Virtual Sensing for Solder Layer Degradation and Temperature Monitoring in IGBT Modules

TL;DR

This paper demonstrates that machine learning-based virtual sensing can accurately estimate solder layer degradation and temperature distribution in IGBT modules using limited sensor data, enhancing reliability monitoring.

Contribution

It introduces a virtual sensing approach for estimating solder degradation and temperature maps in IGBT modules, validated with synthetic data showing high accuracy.

Findings

Mean absolute error in solder area estimation: 1.17%

Maximum relative error in temperature mapping: 4.56%

Average relative error in temperature estimation: 0.37%

Abstract

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%).