@inproceedings{cc71c9a97ae84c19ae6f843810bf4a4d,
title = "Study on life prediction method of mosfet thermal environment experiments based on extended kalman filter",
abstract = "The prediction method of the residual service life of power MOSFET is studied in this paper. By analyzing the data collected under the existing thermal overload accelerated aging experiment, after processing the data, the failure threshold was set by using the prediction algorithm based on data drive and model to predict the residual life of power MOSFET. The traditional SVR algorithm requires a lot of parameter selection and only a few parameter convergence. The prediction algorithm model is based on Extended Kalman Filter, the extended Kalman filter is relative to the advantage of support vector machine (SVM) regression is used to predict the variance is small, and can be found in a wide range of required to predict the sample interval and the predicted results are more accurate, the test results verify the feasibility of this method.",
keywords = "Accelerated aging, Extended Kalman filter, Failure mechanism, Life prediction, Power MOSFET",
author = "Ke Li and Yuxiang Zhang and Shimin Song and Zhijian Zhao and Lijing Wang",
note = "Publisher Copyright: {\textcopyright} The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2020.; 20th International Conference on Man-Machine-Environment System Engineering, MMESE 2020 ; Conference date: 24-10-2020 Through 26-10-2020",
year = "2020",
doi = "10.1007/978-981-15-6978-4\_58",
language = "英语",
isbn = "9789811569777",
series = "Lecture Notes in Electrical Engineering",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "495--503",
editor = "Shengzhao Long and Dhillon, \{Balbir S.\}",
booktitle = "Man-Machine-Environment System Engineering - Proceedings of the 20th International Conference on MMESE, 2020",
address = "德国",
}