An IGBT coupling structure with a smart service life reliability predictor using active learning

IF 3.7 3区材料科学 Q1 INSTRUMENTS & INSTRUMENTATION

Smart Materials and Structures Pub Date : 2024-09-18 DOI:10.1088/1361-665x/ad7659

Shizhe Feng, Yicheng Guo, Weihua Li, Haiping Du, Grzegorz Krolczyk and Z Li

引用次数: 0

Abstract

An effective approach is proposed to evaluate the service life reliability of a multi-physics coupling structure of an insulated gate bipolar transistor (IGBT) module. The node-based smoothed finite element method with stabilization terms is firstly employed to construct an electrical-thermal-mechanical (ETM) coupling structure of the IGBT module, based on which the multi-physics responses can be accurately calculated to predict the service life of the IGBT module. By using the high-quality sample data obtained through the ETM coupling model, a Monte Carlo based active learning Kriging metamodel (AK-MCS) is developed to assess the service life reliability of the IGBT module, which can greatly reduce the computational cost needed by the surrogate model construction and reliability analysis. Numerical results show that the proposed ETM coupling structure can produce high-quality sample data of the IGBT dynamics and the AK-MCS machine learning technique can accurately estimate the service life reliability of the IGBT module.

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采用主动学习技术的智能使用寿命可靠性预测器的 IGBT 耦合结构

本文提出了一种评估绝缘栅双极晶体管（IGBT）模块多物理耦合结构使用寿命可靠性的有效方法。首先采用基于节点的平滑有限元法和稳定项来构建 IGBT 模块的电-热-机耦合（ETM）结构，在此基础上精确计算多物理场响应，从而预测 IGBT 模块的使用寿命。利用 ETM 耦合模型获得的高质量样本数据，开发了基于蒙特卡罗的主动学习克里金元模型（AK-MCS）来评估 IGBT 模块的使用寿命可靠性，从而大大降低了代用模型构建和可靠性分析所需的计算成本。数值结果表明，所提出的 ETM 耦合结构可以产生高质量的 IGBT 动态样本数据，AK-MCS 机器学习技术可以准确估计 IGBT 模块的使用寿命可靠性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Smart Materials and Structures 工程技术-材料科学：综合

CiteScore

7.50

自引率

12.20%

发文量

317

审稿时长

3 months

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