基于双树复小波与非线性时间序列的降噪方法

PDF(2329 KB)

振动与冲击 ›› 2015, Vol. 34 ›› Issue (16) : 135-140.

论文

胥永刚，赵国亮，马朝永，张建宇

作者信息 +

A new denoising method based on dual-tree complex wavelet transform and nonlinear time series

XU Yong-gang，ZHAO Guo-liang，MA Chao-yong，ZHANG Jian-yu

Author information +

文章历史 +

摘要

针对双树复小波变换传统软阈值降噪方法对实、虚部树系数分别进行阈值处理时提取的强背景噪声下轴承故障特征信号效果不理想，且实、虚部分离的阈值处理方法会引起局部相位失真问题，利用故障信号小波变换系数具有周期性与双树复小波系数模震荡小等特点，提出双树复小波变换与非线性时间序列方法相结合的强背景噪声下轴承故障特征提取方法。对故障信号进行双树复小波变换，获得各层小波系数并求模，选择系数模周期性较强层系数进行非线性时间序列处理，增强系数中周期性成分，抑制随机噪声；对增强后系数进行软阈值处理消除直流成分对提取结果影响；将处理后系数还原为复数形式进行双树复小波重构，可成功提取弱故障特征信号。仿真、试验信号处理结果表明，该方法能有效提取强背景噪声下的故障特征信号。

Abstract

A new denoising method based on dual-tree complex wavelet transform and nonlinear time series is proposed in this paper, considering the weakness, such as the phase distortion, of the wavelet soft-threshold denoising method, in which the real and image parts of the coefficient are processed individually. The new method process the magnitude of the complex coefficients instead, for the fact that the magnitude does not oscillate positive and negative which is more suitable for threshold denoising and the fact that the coefficients of the fault signal is always periodic. The nonlinear time series method can be used to strengthen the periodicity of the coefficients which is always caused by the fault signal and restrain the noise meanwhile. Firstly, the fault signal is decomposed by dual-tree complex wavelet transform to obtain the coefficients of different layers. Secondly, the nonlinear time series method is used to strengthen the periodicity of the coefficient whose amplitude is more periodic, and then do soft-threshold denoising to remove the DC component. Finally, the fault characteristic signal can be obtained by coefficient reconstruction. The simulation and experimental results show the effectiveness of this method, which provides a new efficient denoising method.

导出引用

胥永刚，赵国亮，马朝永，张建宇. 基于双树复小波与非线性时间序列的降噪方法[J]. 振动与冲击, 2015, 34(16): 135-140

XU Yong-gang，ZHAO Guo-liang，MA Chao-yong，ZHANG Jian-yu. A new denoising method based on dual-tree complex wavelet transform and nonlinear time series[J]. Journal of Vibration and Shock, 2015, 34(16): 135-140

参考文献

[1] 杨国安. 滚动轴承故障诊断实用技术[M]. 北京：中国石化出版社，2012：13-17.
[2] Selesnick I W, Baraniuk R G, Kingsbury N G. The dual-tree complex wavelet transform[J]. IEEE Digital Signal Processing Magazine, 2005, 22(6): 123-151.
[3] Snekhalatha U, Anburajan M. Dual tree wavelet transform based watershed algorithm for image segmentation in hand radiographs of arthritis patients and classification using BPN neural network[C]. Information and Communication Technologies(WICT),World Congress on, IEEE, 2012: 448- 452.
[4] 王娜,郑德忠,刘永红. 双树复小波包变换语音增强新算法[J]. 传感技术学报,2009,22(7):983-987.
WANG Na, ZHENG De-zhong, LIU Yong-hong. New method for speech enhancement based on dual tree complex wavelet packet transform[J]. Journal of Sensors and Actuators, 2009, 22(7): 983-987.
[5] 季晨宇,袁振海. 基于双树复小波的行波选线选相法[J].电力系统保护与控制,2011,39(12)：48-54.
JI Chen-yu, YUAN Zhen-hai. Fault line and phase selection based on traceling wave and dual-tree complex wavelet[J]. Power System Protection and Control, 2011, 39(12): 48-54.
[6] 苏文胜,王奉涛,朱泓，等. 双树复小波域隐Markov树模型降噪及在故障诊断中的应用[J]. 振动与冲击,2011,30(6)：47-52.
SU Wen-sheng, WANG Feng-tao, ZHU Hong, et al. Denoising method based on hidden Markov tree model in dual tree complex wavelet domain and its application in mechanical fault diagnosis[J]. Journal of Vibration and Shock, 2011,30(6): 47-52.
[7] 胥永刚,孟志鹏,陆明,等. 基于双树复小波和奇异差分谱的齿轮故障诊断研究[J]. 振动与冲击,2014,33(1)：11-16.
XU Yong-gang, MENG Zhi-peng, LU Ming, et al. Gear fault diagnosis based on dual-tree complex wavelet transform and singular value difference spectrum[J]. Journal of Vibration and Shock, 2014, 33(1): 11-16.
[8] 陈彬强,张周锁,何正嘉. 双密度双树复小波变换及其在机械故障微弱特征提取中的应用[J]. 机械工程学报,2012, 48(9): 56-63.
CHEN Bin-qiang, ZHANG Zhou-suo, HE Zheng-jia. Enhancement of weak feature feature extraction in machinery fault diagnosis by using double density dual tree complex wavelet transform[J]. Journal of Mechanical Engineering, 2012, 48(9): 56-63.
[9] 邱爱中. 对偶树复小波阈值降噪法及在机械故障诊断中的应用[J]. 机械传动，2011，35(9)：58-61.
Qiu Aizhong. A New Denoising Method of DT-CWT and Its Application in Mechanical Fault Diagnosis[J]. Journal of Mechanical Transmission, 2011, 35(9): 58-61.
[10] 陈志新. 对偶树复小波分析及其在故障诊断中的应用[D]. 北京：北京科技大学，2007.
[11] 任明荣,王晨,方滨,等. 基于非线性时间序列的胎儿心电信号提取算法[J]. 系统仿真学报,2009，21(16)：5006-5008.
REN Ming-rong, WANG Chen, FANG Bin, et al. Fetal ECG extraction algorithm based on nonlinear time series[J]. Journal of System Simulation, 2009, 21(16): 5006-5008.
[12] 阳建宏,徐金梧,杨德斌,等. 邻域自适应选取的局部投影非线性降噪方法[J]. 振动与冲击,2006,25(4)：64-67.
YANG Jian-hong, XU Jin-wu, YANG De-bin, et al. Nonlinear noise reduction method by local projection with adaptive neighborhood selection[J]. Journal of Vibration and Shock, 2006, 25(4): 64-67.
[13] 黄艳林,李友荣,肖涵,等. 基于相空间重构与独立分量分析的局部独立投影降噪算法[J]. 振动与冲击,2011,30(1)：33-36.
HUANG Yan-lin, LI You-rong, XIAO Han, et al. Local independent projection de-noising algorithm based on phase-space reconstruction technique and independent component analysis[J]. Journal of Vibration and Shock, 2011,30(1): 33-36.
[14] 王晨. 基于非线性时间序列的胎儿心电信号分析与提取[D]. 北京:北京工业大学,2009.
[15] Donobo D L. De-noising by soft-thresholding[J]. Transactions on Information Theory, 1995, 41(3): 613-627.