Remaining Useful Lifetime Prediction of Discrete Power Devices by Means of Artificial Neural Networks-Reference-Cited by-同舟云学术

Remaining Useful Lifetime Prediction of Discrete Power Devices by Means of Artificial Neural Networks

Published:2023 Issue: Volume:4 Page:978-986
ISSN:2644-1314
Container-title:IEEE Open Journal of Power Electronics
language:
Short-container-title:IEEE Open J. Power Electron.

Author:

Vaccaro Alessandro¹^ORCID,Biadene Davide¹^ORCID,Magnone Paolo¹^ORCID

Affiliation:

1. Department of Management and Engineering, Università degli Studi di Padova, Vicenza, Italy

Funder

Interdisciplinary Strategy for the Development of Advanced Mechatronics Technologies

DTG, University of Padova-Project

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/8782709/10004734/10315015.pdf?arnumber=10315015

Reference47 articles.

1. A new lifetime model for advanced power modules with sintered chips and optimized Al wire bonds;scheuermann;Proc PCIM Europe Conf,2013

2. IGBT aging monitoring and remaining lifetime prediction based on long short-term memory (LSTM) networks

3. Model for power cycling lifetime of IGBT modules-various factors influencing lifetime;bayerer;Proc CIPS -5th Int Conf Integr Power Electron Syst,2008

4. Prognosis of power MOSFET resistance degradation trend using artificial neural network approach

5. Predicting Lifetime of Semiconductor Power Devices under Power Cycling Stress using Artificial Neural Network

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