自适应模态总数变分模态分解方法及其性能评估

PDF(3296 KB)

振动与冲击 ›› 2023, Vol. 42 ›› Issue (10) : 251-262.

论文

王锦鸿1，李鸿光1，张文笛1，陈亚农2

作者信息 +

Adaptive modal total variational mode decomposition method and its performance evaluation

WANG Jinhong1， LI Hongguang1， ZHANG Wendi1，CHEN Yanong2

Author information +

文章历史 +

摘要

VMD方法目前广泛应用于转子系统故障诊断以及二维图像分解等领域，但传统VMD算法需要预先给出模态总数，且VMD对信号能量较弱的非中心频段存在重构效果差，含噪声下提取能力差，容易遗漏故障特征等问题；基于上述问题对VMD修改了信号的约束准则，并引入信号未处理以及残差信号的多元约束目标，使得模态总数可通过收敛得到；对模拟内圈轴承信号和实验信号的时频域重构分析以及分解模态信号的分析，对比三种算法的在非中心频段的重构效果以及特征提取能力的优劣。

Abstract

The VMD method is widely used in the fields of rotor system fault diagnosis and two-dimensional image decomposition, but the traditional VMD algorithm needs to give the total number of modes in advance, and the VMD has poor reconstruction effect on the non-center frequency band with weak signal energy, poor extraction ability under noise, and easy to miss fault features; Based on the above problems, this paper modifies the constraint criterion of signal for VMD, and introduces the constraint objectives of signal unprocessed and residual signal, so that the total number of modes can be obtained by convergence. In this chapter, the time-frequency domain reconstruction analysis of simulated inner ring bearing signals and experimental signals and the analysis of decomposed modal signals are compared, and the reconstruction effect of the three algorithms in the non-center frequency band and the advantages and disadvantages of feature extraction ability are compared.

导出引用

王锦鸿1，李鸿光1，张文笛1，陈亚农2. 自适应模态总数变分模态分解方法及其性能评估[J]. 振动与冲击, 2023, 42(10): 251-262

WANG Jinhong1， LI Hongguang1， ZHANG Wendi1，CHEN Yanong2. Adaptive modal total variational mode decomposition method and its performance evaluation[J]. Journal of Vibration and Shock, 2023, 42(10): 251-262

参考文献

[1] 周川, 伍星, 刘畅,等.基于EMD和模糊C均值聚类的滚动轴承故障诊断[J]. 昆明理工大学学报(理工版), 2009, 34(6):34-39.
ZHOU Chuan, WU Xing, LIU Chang, et al. Rolling bearing fault diagnosis based on EMD and fuzzy C means clustering [J]. Journal of Kunming University of Science and Technology (Science and Technology Edition), 2009, 34(6):34-39.

[2] 彭畅, 柏林, 谢小亮.基于EEMD、度量因子和快速峭度图的滚动轴承故障诊断方法[J]. 振动与冲击, 2012, 31(20):4.
PENG Chang, Berlin, XIE Xiao-liang. Fault diagnosis method of rolling element bearings based on EEMD,measure-factor and fast kurtogram [J]. Vibration and Shock, 2012, 31(20):4.
[3] 张可, 周东华, 柴毅．复合故障诊断技术综述［J］．控制理论与应用,2015,32(9):1143-1157.
ZHANE Ke, ZHOU Dong-hua, CHAI Yi. Review of multiple fault diagnosis methods［J］．Control Theory and Application, 2015, 32(9):1143-1157.
[4] 胡爱军, 白泽瑞, 赵军. 参数优化VMD结合1.5维谱的滚动轴承复合故障特征分离方法[J]. 振动与冲击, 2020, 39(11):9.
HU Ai-jun, BAI Ze-rui, ZHAO Jun. Compound fault features separation method of rolling bearing based on parameter optimization VMD and 1.5 dimension spectrum [J]. Vibration and Shock, 2020, 39(11):9.
[5] 杨世锡, 胡劲松, 吴昭同,等. 旋转机械振动信号基于EMD的希尔伯特变换和小波变换时频分析比较[J]. 中国电机工程学报, 2003, 23(6):6.
YANG Shi-xi, HU Jin-song, WU Zhao-tong, et al. The comparison of vibration signals' time-frequency analysis between EMD-based HT and WT method in rotating machinery [J]. Proceedings of the CSEE, 2003, 23(6):6.
[6] Dragomiretskiy Konstantin , Zosso D.Variational Mode Decomposition[J]. IEEE Transactions on Signal Processing, 2014, 62(3):531-544.
[7] Wang Y, Markert R, Xiang J, et al. Research on variational mode decomposition and its application in detecting rub-impact fault of the rotor system[J]. Mechanical Systems and Signal Processing, 2015, 60: 243-251.
[8] Ni P, Li J, Hao H, et al. Time-varying system identification using variational mode decomposition[J]. Structural Control and Health Monitoring, 2018, 25(6): e2175.
[9] Oberlin T, Meignen S, Perrier V. An alternative formulation for the empirical mode decomposition[J]. IEEE Transactions on Signal Processing, 2012, 60(5): 2236-2246.
[10] 胡欣球, 马立新. VMD-LSTM算法在短期负荷预测中的应用[J]. 电力科学与工程, 2018, 34(6):5.
HU Xin-qiu, MA Li-xin. Application of VMD-LSTM algorithm in short-term load forecasting [J]. Electric Power Science and Engineering, 2018, 34(6):5.
[11] Nazari M, Sakhaei S M. Variational mode extraction: A new efficient method to derive respiratory signals from ECG[J]. IEEE journal of biomedical and health informatics, 2017, 22(4): 1059-1067.
[12] Rehman N, Aftab H. Multivariate variational mode decomposition[J]. IEEE Transactions on Signal Processing, 2019, 67(23): 6039-6052.
[13] Chen Q, Lang X, Xie L, et al. Multivariate intrinsic chirp mode decomposition[J]. Signal Processing, 2021, 183: 108009.
[14] Stanković L, Mandić D, Daković M,et al.Time-frequency decomposition of multivariate multicomponent signals[J]. Signal Processing, 2018, 142: 468-479.
[15] Brajović M, Stanković L, Daković M. Decomposition of multichannel multicomponent nonstationary signals by combining the eigenvectors of autocorrelation matrix using genetic algorithm[J]. Digital Signal Processing, 2020, 102: 102738.
[16] Chen S, Dong X, Peng Z, et al. Nonlinear chirp mode decomposition: A variational method[J]. IEEE Transactions on Signal Processing, 2017, 65(22): 6024-6037.
[17] Wu, ZH, Huang, NE Ensemble empirical mode decomposition: a noise-assisted data analysis method[J]. Advances in adaptive data analysis, 2009, 1(01): 1-41.
[18] 杨龙, 陈建明. 语音增强算法及进展[J]. 电声技术, 2015(7):6.
YANG Long, CHEN Jian-ming. The review of speech enhancement algorithms and evolution [J]. Audio Engineering, 2015(7):6.
[19] 汤杰, 陈剑, 杨斌. 基于IVMD的单通道盲源分离方法及其应用[J]. 组合机床与自动化加工技术, 2018(7):6.
TANG Jie, CHEN Jian, YANG Bin. Single-channel blind source separation based on IVMD and its applications [J]. Combined Machine Tool and Automated Machining Technology, 2018(7):6.
[20] Liu S, Yu K. Successive multivariate variational mode decomposition based on instantaneous linear mixing model[J]. Signal Processing, 2022, 190: 108311.
[21] Haskell B G, Yan L. Video transmission rate matching for multimedia communication systems: U.S. Patent 5,687,095[P]. 1997-11-11..
[22] Ghadimi E, Teixeira A, Shames I, et al. Optimal parameter selection for the alternating direction method of multipliers (ADMM): quadratic problems[J]. IEEE Transactions on Automatic Control, 2014, 60(3): 644-658.
[23] Upadhyay A, Pachori R B. Instantaneous voiced/non-voiced detection in speech signals based on variational mode decomposition[J]. Journal of the Franklin Institute, 2015, 352(7): 2679-2707.
[24] Wang Y, Zhou G. The Novel Successive Variational Mode Decomposition and Weighted Regularized Extreme Learning Machine for Fault Diagnosis of Automobile Gearbox[J]. Shock and Vibration, 2021, 2021.
[25] Lin J, Qu L. Feature extraction based on Morlet wavelet and its application for mechanical fault diagnosis[J]. Journal of sound and vibration, 2000, 234(1): 135–148.
[26] 王少君. 基于EEMD的滚动轴承微弱故障特征提取方法研究[D]. 石家庄铁道大学.
WANG Shao-jun. Research on the feature extraction method of rolling bearing weak fault based on EEMD[D]. Shijiazhuang Railway University.
[27] Wang Y, Liu F, Jiang Z, et al. Complex variational mode decomposition for signal processing applications[J]. Mechanical systems and signal processing, 2017, 86: 75-85.
[28] 白欣田. 滚动轴承故障模拟实验系统设计及故障诊断方法研究[D]. 中国矿业大学, 2021.
BAI Xin-tian. Research on the design and fault diagnosis method of rolling bearing fault simulation experimental system[D]. China University of Mining and Technology, 2021.
[29] Liu W, Cao S, Chen Y. Applications of variational mode decomposition in seismic time-frequency analysis[J]. Geophysics, 2016, 81(5): V365-V378.
[30] Nazari M , Sakhaei S M . Successive Variational Mode Decomposition[J]. Signal Processing, 2020:107610.