Mahalanobis semi-supervised mapping and beetle antennae search based support vector machine for wind turbine rolling bearings fault diagnosis-Reference-Cited by-同舟云学术

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Mahalanobis semi-supervised mapping and beetle antennae search based support vector machine for wind turbine rolling bearings fault diagnosis

Published:2020-08 Issue: Volume:155 Page:1312-1327
ISSN:0960-1481
Container-title:Renewable Energy
language:en
Short-container-title:Renewable Energy

Author:

Wang Zhenya,Yao Ligang,Cai Yongwu,Zhang Jun

Funder

National Natural Science Foundation of China

Fujian Provincial Department of Science and Technology

Publisher

Elsevier BV

Subject

Renewable Energy, Sustainability and the Environment

Reference43 articles.

1. Real-time monitoring, prognosis, and resilient control for wind turbine systems;Gao;Renew. Energy,2018

2. Parameter-varying modelling and fault reconstruction for wind turbine systems;Shao;Renew. Energy,2018

3. Machine learning methods for wind turbine condition monitoring: a review;Stetco;Renew. Energy,2019

4. A review of failure modes, condition monitoring and fault diagnosis methods for large-scale wind turbine bearings;Liu;Measurement,2020

5. Vibration fault diagnosis of wind turbines based on variational mode decomposition and energy entropy;Chen;Energy,2019

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