RESEARCH ON BEARING COMPOUND FAULT DIAGNOSIS METHODS BASED ON AVMD AND WDK FOR WIND TURBINE GEARBOX

Kong Xiaojia; Meng Liang; Xu Tongle; Yuan Wei; Yuan Maojun; Sun Yanfei

doi:10.19912/j.0254-0096.tynxb.2021-0778

PDF(2426 KB)

Acta Energiae Solaris Sinica ›› 2022, Vol. 43 ›› Issue (12) : 206-213. DOI: 10.19912/j.0254-0096.tynxb.2021-0778

RESEARCH ON BEARING COMPOUND FAULT DIAGNOSIS METHODS BASED ON AVMD AND WDK FOR WIND TURBINE GEARBOX

Kong Xiaojia^1,2, Meng Liang¹, Xu Tongle¹, Yuan Wei¹, Yuan Maojun³, Sun Yanfei¹

Author information +

History +

Abstract

In order to solve the difficulty of bearing weak compound fault feature extraction under strong background noise, a bearing compound fault diagnosis method based on AVMD and WDK for wind turbine gearbox is proposed in this paper. Firstly, an adaptive learning particle swarm optimization （ALPSO） algorithm is introduced, and the average envelope entropy is adopted as the fitness function of ALPSO to search for the optimal influence parameters of the variational mode decomposition, thus the adaptive variational mode decomposition （AVMD） is constructed. Secondly, the Wasserstein distance kurtosis（WDK） index is proposed to screen the effective modal components combining the advantages of Wasserstein distance and kurtosis, and the selected effective modal components are reconstructed. Thirdly, the reconstructed signal is analyzed through envelope spectrum analysis to realize the bearing compound fault diagnosis. Finally, the AVMD-WDK method is applied to the bearing fault diagnosis for a 2 MW wind turbine gearbox in a wind field. The experimental results show that the proposed method can effectively extract the weak fault features of bearings and realize the bearing compound fault diagnosis accurately.

Key words

wind turbine / compound fault / gearbox / adaptive variational mode decomposition / Wasserstein distance kurtosis

Cite this article

EndNote

Ris (Procite)

Bibtex

Download Citations

Kong Xiaojia, Meng Liang, Xu Tongle, Yuan Wei, Yuan Maojun, Sun Yanfei. RESEARCH ON BEARING COMPOUND FAULT DIAGNOSIS METHODS BASED ON AVMD AND WDK FOR WIND TURBINE GEARBOX[J]. Acta Energiae Solaris Sinica. 2022, 43(12): 206-213 https://doi.org/10.19912/j.0254-0096.tynxb.2021-0778

References

[1] CHEN J L, PAN J, LI Z P, et al.Generator bearing fault diagnosis for wind turbine via empirical wavelet transform using measured vibration signals[J]. Renewable energy, 2016, 89: 80-92.
[2] DRAGOMIRETSKIY K, ZOSSO D.Variational mode decomposition[J]. IEEE transactions on signal processing, 2014, 62(3): 531-544.
[3] LI Z X, JIANG Y, GUO Q, et al.Multi-dimensional variational mode decomposition for bearing-crack detection in wind turbines with large driving-speed variations[J]. Renewable energy, 2018, 116: 55-73.
[4] LI Y, CHENG G, LIU C, et al.Study on planetary gear fault diagnosis based on variational mode decomposition and deep neural networks[J]. Measurement, 2018, 130: 94-104.
[5] MA J, WU J D, WANG X D.Incipient fault feature extraction of rolling bearings based on the MVMD and teager energy operator[J]. ISA transactions, 2018, 80: 297-311.
[6] LIAN J J, LIU Z, WANG H J, et al.Adaptive variational mode decomposition method for signal processing based on mode characteristic[J]. Mechanical systems and signal processing, 2018, 107: 53-77.
[7] 党建, 罗燚, 田录林, 等. 基于优化的VMD融合信息熵和FA_PNN的风电机组齿轮箱故障诊断[J]. 太阳能学报, 2021, 42(1): 198-204.
DANG J, LUO Y, TIAN L L, et al.Fault diagnosis of wind turbine gearbox based on optimized VMD fusion information entropy and FA_PNN[J]. Acta energiae solaris sinica, 2021, 42(1): 198-204.
[8] 齐咏生, 白宇, 高胜利, 等.基于AVMD和谱相关分析的风电机组轴承故障诊断[J]. 太阳能学报, 2019, 40(7): 2053-2063.
QI Y S, BAI Y, GAO S L, et al.Fault diagnosis of wind turbine bearing based on AVMD and spectral correlation analysis[J]. Acta energiae solaris sinica, 2019, 40(7):2053-2063.
[9] YANG K, WANG G F, DONG Y, et al.Early chatter identification based on an optimized variational mode decomposition[J]. Mechanical systems and signal processing, 2019, 115: 238-254.
[10] WANG F, ZHANG H, LI K S, et al.A hybrid particle swarm optimization algorithm using adaptive learning strategy[J]. Information sciences, 2018, 436: 162-177.
[11] WANG X B, YANG Z X, YAN X A.Novel particle swarm optimization-based variational mode decomposition method for the fault diagnosis of complex rotating machinery[J].IEEE/ASME transactions on mechatronics, 2018, 23(1): 68-79.
[12] 周怡娜, 董宏丽, 张勇, 等. 基于VMD去噪和散布熵的管道信号特征提取方法[J]. 吉林大学学报(工学版), 2022, 52(4): 959-969.
ZHOU Y N, DONG H L, ZHANG Y, et al.Feature extraction method of pipeline signals based on VMD de-noising and dispersion entropy[J]. Journal of Jilin University (engineering and technology edition), 2022, 52(4): 959-969.
[13] LIPMAN Y, PUENTE J, DAUBECHIES I.Conformal wasserstein distance: II. computational aspects and extensions[J]. Mathematics of computation, 2013, 82(281): 331-381.
[14] 谷然, 陈捷, 洪荣晶, 等. 基于改进自适应变分模态分解的滚动轴承微弱故障诊断[J]. 振动与冲击, 2020, 39(8): 1-7, 22.
GU R, CHEN J, HONG R J, et al.Early fault diagnosis of rolling bearings based on adaptive variational mode decomposition and the teager energy operator[J]. Journal of vibration and shock, 2020, 39(8): 1-7, 22.