The feature extraction from noise contaminated vibration signals is very difficult.The synchrosqueezed wavelet transform(SST) was introduced in the process of vibration signal denoising.To solve the difficulty of slecting intrinsic mode functions (IMF)s, a novel denoising method for vibration signals based on the instantaneous frequency complexity and autocorrelation coefficient kurtosis threshold of IMF was presented. The SST signal analysis method was ultilized to extract IMF components and then the Hilbert transform was applied to obtain the instantaneous frequency curve.IMF components were selected for reconstruction according to their corresponding component instantaneous frequency complexity. In order to further eliminate the noise effect,the correlation coefficient threshold method based on kurtosis value was employed to select once more the components for reconstructiing the original signal which can finally realize the original signal noise reduction.By experiments,the feasibility and effectiveness of the proposed method were verified by using simulated noise signals with different standard variances and bearing vibration signals of logistics machinery transmission equipments. Compared with other methods based on the ensemble empirical mode decomposition and wavelet transform, the results demonstrate that the proposed method is better than others.
[1]单锐,齐越.一种改进的小波阈值去噪算法[J].兰州理工大学学报，2014，40( 4) : 101-104.
Shan Rui,Qi Yue. Improved algorithm for denoising of wavelet threshold value[J]. Journal of Lanzhou University of Technology. 2014，40( 4) : 101-104.
[2]王之海,伍星. 基于二次相关加权阈值的滚动轴承声发射信号小波包降噪算法研究[J]. 振动与冲击,2015,34(21): 175- 180.
Wang Zhihai,Wu Xing. A new noise reduction method for rolling bearing acoustic emission signals based on wavelet packet transformation with quadratic correlation weighted threshold[J]. Journal of Vibration and Shock,2015,34(21): 175- 180.
[3]王奉涛,陈守海.对偶树复小波流行域降噪方法及其在故障诊断中的应用[J].机械工程学报,2014,50(21):159-163.
Wang Fengtao Chen Shouhai. Noise Reduction Based on Dual Tree Complex Wavelet Transform unfolding and Its Application in Fault Diagnosis[J].Journal of Mechanical Engineering, 2014,50(21):159-163.
[4]Yuan J, He Z J,Zi Y Y. Gear fault detection using customized multiwavelet lifting schemes[J].Mechanical System and Signal Processing,2010,24(5):1509-1528.
[5]杨绍普,赵志宏.改进的小波相邻系数降噪方法及其在机械故障诊断中的应用[J].机械工程学报,2013,49(17):137-141.
Yang Shaopu, Zhao Zhihong. Improved Wavelet Denoising Using Neighboring Coefficients and Its Application to Machinery Fault Diagnosis[J]. Journal of Mechanical Engineering, 2013,49(17):137-141.
[6]曲巍葳,高峰.基于噪声方差估计的小波阈值降噪研究[J].机械工程学报,2010,46(2):28-33.
QU Weiwei,GAO Feng. Study on wavelet threshold denoising algorithm based on estimation of noise variance[J]. Journal of Mechanical Engineering, 2010,46(2):28-33.
[7]张绍辉,李巍华. 基于特征空间降噪的局部保持投影算法及其在轴承故障分类中的应用[J]. 机械工程学报,2014,50(3):92-99.
ZHANG Shaohui, LI Weihua. Locality Preserving Projections Based on Feature Space Denoising and Its Application in Bearing Fault Classification[J]. Journal of Mechanical Engineering, 2014,50(3):92-99.
[8]曾鸣,杨宇. SVD 降噪算法及其在齿轮故障诊断中的应用[J]. 机械工程学报,2015,51(3):95-103.
ZENG Ming YANG Yu. SVD Based Denoising Method and Its Application to Gear Fault Diagnosis[J]. Journal of Mechanical Engineering, 2015,51(3):95-103.
[9]孙伟,熊邦书.小波包降噪与LMD相结合的滚动轴承故障诊断方法[J[.振动与冲击,2012,31(18):153-156.
Sun Wei,Xiong Bangshu. Fault diagnosis of a rolling bearing using Wavelet packet de-noising and LMD[J]. Journal of Vibration and Shock, 2012, 31(18):153-156..
[10] Flandrin P,Rilling G. Empirical Mode Decomposition as A Filter Bank[J]. IEEE Signal Proc Lett. 2004,11(2):112-114.
[11]剡昌锋, 周礼龙. 基于EEMD 降噪与二次相位耦合的滚动轴承故障诊断[J]. 兰州理工大学学报,2016, 42(3):35-40.
Yan Changfeng, Zhou Lilong. Fault diagnosis of roller bearings based on EEMD denoising and quadratic phase coupling[J]. Journal of Lanzhou University of Technology, 2016, 42(3):35-40.
[12]陈仁祥,汤宝平.基于EEMD的振动信号自适应降噪方法[J].振动与冲击,2012,31(15):82-86.
Chen Renxiang,Tang Boping. Adaptive de-noising method based on ensemble empirical mode decomposition for vibration signal[J]. Journal of Vibration and Shock, 2012,31(15):82-86.
[13]张文忠,周蓉. 利用白噪声分解特征的EEMD阈值降噪方法[J]. 测绘科学技术学报,2013,30(3):255-259.
Zhang Wenzhong, Zhou Rong. A Threshold De-Noising Method Based on the Characteristics of White Noise Decomposed by EEMD[J]. Journal of Geomatics Science and Techndogy, 2013,30(3):255-259.
[14]陈仁祥,汤宝平.基于相关系数的EEMD转子振动信号降噪方法[J]. 振动、测试与诊断.2012,32(4):542-546.
Chen Renxiang,Tang Boping. De-noising method based on ensemble empirical mode decomposition for Rotor vibration signal[J]. Journal of Vilbration, Measurement&Diagnosis, 2012, 32(4) :542-546.
[15]Daubechies I,Lu Jiangfeng, Wu H T. Synchrosqueezed wavelet transforms: an empirical model decomposition like tool[J].Applied and Computational Harmonic Analysis,2011,30(2):243-261.
[16]Thakur G,Brevdo E.．The Synchrosqueezing algorithm for time-varying spectral analysis: robustness properties and new paleoclimate applications.[J]．Signal Processing，2013，93(5):1079-1094.
[17]熊炘,占锐. 同步压缩小波与希尔伯特－黄变换性能对比[J]. 振动、测试与诊断, 2015,35(6):1103-1109.
Xiong Xi,Zhan Rui. Comparison Study on Synchrosqueezed Wavelet Transform and Hilbert-Huang Transform[J], Journal of Vibration,Measurement&Diagnosis, 2015,35(6):1103-1109.
[18]张志超，张会星.同步压缩小波变换在油气检测中的应用 [J].中国煤炭地质,2016, 28(5):67-75.
Zhang Zhichao, Zhang Huixing. Application of Synchrosqueezing Wavelet Transform (SWT) in Hydrocarbon Detection[J]. Coal Geology of China, 2016, 28(5):67-75.
[19]秦晅,宋维琪. 基于同步压缩变换微地震弱信号提取方法研究[J]. 石油物探,2016,55(1):60-66.
Qin Xuan, Song Weiqi. Weak signal extraction method of microseismic data based on synchrosqueezing transform[J]. Geophysical Prospecting for Petroleum, 2016,55(1):60-66.
[20]刘晗,张建中. 应用同步挤压小波变换去除面波[J] .石油地球物理学报. 2016,51(1): 71-79.
Liu Han, Zhang Jianzhong. Surface wave removal with synchrosqueezing wavelet transform[J]. OGP, 2016,51(1): 71-79.
[21]汪祥莉,王斌. 混沌干扰中基于同步挤压小波变换的谐波信号提取方法[J],物理学报.2015,64(10):1-10.
Wang Xiang-Li, Wang Bin. Harmonic signal extraction from chaotic interference based on synchrosqueezed wavelet transform[J], Acta Phys. Sin., 2015,64(10):1-10.
