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A Dynamic Physics-Guided Ensemble Model for Non-Intrusive Bond Wire Health Monitoring in IGBTs.

Xinyi Yang¹, Zhen Hu¹, Yizhi Bo¹

¹College of Artificial Intelligence, Nanjing University of Posts and Telecommunications, Nanjing 210023, China.

|January 28, 2026

Summary

This study introduces a physics-constrained ensemble learning framework to predict bond wire degradation in IGBT modules. The method enhances reliability by accurately assessing bond wire health non-intrusively.

Keywords:

IGBT health monitoring bond wire degradation ensemble learning physics-constrained machine learning

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Area of Science:

Power Electronics
Reliability Engineering
Machine Learning

Background:

Bond wire degradation is the primary failure mode in Insulated Gate Bipolar Transistor (IGBT) modules, causing significant reliability issues in power converters.
Current monitoring techniques for bond wire health present limitations in accuracy, complexity, and electromagnetic compatibility.

Purpose of the Study:

To develop a non-intrusive framework for assessing bond wire health in IGBT modules by predicting on-state collector-emitter voltage (Vce-on).
To improve the accuracy and robustness of bond wire health monitoring by integrating physics-based constraints with ensemble learning.

Main Methods:

A physics-constrained ensemble learning framework was developed, integrating multidimensional feature engineering and adaptive ensemble fusion.
A 16-dimensional feature vector was created using electrical, thermal, and aging parameters, including novel terms for electro-thermal stress coupling.
Three gradient boosting models (CatBoost, LightGBM, XGBoost) were adaptively fused, incorporating physics-based regularization for thermodynamic consistency.

Main Results:

The proposed framework achieved high accuracy in Vce-on prediction, with a mean absolute error of 0.0066 V and R² of 0.9998.
Demonstrated a 48.4% improvement over individual base models while maintaining 99.1% physical constraint compliance.
The approach effectively harmonizes data-driven learning with physical principles for robust health monitoring.

Conclusions:

The developed framework offers a paradigm-shifting approach for accurate and practical bond wire health assessment in power electronic systems.
This method enhances the reliability and longevity of IGBT modules by enabling early detection of degradation.
The integration of physics-based constraints with ensemble learning provides a robust solution for next-generation power electronics reliability.