FAULT DIAGNOSIS METHOD OF WIND TURBINE&#x02019;S GEARBOX BASED ON COMPOSITE MULTISCALE DISPERSION ENTROPY OF OPTIMISED VMD AND LSTM

Wang Hongwei; Sun Wenlei; Zhang Xiaodong; He Li

doi:10.19912/j.0254-0096.tynxb.2020-0457

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Acta Energiae Solaris Sinica ›› 2022, Vol. 43 ›› Issue (4) : 288-295. DOI: 10.19912/j.0254-0096.tynxb.2020-0457

Topics on Key Technologies for Safety of Electrochemical Energy Storage Systems and Echelon Utilization of Decommissioned Power Batteries

FAULT DIAGNOSIS METHOD OF WIND TURBINE’S GEARBOX BASED ON COMPOSITE MULTISCALE DISPERSION ENTROPY OF OPTIMISED VMD AND LSTM

Wang Hongwei¹, Sun Wenlei¹, Zhang Xiaodong², He Li¹

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Abstract

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.

Key words

wind turbines / gearbox / fault detection / grey wolf optimizer / variational modal decomposition / normalized composite multiscale dispersion entropy / long short-term memeory network

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Wang Hongwei, Sun Wenlei, Zhang Xiaodong, He Li. FAULT DIAGNOSIS METHOD OF WIND TURBINE’S GEARBOX BASED ON COMPOSITE MULTISCALE DISPERSION ENTROPY OF OPTIMISED VMD AND LSTM[J]. Acta Energiae Solaris Sinica. 2022, 43(4): 288-295 https://doi.org/10.19912/j.0254-0096.tynxb.2020-0457

References

[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[J]. Proceedings of Royal Society of London, 1998, 454(1971): 903-995.
[2] DRAGOMIRETSKIY K, ZOSSO D.Variational mode decomposition[J]. IEEE transactions on signal processing, 2014, 32(3): 531-544.
[3] 李志农, 朱明.基于变分模态分解的机械故障诊断方法研究[J]. 兵工学报, 2017, 38(3): 593-599.
LI Z N, ZHU M.Research on mechanical fault diagnosis method based on variational mode decomposition[J]. Acta armamentarii, 2017, 38(3): 593-599.
[4] 李宏坤, 侯梦凡, 唐道龙, 等. 基于POVMD和CAF的低转速齿轮箱故障诊断[J]. 振动、测试与诊断, 2020, 40(1): 35-42.
LI H K, HOU M F, TANG D L, et al. Low speed gearbox fault diagnosis based on POVMD and CAF[J]. Journal of vibration, measurement & diagnosis, 2020, 40(1): 35-42.
[5] 焦博隆, 钟志贤, 刘翊馨, 等. 基于蝙蝠算法优化的变分模态分解的转子裂纹检测方法[J]. 振动与冲击, 2020, 39(6): 98-103, 124.
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[J]. Journal of vibration and shock, 2020, 39(6): 98-103,124.
[6] 张淑清, 李盼, 胡永涛, 等. 多重分形近似熵与减法FCM聚类的研究及应用[J]. 振动与冲击, 2015, 34(18): 205-209.
ZHANG S Q, LI P, HU Y T, et al. Application of multifractal approximate entropy and subtractive FCM clustering in gearbox fault diagnosis[J]. Journal of vibration and shock, 2015, 34(18): 205-209.
[7] 王广斌, 杜谋军, 韩清凯, 等. 基于多尺度子带样本熵和LPP的轴承故障诊断方法[J]. 振动与冲击, 2016, 35(20): 71-76, 97.
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[J]. Journal of vibration and shock, 2016, 35(20): 71-76, 97.
[8] 郑近德, 姜战伟, 代俊习, 等. 基于VMD的自适应复合多尺度模糊熵及其在滚动轴承故障诊断中的应用[J]. 航空动力学报, 2017, 32(7): 1683-1689.
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[J]. Journal of aerospace power, 2017, 32(7): 1683-1689.
[9] 郑近德, 潘海洋, 程军圣, 等. 基于复合多尺度模糊熵的滚动轴承故障诊断方法[J]. 振动与冲击, 2016, 35(8): 116-123.
ZHENG J D, PAN H Y, CHENG J S, et al. Composite mutil-scale fuzzy entropy based rolling bearing fault diagnosis method[J]. Journal of vibration and shock, 2016, 35(8): 116-123.
[10] ROSTAGHI M, AZAMI H.Dispersion entropy: a measure for time series snalysis[J]. IEEE signal processing letters, 2016, 23(5): 610-614.
[11] 付文龙, 谭佳文, 王凯.基于VMD散布熵与改进灰狼优化SVDD的轴承半监督故障诊断研究[J]. 振动与冲击, 2019, 38(22): 190-197.
FU W L, TAN J W, WANG K.Semi-supervised fault diagnosis of bearings based on the VMD dispersion entropy and improved SVDD with modified gre wolf optimizer[J]. Journal of vibration and shock, 2019, 38(22): 190-197.
[12] 皮骏, 马圣, 杜旭博, 等. 基于BQGA-ELM网络在滚动轴承故障诊断中的应用研究[J]. 振动与冲击, 2019, 38(18): 192-200.
PI J, MA S, DU X B, et al. Application of BAGA-ELM network in the fault diagnosis of rolling bearings[J]. Journal of vibration and shock, 2019, 38(18): 192-200.
[13] 于洋, 何明, 刘博, 等. 基于TL-LSTM的轴承故障声发射信号识别研究[J]. 仪器仪表学报, 2019, 40(5): 51-59.
YU Y, HE M, LIU B, et al. Research on acoustic emission signal recognition of bearing fault based on TL-LSTM[J]. Chinese journal of scientific instrument, 2019, 40(5): 51-59.
[14] MIRJALILI S, MIRJALILI S M, ANDREW L.Grey wolf optimizer[J]. Advances in engineering software, 2014, 69: 46-61.
[15] 魏昱洲, 许西宁.基于LSTM长短期记忆网络的超短期风速预测[J]. 电子测量与仪器学报, 2019, 33(2): 64-71.
WEI Y Z, XU X N.Ultra-short-term wind speed prediction model using LSTM networks[J]. Journal of electronic measurement and instrumentation, 2019, 33(2): 64-71.