State accessment and prediction of wind turbine high speed shaft bearing based on health index

被引：0
|
作者：
Li Z. [1 ]
Zhang X. [1 ]
Hu W. [1 ]
Xie L. [1 ]
机构：
[1] College of Electrical Engineering, Xinjiang University, Urumqi
来源：
Taiyangneng Xuebao/Acta Energiae Solaris Sinica | 2021年 / 42卷 / 10期
关键词：
Assessment; Bearings; Bidirectional long shot term memory(Bi-LSTM); Health index; Kernel principal component analysis(KPCA); Prediction; Wind turbines;
D O I：
10.19912/j.0254-0096.tynxb.2020-1170
中图分类号：
学科分类号：
摘要：
In order to evaluate the health condition of the wind turbine high-speed shaft bearings and predict its subsequent state, a health index(HI)curve reflecting the degradation process of the bearing was constructed as the basis of health condition prediction, A method based on Kernel Principal Component Analysis(KPCA) and Bidirectional Long Shot Term Memory(Bi-LSTM) network model was proposed. Firstly, the HI curve of high-speed shaft bearing was constructed by using monotonicity analysis and KPCA, Then the health state of high-speed shaft bearing was predicted by using Bi-LSTM network on the basis of the constructed HI curve. The HI curve constructed by this method is proved to have good monotonicity and can be used for more accurate health assessment and state prediction of high-speed shaft bearings. © 2021, Solar Energy Periodical Office Co., Ltd. All right reserved.
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页码：290 / 297
页数：7
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