A Wind Turbine Bearing Fault Detection Method Based on Improved CEEMDAN and AR-MEDA
Author:
Publisher
Springer Science and Business Media LLC
Subject
Microbiology (medical),Immunology,Immunology and Allergy
Link
https://link.springer.com/content/pdf/10.1007/s42417-023-01117-x.pdf
Reference47 articles.
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2. Liu Z, Zhang L (2020) A review of failure modes, condition monitoring and fault diagnosis methods for large-scale wind turbine bearings. Measurement 149:107002. https://doi.org/10.1016/j.measurement.2019.107002
3. de Azevedo HDM, Araújo AM, Bouchonneau N (2016) A review of wind turbine bearing condition monitoring: state of the art and challenges. Renew Sustain Energy Rev 56:368–379. https://doi.org/10.1016/j.rser.2015.11.032
4. Qiao W, Lu D (2015) A survey on wind turbine condition monitoring and fault diagnosis—part II: signals and signal processing methods. IEEE Trans Ind Electron 62(10):6546–6557. https://doi.org/10.1109/TIE.2015.2422394
5. Gao Z, Liu X (2021) An overview on fault diagnosis, prognosis and resilient control for wind turbine systems. Processes 9(2):300. https://doi.org/10.3390/pr9020300
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1. Advancing bearing fault diagnosis under variable working conditions: a CEEMDAN-SBS approach with vibro-electric signal integration;The International Journal of Advanced Manufacturing Technology;2024-04-03
2. A Novel Customised Load Adaptive Framework for Induction Motor Fault Classification Utilising MFPT Bearing Dataset;Machines;2024-01-08
3. Advancing Bearing Fault Diagnosis under Variable Working Conditions: A CEEMDAN-SBS Approach with Vibro-Electric Signal Integration;2023-12-29
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