Abstract：Aiming at problems of early fault features of rolling bearings being weak and the deficiency of the frequency sliced wavelet transformation (FSWT)’s extracting fault features under strong background noise,a new fault feature extracting method using FSWT and joint de-noising of the variational mode decomposition(VMD) and the singular value decomposition (SVD) was proposed. Firstly,VMD was used to decompose adaptively a fault signal into a series of intrinsic mode functions (IMFs). Some IMFs containing richest fault information were selected to reconstruct a signal based on the kurtosis criterion. Then SVD was used to de-noise the reconstructed signal again and improve its signal-noise ratio. Finally,FSWT was done for the reconstructed signal to highlight fault features’time-frequency distribution information and extract fault features. The analysis results of simulated signals and test data showed that this method can effectively eliminate noise effects and clearly extract the fault signal’s characteristic frequency to realize the accurate recognition of rolling bearings’faults.
马增强，张俊甲，张 安，阮婉莹. 基于VMD-SVD联合降噪和频率切片小波变换的滚动轴承故障特征提取[J]. 振动与冲击, 2018, 37(17): 210-217.
MA Zengqiang,ZHANG Junjia,ZHANG An,RUAN Wanying. Fault feature extraction of rolling bearings based on VMD-SVD joint de-noising and FSWT. JOURNAL OF VIBRATION AND SHOCK, 2018, 37(17): 210-217.
[1] Ram P B, Pradip S. A new technique to reduce cross terms in the Wigner distribution [J]. Digital Signal Processing, 2007,17(2):466-474.
[2] 王晓冬，何正嘉，訾艳阳．滚动轴承故障诊断的多小波谱峭度方法[J]西安交通大学学报，2010,44(3):77-81．
WANG Xiao-dong,HE Zheng-jia,ZI Yan-yang.Spectral kurtosis of multiwavelet for fault diagnosis of rolling bearing．Journal of Xi'an Jiao tong University，2010,44(3):77-81．
[3] Yan Z，Miyamoto A，Jiang Z．Frequency slice wavelet transform for transient vibration response analysis [J]. Mechanical Systems and Signal Processing，2009，23 (5) :1474-1489.(5)
[4] Yan Z, Miyamoto Z, Jiang Z, et al.. An overall theoretical description of frequency slice wavelet transform[J]．Mechanical Systems and Signal Processing，2010，24(2): 491 － 507．
[5] 钟友先，赵春华，陈保家，等.基于形态自相关和时频切片分析的轴承故障诊断方法[J]. 振动与冲击，2014,33(4):11-16.
ZHONG Xian-you, ZHAO Chun-hua Yang,CHEN Bao-jia, et al. Bearing fault diagnosis method based on morphological filtering，time-delayed autocorrelation and time-frequency slice analysis[J].Journal of vibration and shock, 2014,33(4):11-16.
[6] 段晨东，高 强.基于时频切片分析的故障诊断方法及应用[J].振动与冲击，2011，30(9):1-5．
DUAN Chen-dong, GAO Qiang. Noval fault diagnosis approach using time-frequency slice analysis and its application[J].Journal of Vibration and Shock,2011,30(9): 1-5．
[7] 周福成，唐贵基，廖兴华.奇异值分解结合频率切片小波的齿轮故障特征提取[J].噪声与振动控制，2016,36(5):139-143.
ZHOU Fu-cheng,TANG Gui-ji,LIAO Xing-hua.A Method of Fault Characteristic Extraction of Gears Based on Singular Value Decomposition and Frequency Slice Wavelet Transform[J].Noise and vibration control，2016,36(5):139-143.
[8] Konstantin Dragomiretskiy, Dominique Zosso. Variational Mode Decomposition [J]. Transactions on Signal Processing, 2013, 10 (1109):1-15.
[9] Wu Z H, Huang N E. Ensemble Empirical Mode Decomposition: a noise-assisted data analysis method [J]. Advances in Adaptive Data Analysis, 2009, 1(1): 1-41.
[10] 王建国，李健，万旭东．基于奇异值分解和局域均值分解的滚动轴承故障特征提取方法[J].机械工程学报，2015，51( 3) :104-110.
WANG Jian-guo, LI Jian, WAN Xu-dong. Fault feature extraction method of rolling bearings based on singular value decomposition and local mean decomposition[J].Power System Technology，2015，51( 3) :104-110.
[11] 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 (0):1-7.
Liu Changliang, Wu Yingjie, Zhen Chenggang. Rolling bearing fault diagnosis based on variational mode decomposition and fuzzy C means clustering [J]. Proceedings of the CSEE, 2015, 35 (0): 1-7.
[12] 马增强,李亚超,刘政,谷朝健.基于变分模态分解和Teager能量算子的滚动轴承故障特征提取[J]. 振动与冲击,2016, 35(13): 134-139.
MA Zeng-qiang,LI Ya-chao,LIU Zheng.et al. Rolling bearings' fault feature extraction based on variational mode decomposition and Teager energy operator[J].Journal of Vibration and Shock,,2016, 35(13): 134-139.
[13] 苏文胜，王奉涛，张志新等. EMD降噪和谱峭度法在滚动轴承早期故障诊断中的应用[J]. 振动与冲击，2010，29(3)：18-21.
Su Wen-sheng, Wang Feng-tao, Zhang Zhi-xin, et al. Application of EMD denoising and spectral kurtosis in early fault diagnosis of rolling element bearings[J]. Journal of vibration and shock, 2010, 29(3): 18-21+201.
[14] 赵洪山，郭双伟，高 夺.基于奇异值分解和变分模态分解的轴承故障特征提取[J]. 振动与冲击，2016,35(22):183-188.
ZHAO Hong-shan，GUO Shuang-wei，GAO Duo.Fault feature extraction of bearing faults based on singular value decomposition and variational modal decomposition[J].Journal of vibration and shock，2016,35(22):183-188.
[15] 王建国，李 健，刘颖源.一种确定奇异值分解降噪有效秩阶次的改进方法[J].振动与冲击，2014,33(12):176-180.
WANG Jian-guo, LI Jian, LIU Ying-yuan.An improved method for determining effective order rank of SVD denoising[J]. Journal of vibration and shock, 2014,33(12):176-180.
[16] 张宇辉，刘梦婕，黄南天等.频率切片小波变换在局部放电信号分析中的应用[J].高电压技术，2015，41(7)：2283-2293.
ZHANG Yuhui,LIU Mengjie,HUANG Nantian.et al.Application of Frequency Slice Wavelet Transform in Partial Discharge Signal Analysis [J].High voltage engineering,2015,41(7)：2283-2293.
