Performer-KAN-Based Failure Prediction for IGBT with BO-CEEMDAN

Insulated Gate Bipolar Transistors (IGBTs) are widely deployed in power electronic systems due to their superior performance. However, at the same time, they are one of the most critical and fragile components in electronic systems. The failure prediction of IGBTs can precisely forecast the potential risk to guarantee system reliability. In this paper, Bayesian-optimized CEEMDAN is adopted to extract fault features efficiently, and a prognostic model named Performer-KAN is proposed for IGBT failure prediction. The proposed model combines the efficient FAVOR+ mechanism from the Performer with the flexible spline-based activation of the Kolmogorov-Arnold Network (KAN), enabling improved nonlinear approximation and predictive precision. Comprehensive experiments were conducted using the IMFS, which were decomposed by BO-CEEMDAN. The model's performance was evaluated using key metrics such as MAE, RMSE, and R². The Performer-KAN demonstrates superior prediction accuracy while maintaining low computational overhead, compared to six representative deep learning models. The results demonstrate that the proposed method offers a practical and effective solution for real-time IGBT health monitoring and fault prediction in industrial applications.