1. Key Laboratory of Measurement Technology and Instrumentation of Hebei Province, Yanshan University, Qinhuangdao, Hebei 066004, China
2. Tangshan Polytechnic College, Tangshan, Hebei 063000, China
Abstract:Similar frequency signal separation is a difficult problem in fault diagnosis. As a new method of signal time frequency analysis, variational mode decomposition (VMD) has a higher resolution for signals with similar frequency. The number of decomposition levels, which can directly affect the decomposability, is first specified in VMD. Once over-decompose is likely to produce false frequency components, while under-decompose is easy to lose useful frequency components. Thus, a new method of similar frequency signal separation based on VMD and signal singular value decomposition is proposed. Firstly, appropriate decomposition levels is selected to over-decompose the signal, and then singular value decomposition is carried out on the components obtained by VMD, which can detect and eliminate false signal components, so as to separate similar frequency signal well. The effectiveness and feasibility of the proposed method are demonstrated by simulation signal and rolling bearing fault signal.
作者简介: 邢婷婷(1984-), 河北邢台人, 燕山大学博士研究生, 研究方向为振动信号时频分析和机械设备故障诊断等。Email: xttysu2016@sina. com
引用本文:
邢婷婷,关阳,刘子涵,樊凤杰,孟宗. 基于变分模态分解和奇异值分解的频率相近信号分离方法[J]. 计量学报, 2020, 41(11): 1404-1409.
XING Ting-ting,Guan Yang,LIU Zi-han,FAN Feng-jie,MENG Zong. Similar Frequency Signal Separation Based on VMD and Singular Value Decomposition. Acta Metrologica Sinica, 2020, 41(11): 1404-1409.
[1]McDonald G L, Zhao Q. Multipoint Optimal Minimum Entropy Deconvolution and Convolution Fix: Application to vibration fault detection[J]. Mechanical Systems and Signal Processing, 2016, 82(5): 461-477.
[2]Liu S K, Tang G J, Wang X L, et al. Time-Frequency Analysis Based on Improved Variational Mode Decomposition and Teager Energy Operator for Rotor System Fault Diagnosis[J]. Mathematical Problems in Engineering, 2016, (11): 1-9.
[3]Huang N E, Shen Z, Long S R. The Empirical Mode Decomposition and the Hilbert Spectrum for Nonlinear and Nonstationary Time Series Analysis [J]. Proc R Soc Lond A, 1998, 454(1): 903-995.
[4]胡茑庆, 陈徽鹏, 程哲, 等. 基于经验模态分解和深度卷积神经网络的行星齿轮箱故障诊断方法[J]. 机械工程学报, 2019, 55(7): 9-18
Hu N Q, Chen H P, Cheng Z, et al. Fault Diagnosis for Planetary Gearbox Based on EMD and Deep Convolutional Neural Networks[J]. Journal of Mechanical Engineering, 2019, 55(7): 9-18
[5]王栋, 丁雪娟. 基于包络解调随机共振和CEEMD的机械早期微弱故障诊断方法研究[J]. 计量学报, 2016, 37(2): 185-190.
Wang D, Ding X J. Study on Mechanical Early Weak Fault Diagnosis method Based on CEEMD and Envelope Demodulation Stochastic Resonance[J]. Acta Metrologica Sinica, 2016, 37(2): 185-190.
[6]孟宗, 刘东, 岳建辉, 等. 基于DEMD局部时频熵和SVM的风电齿轮箱故障诊断方法研究[J]. 计量学报, 2017, 38(4): 449-452.
Meng Z, Liu D, Yue J H, et al. Wind Power Gear Box Fault Diagnosis Based on DEMD Local Frequency Entropy and SVM[J]. Acta Metrologica Sinica, 2017, 38(4): 449-452.
[7]Feldman M. Analytical Basics of the EMD: Two Harmonics Decomposition[J]. Mechanical Systems and Signal Processing, 2009, 23(7): 2059-2071.
[8]Rilling G, Flandrin P. One or two frequencies? The Empirical Mode Decomposition Answers[J]. IEEE Transactions on Signal Processing, 2008, 56(1): 85-95.
[9]袁静, 何正嘉, 訾艳阳. 基于提升多小波的机电设备复合故障分离和提取[J]. 机械工程学报, 2010, 46(1): 79-85.
Yuan J, He Z J, Zi Y Y. Separation and Extraction of Electromechanical Equipment Compound Faults Using Lifting Multiwavelets[J]. Journal of Mechanical Engineering, 2010, 46(1): 79-85.
[10]Dragomiretskiy K, Zosso D. Variational Mode Decomposition[J]. IEEE Transactions on Signal Processing, 2014, 62(3): 531-544.
[11]Yi C C, Lv Y, Zhang D. A Fault Diagnosis Scheme for Rolling Bearing Based on Particle Swarm Optimization in Variational Mode Decomposition[J]. Shock and Vibration, 2016, (2): 1-10.
[12]唐贵基, 王晓龙. 参数优化变分模态分解方法在滚动轴承早期故障诊断中的应用[J]. 西安交通大学学报, 2015, (5): 73-81.
Tang G J, Wang X L. Parameter Optimized Variational Mode Decomposition Method with Application to Incipient Fault Diagnosis of Rolling Bearing[J]. Journal of Xi'an Jiaotong University, 2015, (5): 73-81.
[13]刘长良, 武英杰, 甄成刚. 基于变分模态分解和模糊C均值聚类的滚动轴承故障诊断[J]. 中国电机工程学报, 2015, 35(13): 3358-3365.
Liu C L, Wu Y J, Zhen C G. Rolling Bearing Fault Diagnosis Based on Variational Mode Decomposition and Fuzzy C Means Clustering[J]. Proceedings of the CSEE, 2015, 35(13): 3358-3365.
[14]Mohanty S, Gupta K K, Raju K S. Comparative study between VMD and EMD in bearing fault diagnosis[C]//IEEE. 2014 9th International Conference on Industrial and Information Systems (ICIIS). 2014.
[15]郑慧峰, 喻桑桑, 王月兵, 等. 基于经验模态分解和奇异值分解的振动声调制信号分析方法研究[J]. 计量学报, 2016, 37(4): 398-401.
Zheng H F, Yu S S, Wang Y B, et al. Research on The Analysis Method of Vibro-acoustic Modulation Signal Based on EMD and SVD[J]. Acta Metrologica Sinica, 2016, 37(4): 398-401.
[16]程一峰, 刘增力. 改进的K-奇异值分解图像去噪算法[J]. 计量学报, 2018, 39(3): 332-336.
Cheng Y F, Liu Z L. Improved K-SVD image denoising algorithm[J]. Acta Metrologica Sinica, 2018, 39(3): 332-336.
[17]孟宗, 谷伟明, 胡猛, 等. 基于改进奇异值分解和经验模式分解的滚动轴承早期微弱故障特征提取[J]. 计量学报, 2016, 37(4): 406-410.
Meng Z, Gu W M, Hu M, et al. Fault Feature Extraction of Rolling Bearing Incipient Fault Based on Improved Singular Value Decomposition and EMD[J]. Acta Metrologica Sinica, 2016, 37(4): 406-410.
[18]时培明,苏晓,袁丹真, 等.基于VMD和变尺度多稳随机共振的微弱故障信号特征提取方法[J]. 计量学报, 2018, 39(4): 515-520.
Shi P M, Su X, Yuan D Z, et al. A New Feature Extraction Method of Weak Fault Signal Based
on VMD and Re-scaling Multi-stable Stochastic Resonance[J]. Acta Metrologica Sinica, 2018, 39(4): 515-520.
[19]何群,杜硕,王煜文,等. 基于变分模态分解与深度信念网络的运动想象分类识别研究[J]. 计量学报, 2020, 41(1): 90-99.
He Q, Du S, Wang Y W, et al. The Classification of EEG Induced by Motor Imagery Based on Variational Mode Decomposition and Deep Belief Network[J]. Acta Metrologica Sinica, 2020, 41(1): 90-99.
[20]付秀伟, 高兴泉. 基于傅里叶分解与奇异值差分谱的滚动轴承故障诊断方法[J]. 计量学报, 2018, 39(5): 688-692.
Fu X W, Gao X Q. Rolling Bearing Fault Diagnosis Based on FDM and Singular Value Difference Spectrum[J]. Acta Metrologica Sinica, 2018, 39(5): 688-692.