An Integrated Ensemble Learning Model for Imbalanced Fault Diagnostics and Prognostics-Reference-Cited by-同舟云学术

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An Integrated Ensemble Learning Model for Imbalanced Fault Diagnostics and Prognostics

Published:2018 Issue: Volume:6 Page:8394-8402
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Wu Zhenyu^ORCID,Lin Wenfang,Ji Yang

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science

Link

http://xplorestaging.ieee.org/ielx7/6287639/8274985/08295035.pdf?arnumber=8295035

Cited by 82 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

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2. Highly imbalanced fault diagnosis of turbine blade cracks via deep focal dynamically weighted conditional variational auto-encoder network;Advanced Engineering Informatics;2024-10

3. A Novel Data-Driven Approach with a Long Short-Term Memory Autoencoder Model with a Multihead Self-Attention Deep Learning Model for Wind Turbine Converter Fault Detection;Applied Sciences;2024-08-23

4. Critical quantitative evaluation of integrated health management methods for fuel cell applications;International Journal of Hydrogen Energy;2024-06

5. Classification of Highly Imbalanced Supervisory Control and Data Acquisition Data for Fault Detection of Wind Turbine Generators;Energies;2024-03-26

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