A data-driven approach for IGBT aging degree evaluation based on multi-observation sequence particle filtering and support vector regression

被引：0

作者：

Liu, He ^{[1
,2
]}

Li, Xinyu ^{[1
,2
]}

Liu, Zhifeng ^{[1
,2
]}

机构：

[1] Hefei Univ Technol, Sch Mech Engn, Hefei 230000, Peoples R China

[2] Anhui Prov Key Lab Low Carbon Recycling Technol &, Hefei, Peoples R China

来源：

MECHANICS OF ADVANCED MATERIALS AND STRUCTURES | 2025年 / 32卷 / 05期

关键词：

Insulated gate bipolar transistor; aging degree evaluation; multi-observation sequence particle filtering; support vector regression; data-driven; SOLDER; MODULE; DAMAGE; LIFE;

D O I：

10.1080/15376494.2024.2355371

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

Insulated gate bipolar transistors (IGBTs) are capable of efficiently and stably converting and regulating electrical power. Precise evaluation of the aging degree of IGBTs is particularly important. This study proposes a data-driven approach for IGBT aging degree evaluation based on multi-observation sequence particle filtering and support vector regression. This method effectively integrates the aging data from different devices, significantly reducing data uncertainty, and constructs an IGBT aging degree evaluation model based on a small amount of data. A series of experiments has verified the effectiveness of this method, demonstrating its high accuracy and reliability.

引用

页码：765 / 776

页数：12

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