Virtual Sensing for Solder Layer Degradation and Temperature Monitoring in IGBT Modules
This addresses the challenge of inaccessible internal monitoring in power electronic systems, but it is incremental as it applies existing virtual sensing methods to a specific degradation mode with synthetic data.
The paper tackled the problem of monitoring degradation in IGBT modules by using machine learning-based virtual sensing to estimate solder layer degradation and temperature maps from limited physical sensors, achieving a mean absolute error of 1.17% for degraded area and a maximum relative error of 4.56% for temperature.
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%).