用少量传感器估算IGBT焊层退化与温度分布，提升电力电子可靠性。
Virtual Sensing for Solder Layer Degradation and Temperature Monitoring in IGBT Modules
- 基于合成数据训练模型，从有限传感器推断内部退化状态。
- 焊层退化面积估计误差仅1.17%，温度分布相对误差平均0.37%。
- 适合电力电子系统健康监测，尤其适用于无法直接测量的场景。
监控绝缘栅双极晶体管（IGBT）模块的退化状态对于确保电力电子系统的可靠性和寿命至关重要，特别是在安全关键和高性能应用中。然而，由于内部组件物理上不可及且环境恶劣，直接测量关键退化指标——如结温、焊点疲劳或脱层——仍具挑战性。在此背景下，基于机器学习的虚拟传感提供了一种有前景的替代方案，可连接可行的传感器布置与相关但难以触及的位置。本文探讨了基于特定退化模式的合成数据，利用有限数量的物理传感器估算焊层退化状态及相应全温度分布的可行性。结果表明，焊层退化面积估计的平均绝对误差为1.17%，表面温度重建的最大相对误差为4.56%（平均相对误差0.37%），展现出高精度预测能力。
原文摘要 · Abstract (English)
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%).
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