RESEARCH ON COMPOSITE FAULT FEATURE EXTRACTION OF WIND TURBINE GEARBOX BASED ON IMPROVED WAVELET PACKET

Zhang Zhenhai; Wang Weiqing; Wang Haiyun; Cao Yuan

doi:10.19912/j.0254-0096.tynxb.2019-0315

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Acta Energiae Solaris Sinica ›› 2022, Vol. 43 ›› Issue (9) : 331-336. DOI: 10.19912/j.0254-0096.tynxb.2019-0315

RESEARCH ON COMPOSITE FAULT FEATURE EXTRACTION OF WIND TURBINE GEARBOX BASED ON IMPROVED WAVELET PACKET

Zhang Zhenhai, Wang Weiqing, Wang Haiyun, Cao Yuan

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Abstract

For the wavelet packet decomposition, the spectrum aliasing will occur, which will lead to the difficulty in extracting the energy spectrum of the composite fault feature of the gearbox. A method based on bypass filter to improve the wavelet packet is proposed to study the composite fault vibration signal of the doubly-fed wind turbine gearbox. Based on the vibration signal of a large number of gearboxes in the wind farm, the traditional wavelet packet and bypass filter are used to improve the wavelet packet to extract the characteristic energy spectrum of the gearbox vibration signal. Experimental results show that the bypass filter is used to improve the wavelet packet's composite fault vibration signal analysis of the doubly-fed wind turbine gearbox, which can effectively avoid the spectral aliasing of the traditional wavelet packet analysis vibration signal and accurately extract the characteristic energy spectrum of each fault state.

Key words

wind turbines / feature extraction / wavelet packet / wavelet denoising / gearbox composite failure

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Zhang Zhenhai, Wang Weiqing, Wang Haiyun, Cao Yuan. RESEARCH ON COMPOSITE FAULT FEATURE EXTRACTION OF WIND TURBINE GEARBOX BASED ON IMPROVED WAVELET PACKET[J]. Acta Energiae Solaris Sinica. 2022, 43(9): 331-336 https://doi.org/10.19912/j.0254-0096.tynxb.2019-0315

References

[1] 高润华. 小波分析在风电齿轮箱故障特征提取中的应用研究[D]. 北京: 华北电力大学, 2014.
GAO R H.Application of the wavelet analysis in fault feature extraction of gearbox in wind turbine[D]. Beijing: North China Electric Power University, 2014.
[2] 田浩. 基于HHT和XGBoost的风电机组滚动轴承故障诊断研究[D]. 乌鲁木齐: 新疆大学, 2018.
TIAN H.Research on fault diagnosis of rolling bearing of wind turbine based on HHT and XGBoost[D]. Urumqi: Xinjiang University, 2018.
[3] 冯志鹏, 范寅夕, Liang M, 等. 行星齿轮箱故障诊断的非平稳振动信号分析方法[J]. 中国电机工程学报, 2013, 33(17): 105-110, 19.
FENG Z P, FAN Y X, LIANG M,et al.A nonstationary vibration signal analysis method for fault diagnosis of planetary gearboxes[J]. Proceedings of the CSEE, 2013, 33(17): 105-110, 19.
[4] 王军辉, 贾嵘, 谭泊. 基于EEMD和模糊C均值聚类的风电机组齿轮箱故障诊断[J]. 太阳能学报, 2015, 36(2): 319-324.
WANG J H, JIA R, TAN B.Fault diagnosis of wind turbine's gearbox based on EEMD and fuzzy C means clustering[J]. Acta energiae solaris sinica, 2015, 36(2): 319-324.
[5] 程军圣, 王健, 桂林. 改进的EEMD方法及其在滚动轴承故障诊断中的应用[J]. 振动与冲击, 2018, 37(16): 51-56.
CHENG J S, WANG J, GUI L.An improved EEMD method and its application in rolling bearing fault diagnosis[J]. Journal of vibration and shock, 2018, 37(16): 51-56.
[6] 张智, 刘成颖, 刘辛军, 等. 采用小波包能量熵的铣削振动状态分析方法研究[J]. 机械工程学报, 2018, 54(21): 57-62.
ZHANG Z, LIU C Y, LIU X J, et al.Analysis of milling vibration state based on the energy entropy of WPD[J]. Journal of mechanical engineering, 2018, 54(21): 57-62.
[7] 申戬林, 王灵梅, 郭东杰, 等. 基于改进小波包与包络谱的风电机组传动系统的故障诊断方法研究[J]. 太阳能学报, 2014, 35(9): 1771-1777.
SHEN J L, WANG L M, GUO D J, et al.Research on fault diagnosis method based on improved wavelet packet envelop spectrum of wind turbine transmission system[J]. Acta energiae solaris sinica, 2014, 35(9): 1771-1777.
[8] 柴慧理, 叶美桃, 王志坚. 基于提高变分模态分解的齿轮箱复合故障特征提取[J]. 机械传动, 2018, 42(7): 151-156.
CHAI H L, YE M T, WANG Z J.Compound fault feature extraction of gearbox based on improve variational mode decomposition[J]. Journal of mechanical transmission, 2018, 42(7): 151-156.
[9] DHAMANDE L S, CHAUDHARI M B.Compound gear-bearing fault feature extraction using statistical features based on time-frequency method[J]. Measurement, 2018, 125: 63-77.
[10] 柳孝云, 张相芬, 马燕. 基于小波包改进算法的管道泄漏特征信号提取[J]. 计算机应用与软件, 2015, 32(5): 241-245.
LIU X Y, ZHANG X F, MA Y.Pipeline leakage characteristic signal extraction based on improved wavelet packet algorithm[J]. Computer applications and software, 2015, 32(5): 241-245.
[11] 杨志凌, 姚治业, 王瑞明. 基于HMM的风电机组齿轮箱故障诊断研究[J]. 太阳能学报, 2017, 38(9): 2574-2581.
YANG Z L, YAO Z Y, WANG R M.Study on fault diagnosis of gearbox based on HMM[J]. Acta energiae solaris sinica, 2017, 38(9): 2574-2581.
[12] RAFIEE J, TSE P W, HARIFI A, et al.A novel technique for selecting mother wavelet function using an intelligent fault diagnosis system[J]. Expert systems with applications, 2009, 36(3): 4862-4875.