Xiao Yue, Wang Fanrong
School of Electrical and Electronic Engineering, Hubei University of Technology, Wuhan 430070, China.
Micromachines (Basel). 2025 Jun 8;16(6):689. doi: 10.3390/mi16060689.
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 R. 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.
绝缘栅双极型晶体管（IGBT）因其卓越的性能而广泛应用于电力电子系统中。然而，与此同时，它们也是电子系统中最关键且最脆弱的部件之一。IGBT的故障预测能够精确地预测潜在风险，以确保系统可靠性。本文采用贝叶斯优化的完全集成经验模态分解自适应噪声（BO-CEEMDAN）来高效提取故障特征，并提出了一种名为Performer-KAN的预后模型用于IGBT故障预测。所提出的模型将Performer中高效的FAVOR+机制与基于灵活样条的柯尔莫哥洛夫-阿诺德网络（KAN）激活相结合，从而实现了改进的非线性逼近和预测精度。使用由BO-CEEMDAN分解的增量模态函数空间（IMFS）进行了综合实验。使用平均绝对误差（MAE）、均方根误差（RMSE）和相关系数（R）等关键指标对模型性能进行了评估。与六个具有代表性的深度学习模型相比，Performer-KAN在保持低计算开销的同时展现出卓越的预测精度。结果表明，所提出的方法为工业应用中的IGBT实时健康监测和故障预测提供了一种实用且有效的解决方案。
