A data-driven lifetime prediction method for thermal stress fatigue failure of power MOSFETs

被引：14

作者：

Wang, Xiang ^{[1
]}

Wei, Weiwei ^{[1
]}

Zhang, Yanhui ^{[2
]}

Feng, Wei ^{[2
]}

Xu, Guoqing ^{[1
]}

Xiang, An ^{[3
]}

机构：

[1] Shanghai Univ, Coll Mechatron Engn & Automat, Shanghai 200444, Peoples R China

[2] Chinese Acad Sci, Shenzhen Inst Adv Technol, Shenzhen 518055, Peoples R China

[3] Tongji Univ, Coll Elect & Informat Engn, Shanghai 201804, Peoples R China

来源：

ENERGY REPORTS | 2022年 / 8卷

关键词：

Power MOSFET; Lifetime prediction; On-state resistance; Data-driven;

D O I：

10.1016/j.egyr.2022.10.137

中图分类号：

TE [石油、天然气工业]; TK [能源与动力工程];

学科分类号：

080707 [能源环境工程]; 082001 [油气井工程];

摘要：

As one of the core power electronic devices that undertake power conversion and control tasks in electrical systems, power MOSFETs are widely used in key fields such as transportation, industrial drives, and aerospace. At present, the traditional method improves the reliability of power electronic devices by new material/ structure/ process, redundancy, and derating operation, which is becoming increasingly difficult to meet the requirements of rapidly developing power conversion. Based on the needs of reliability research, a data-driven lifetime prediction method for thermal stress fatigue failure of power MOSFETs is proposed. The main work is reflected in two aspects: (1) The thermal stress fatigue failure mechanism of the power MOSFETs is analyzed. On-state resistance is selected as the failure precursor parameter for evaluating the health status of power MOSFETs. (2) Autoregressive Integrated Moving Average (ARIMA) model of Time-Series Analysis is applied to realize data-driven lifetime prediction. Compared with the model-based lifetime prediction method using nonlinear regression algorithm, The data-driven method has higher prediction accuracy and better prediction stability. (C) 2022 The Author(s). Published by Elsevier Ltd.

引用

页码：467 / 473

页数：7

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