Monitoring the degradation state of Insulated Gate Bipolar Transistor (IGBT) modules is essential for ensuring the reliability and longevity of power electronic systems, especially in safety-critical and high-performance applications. However, direct measurement of key degradation indicators - such as junction temperature, solder fatigue or delamination - remains challenging due to the physical inaccessibility of internal components and the harsh environment. In this context, machine learning-based virtual sensing offers a promising alternative by bridging the gap from feasible sensor placement to the relevant but inaccessible locations. This paper explores the feasibility of estimating the degradation state of solder layers, and the corresponding full temperature maps based on a limited number of physical sensors. Based on synthetic data of a specific degradation mode, we obtain a high accuracy in the estimation of the degraded solder area (1.17% mean absolute error), and are able to reproduce the surface temperature of the IGBT with a maximum relative error of 4.56% (corresponding to an average relative error of 0.37%).

翻译：绝缘栅双极型晶体管（IGBT）模块的退化状态监测对于确保电力电子系统的可靠性与长寿命运至关重要，尤其在安全关键型及高性能应用场景中。然而，由于内部组件的物理不可及性与恶劣运行环境，对结温、焊料疲劳或分层等关键退化指标的直接测量仍极具挑战性。在此背景下，基于机器学习的虚拟传感技术通过弥合可部署传感器位置与相关但不可达位置之间的鸿沟，提供了一种有前景的替代方案。本文探索了基于有限数量物理传感器估测焊料层退化状态及对应全温度分布的可行性。通过针对特定退化模式的合成数据，我们在退化焊料区域估测中实现了高精度（平均绝对误差1.17%），并能以最大相对误差4.56%（对应平均相对误差0.37%）复现IGBT表面温度场。