针对强背景噪声下轴承微弱复合故障特征提取困难的问题,提出一种基于自适应变分模态分解（AVMD）和优化的Wasserstein距离指标（WDK）的风电齿轮箱轴承复合故障诊断方法。首先,引入自适应学习粒子群优化算法（ALPSO）,以平均包络熵作为ALPSO的适应度函数来搜索变分模态分解的最佳影响参数,从而构造AVMD;其次,结合Wasserstein距离（WD）和峭度优点,提出WDK指标筛选有效模态分量,并对筛选的有效模态分量进行重构;然后,通过对重构信号进行包络谱分析实现轴承复合故障的诊断;最后,将所提AVMD-WDK方法应用于某风场2 MW风电齿轮箱轴承振动信号的故障诊断。结果表明,该方法能有效提取轴承的微弱故障特征,实现轴承复合故障的精确诊断。
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.
关键词
风电机组 /
复合故障 /
齿轮箱 /
自适应变分模态分解 /
优化的Wasserstein距离指标（WDK）
Key words
wind turbine /
compound fault /
gearbox /
adaptive variational mode decomposition /
Wasserstein distance kurtosis
{{custom_sec.title}}
{{custom_sec.title}}
{{custom_sec.content}}
参考文献
[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.
基金
国家自然科学基金（51805299）; 山东省面上基金（ZR2021ME221）
PDF(2426 KB)
