EVALUATION OF HEALTH CONDITION OF WIND TURBINE BEARING BASED ON DYNAMIC THRESHOLD

被引:0
作者
Fang C. [1 ]
Li Z. [1 ]
Wang Y. [1 ]
Wang D. [1 ]
Cheng X. [1 ]
机构
[1] Shanghai Power Equipment Research Institute Co.,Ltd, Shanghai
来源
Taiyangneng Xuebao/Acta Energiae Solaris Sinica | 2024年 / 45卷 / 02期
关键词
bearing; condition evaluation; deterioration; dynamic threshold; health management; wind turbines;
D O I
10.19912/j.0254-0096.tynxb.2022-1671
中图分类号
学科分类号
摘要
To address the challenges faced by current wind turbine bearing health assessment methods,which involve extensive sample extraction,model training,and are therefore cumbersome,time-intensive,and labor-intensive with limited applicability,a method utilizing a dynamic threshold for assessing the health status of wind turbine bearings is introduced. Firstly,considering the randomness and intermittency of wind,the concept of deterioration degree is introduced by using the temperature monitoring data of wind turbine bearings,and the upper and lower dynamic thresholds of deterioration degree are determined by curve fitting and cluster clustering methods. Secondly,the health condition evaluation method of wind turbine bearings based on dynamic threshold is proposed by calculating the health status comment set and its grade division range and deterioration degree. Finally,taking the overtemperature fault of the rear bearing of a wind turbine as an example,it is verified that the proposed evaluation method can obtain the effective health conditions of the wind turbine bearing and obtain the fault symptom as soon as possible. © 2024 Science Press. All rights reserved.
引用
收藏
页码:152 / 157
页数:5
相关论文
共 16 条
[1]  
JIN X H, SUN Y, SHAN J H, Et al., Fault diagnosis and prognosis for wind turbines:an overview[J], Chinese journal of scientific instrument, 38, 5, pp. 1041-1053, (2017)
[2]  
HU Y G, LI H, LIAO X L, Et al., Performance degradation model and prediction method of real-time remaining life for wind turbine bearings[J], Proceedings of the CSEE, 36, 6, pp. 1643-1649, (2016)
[3]  
ZHAO J,, CHEN Z G,, ZHAO Z C,, Et al., Fault diagnosis of rolling bearing based on SET and DRSN[J], Journal of Chongqing University of Technology (natural science), 35, 1, pp. 138-144, (2021)
[4]  
SAIDI L,, BEN ALI J, BECHHOEFER E, Et al., Wind turbine high-speed shaft bearings health prognosis through a spectral Kurtosis-derived indices and SVR[J], Applied acoustics, 120, pp. 1-8, (2017)
[5]  
SONG W, LIN J W, ZHOU F Z,, Et al., Wind turbine bearing fault diagnosis method based on an improved denoising Autoencoder[J], Power system protection and control, 50, 10, pp. 61-68, (2022)
[6]  
YU J B., Health condition monitoring of machines based on hidden Markov model and contribution analysis[J], IEEE transactions on instrumentation and measurement, 61, 8, pp. 2200-2211, (2012)
[7]  
ZHANG T, HE A M,, Et al., Rotating parts fault diagnosis method considering mixed feature and PSO-SVM [J], Journal of Chongqing University of Technology (natural science), 36, 8, pp. 117-124, (2022)
[8]  
LI Z N, ZHANG X Y,, HU W,, Et al., State accessment and prediction of wind turbine high speed shaft bearing based on health index[J], Acta energiae solaris sinica, 42, 10, pp. 290-297, (2021)
[9]  
WEI L,, HU X D, YIN S., Optimized- XGBoost early warning of wind turbine generator front bearing fault[J], Journal of system simulation, 33, 10, pp. 2335-2343, (2021)
[10]  
ZHAO H S, LIU H H., Fault detection of wind turbine main bear based on deep learning network[J], Acta energiae solaris sinica, 39, 3, pp. 588-595, (2018)