基于VMD倒频变换的风电机组齿轮箱复合故障诊断

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摘要风电机组齿轮箱结构复杂，零部件多，可能产生复合故障。同一旋转轴上的不同零部件（齿轮、轴承等）的故障信息往往具有相同的转频边带频率成分，分布在信号频谱的不同频带中，倒频谱对边带频率信息进行了压缩处理，不能识别此类复合故障。基于窄带倒谱变换的思想，本文将变分模态分解(variational modal decomposition，VMD)应用到振动信号的对数谱中，提出了一种基于变分模态分解（VMD）倒频变换的方法，实现了对这一复合故障的精细诊断。通过仿真信号和风电场实例信号的验证，相比于窄带倒谱变换，该方法在有效区分此类风电机组齿轮箱复合故障的基础上，改善了频域和倒频域的分辨率，拥有更高的诊断定位精度。

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	吴岚1
	柳亦兵1
	吴仕明1
	2
	任锦胜1
	2
	滕伟1

关键词 ：变分模态分解, 倒频谱, 复合故障, 风电机组

Abstract：The wind turbine gearboxes have complex structures and many parts, which may generate compound faults. The fault information of different parts (gears, bearings, etc.) on the same rotating shaft often have the same sideband components, distributed in different frequency bands of the signal frequency spectrum. The cepstrum compresses the sideband information and cannot identify such compound faults. Based on the idea of narrow-band cepstrum transformation, this paper applies the variational modal decomposition (VMD) to the logarithmic spectrum to achieve a fine diagnosis of this composite fault. Through the verification of simulated signals and actual signals from wind farms, the method is effective in distinguishing such wind turbine gearbox compound faults. Compared with the narrow-band cepstrum transformation, the method improves the resolutions of frequency domain and cepstrum domain, and possesses higher diagnostic localization accuracy.

Key words： Variational Mode Decomposition Cepstrum Composite fault Wind turbine

收稿日期: 2023-01-28 出版日期: 2023-12-28

引用本文:

吴岚1，柳亦兵1，吴仕明1,2，任锦胜1,2，滕伟1. 基于VMD倒频变换的风电机组齿轮箱复合故障诊断[J]. 振动与冲击, 2023, 42(24): 221-227.
WU Lan1,LIU Yibing1,WU Shiming1,2,REN Jinsheng1,2,TENG Wei1. Composite fault diagnosis of wind turbine gearboxes based on VMD cepstral transform. JOURNAL OF VIBRATION AND SHOCK, 2023, 42(24): 221-227.

链接本文:

http://jvs.sjtu.edu.cn/CN/ 或 http://jvs.sjtu.edu.cn/CN/Y2023/V42/I24/221

[1] 陈雪峰,郭艳婕,许才彬,等.风电装备故障诊断与健康监测研究综述[J].中国机械工程,2020,31(02):175-189.
Chen X F, Guo Y J, Xu C B, et. Review of Fault Diagnosis and Health Monitoring for Wind Power Equipment[J]. China Mechanical Engineering,2020,31(02):175-189.
[2] 张振海,王维庆,王海云,等.基于HCS-GWO-MSVM的风电机组齿轮箱复合故障诊断研究[J].太阳能学报,2021,42(10):176-182.
Zhang Z H, Wang W Q, Wang H Y, et. Research on composite fault diagnosis of wind turbine gearbox based on HCS-GWO-MSVM[J]. Acta Energiae Solaris Sinica,2021,42(10):176-182.
[3] Kim Y, Ha J M, Na K,et al. Cepstrum-assisted empirical wavelet transform (CEWT)-based improved demodulation analysis for fault diagnostics of planetary gearboxes[J]. Measurement,2021,183.
[4] 胡爱军,严家祥,白泽瑞.基于MOMEDA和增强倒频谱的风电机组齿轮箱多故障诊断方法[J].振动与冲击,2021,40(07):268-273.
Hu A J, Yan J Y, Bai Z Y. Multi-fault diagnosis method for wind turbine gearbox based on
MOMEDA and enhanced cepstrum[J]. Journal of Vibration and Shock,2021,40(07):268-273.
[5] J. Guo, Y. Li and J. Xiang. An improved cepstrum analysis method to diagnose faults in bearings. 2021 7th International Conference on Condition Monitoring of Machinery in Non-Stationary Operations (CMMNO),2021,pp.240-243.
[6] 柳亦兵, 滕伟. 一种利用窄带倒谱变换的机械振动信号处理方法：CN110333071B [P]. 2021-09-10.
Liu Y B, Teng W. A mechanical vibration signal processing method based on narrow-band cepstrum transformation[P].CN110333071B,2019.
[7] 李肖,冯志鹏.基于变分模式分解的行星齿轮箱齿轮组合故障频率解调分析[J].振动与冲击,2020,39(03):38-45+51.
Li S, Feng Z B. Combined fault frequency demodulation analysis for a planetary gearbox based on VMD[J]. Journal of Vibration and Shock,2020,39(03):38-45+51.
[8] Li G, Tang G, Luo G, et al. Underdetermined blind separation of bearing faults in hyperplane space with variational mode decomposition[J]. Mechanical Systems and Signal Processing,2019,120(APR.1):83-97.
[9] Chen A, Kurfess T R. Signal processing techniques for rolling element bearing spall size estimation[J]. Mechanical Systems and Signal Processing,2019,117:16-32.
[10] WANG Y X, MARKERT R , XIANG J W , et al. Research on variational mode decomposition and its application in detecting rub-impact fault of the rotor system[J]. Mechanical Systems & Signal Processing,2015.
[11] 江星星,宋秋昱,杜贵府,等.变分模式分解方法研究与应用综述[J].仪器仪表学报,2022,1-24.
Jiang X X, Song Q Y, Du G F, et. A review on research and application of variational mode decomposition[J].Chinese Journal of Scientific Instrument,2022,1-24.
[12] Yang H B, Liu S L, Zhang H L. Adaptive estimation of VMD modes number based on cross correlation coefficient[J]. Journal of Vibroengineering,2017.