Logistic regression and feature extraction based fault diagnosis of main bearing of wind turbines-Reference-Cited by-同舟云学术

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Logistic regression and feature extraction based fault diagnosis of main bearing of wind turbines

Published:2016-06 Issue: Volume: Page:
ISSN:
Container-title:2016 IEEE 11th Conference on Industrial Electronics and Applications (ICIEA)
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Author:

Bodla Muhammad Kamran,Malik Sarmad Majeed,Rasheed Muhammad Tahir,Numan Muhammad,Ali Muhammad Zeeshan,Brima Jimmy Baimba

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/7580934/7603539/07603846.pdf?arnumber=7603846

Cited by 18 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

1. Enhancing Fault Diagnosis in IoT Sensor Data through Advanced Preprocessing Techniques;Electronics;2024-08-19

2. Total process of fault diagnosis for wind turbine gearbox, from the perspective of combination with feature extraction and machine learning: A review;Energy and AI;2024-01

3. PBAFS: Preference-Based Active Feature Selection for Fault Diagnosis and Prevention of HVAC Systems;Lecture Notes in Computer Science;2024

4. Residual Signature Analysis for Wind Turbine Failure Prognosis;2023 IEEE PES Innovative Smart Grid Technologies Europe (ISGT EUROPE);2023-10-23

5. Optimization Algorithms for Informative Features Extraction to Predict the Aircraft Electromechanical Systems Technical Condition;2023 16th International Conference Management of large-scale system development (MLSD);2023-09-26

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