[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. |