Remaining Useful Life Prediction of Power MOSFETs Using Model-Based and Data-Driven Methods

被引：4
|
作者：
Wu, Jinjing [1 ]
Xu, Zheng [2 ]
Wei, Xiao [3 ]
机构：
[1] Minist Publ Secur, Key Lab Informat Network Secur, Res Inst 3, 339 Bisheng Rd, Shanghai, Peoples R China
[2] Guangxi Key Lab Cryptog & Informat Secur, Guilin, Peoples R China
[3] Shanghai Univ, Shanghai, Peoples R China
来源：
CYBER SECURITY INTELLIGENCE AND ANALYTICS | 2020年 / 928卷
关键词：
Prognostics and health management; Remaining useful life; Least square support vector machine; Kalman Filter; RELIABILITY; SYSTEM;
D O I：
10.1007/978-3-030-15235-2_56
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Prognostics and health management has become an advanced engineering technology in avionics systems which can implement condition monitoring and reduce unnecessary downtime. A prognostic application to power MOSFETs is developed in this paper. Firstly, failure mechanism of the power MOSFETs under power cycling aging tests is analyzed. Then, the drain-source on-state resistance is considered as a leading precursor of failure as it exhibits a decaying trend. Finally, a degradation model is established to predict the remaining useful life based on Kalman filter and LS-SVM, respectively. Several results are analyzed to demonstrate the feasibility and effectiveness of these methods.
引用
收藏
页码：373 / 381
页数：9
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