Fault diagnosis method of wind turbine's gearbox based on composite multiscale dispersion entropy of optimised VMD and LSTM

被引：0
|
作者：
Wang H. [1 ]
Sun W. [1 ]
Zhang X. [2 ]
He L. [1 ]
机构：
[1] School of Mechanical Engineering, Xinjiang University, Urumqi
[2] School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an
来源：
Taiyangneng Xuebao/Acta Energiae Solaris Sinica | 2022年 / 43卷 / 04期
关键词：
Fault detection; Gearbox; Grey wolf optimizer; Long short-term memeory network; Normalized composite multiscale dispersion entropy; Variational modal decomposition; Wind turbines;
D O I：
10.19912/j.0254-0096.tynxb.2020-0457
中图分类号：
学科分类号：
摘要：
A data driven diagnosis method based on acceleration signals for the gearbox in wind turbine is proposed, which on the basis of the grey wolves optimised variational modal decomposition (AGWO-VMD), normalized composite multiscale dispersion entropy (NCMDE) and long short-term memeory (LSTM), the gearbox faults diagnosis is realized rapidly. Firstly, the discrete signal in time domain is converted to angular domain. Secondly, AGWO-VMD algorithm is used to decompose the signal adaptively, and NCMDE algorithm is used to extract fault features as feature vectors from both original and decomposed signals. At last, the LSTM model is used for intelligentive classification of feature vectors. The proposed method is validated by 100 groups of data under 6 types of faults collected from WTDS, and the result shows that, it can recognize the right type of gearbox's fault rapidly and effectively. © 2022, Solar Energy Periodical Office Co., Ltd. All right reserved.
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页码：288 / 295
页数：7
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  • [1] HUANG N E, SHEN Z, LONG S R, Et al., The empirical mode decomposition and the hilbert Spectrum for nonlinear and non-stationary time series analysis, Proceedings of Royal Society of London, 454, 1971, pp. 903-995, (1998)
  • [2] DRAGOMIRETSKIY K, ZOSSO D., Variational mode decomposition, IEEE transactions on signal processing, 32, 3, pp. 531-544, (2014)
  • [3] LI Z N, ZHU M., Research on mechanical fault diagnosis method based on variational mode decomposition, Acta armamentarii, 38, 3, pp. 593-599, (2017)
  • [4] LI H K, HOU M F, TANG D L, Et al., Low speed gearbox fault diagnosis based on POVMD and CAF, Journal of vibration, measurement & diagnosis, 40, 1, pp. 35-42, (2020)
  • [5] JIAO B L, ZHONG Z X, LIU Y X, Et al., Rotor crack detection method based on variational mode decomposition based on optimization parameters of bat algorithm, Journal of vibration and shock, 39, 6, pp. 98-103, (2020)
  • [6] ZHANG S Q, LI P, HU Y T, Et al., Application of multifractal approximate entropy and subtractive FCM clustering in gearbox fault diagnosis, Journal of vibration and shock, 34, 18, pp. 205-209, (2015)
  • [7] WANG G B, DU M J, HAN Q K, Et al., A bearing fault diagnosis method based on multi-scal sub-band sample entropy and LPP, Journal of vibration and shock, 35, 20, pp. 71-76, (2016)
  • [8] ZHENG J D, JIANG Z W, DAI J X, Et al., VMD based adaptive composite multiscale fuzzy entropy and its application to fault diagnosis of rolling bearing, Journal of aerospace power, 32, 7, pp. 1683-1689, (2017)
  • [9] ZHENG J D, PAN H Y, CHENG J S, Et al., Composite mutil-scale fuzzy entropy based rolling bearing fault diagnosis method, Journal of vibration and shock, 35, 8, pp. 116-123, (2016)
  • [10] ROSTAGHI M, AZAMI H., Dispersion entropy: a measure for time series snalysis, IEEE signal processing letters, 23, 5, pp. 610-614, (2016)
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