Icing prediction of fan blade based on a hybrid model

被引：2
作者：
Peng C. [1 ,2 ]
He J. [1 ]
Chi H. [1 ]
Yuan X. [1 ,2 ]
Deng X. [1 ]
机构：
[1] School of Computer Science, Hunan University of Technology, Zhuzhou
[2] School of Automation, Central South University, Changsha
来源：
International Journal of Performability Engineering | 2019年 / 15卷 / 11期
基金：
中国国家自然科学基金;
关键词：
Blade icing; Data-driven; Fault prediction; Feature extraction; Neural network; Wind turbine;
D O I：
10.23940/ijpe.19.11.p6.28822890
中图分类号：
学科分类号：
摘要：
For the problem that fan blade icing failures cannot be accurately predicted in advance, a data-driven fault prediction method is proposed in this paper. Firstly, the delay window is introduced to the PCA algorithm to extract the fault mode related features from the SCADA high-dimensional data. Then, the trained Elman neural network is adopted to predict the future value of the relevant features. Finally, a BP self-clustering algorithm is designed to predict the icing fault of the blade with the multi-source data fusion. The results show that the proposed method can effectively predict the icing failure of wind turbine blades and has reference significance for the maintenance of wind turbines. © 2019 Totem Publisher, Inc. All rights reserved.
引用
收藏
页码：2882 / 2890
页数：8
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