assessment in the context of modular multilevel converters (MMCs), this study proposes a remaining useful life (RUL) prediction method based on a phased nonlinear Wiener process. By analyzing the failure mechanisms of IGBTs, the turn-off peak voltage and the collector-emitter saturation voltage are identified as key degradation indicators. A piecewise nonlinear Wiener degradation model is employed for RUL prediction, with gradient change detection method used to dynamically determine segmentation points. The proposed model accounts for the stochastic effects of the drift coefficient and measurement errors, with Kalman filtering algorithm applied to update random parameters in real time. Based on accelerated aging test data, the proposed method is compared with the standard Wiener model, the Wiener model considering only drift coefficient randomness, and other recent RUL prediction methods that do not rely on the Wiener process. The results demonstrate that the proposed approach achieves higher prediction accuracy and computational efficiency, particularly in scenarios with significant fluctuations in degradation indicators, making it highly promising for engineering applications.