一种新的声发射信号消噪及故障诊断方法

张 瑞1,邓艾东1,司晓东2,刘东瀛1,李 晶3

振动与冲击 ›› 2018, Vol. 37 ›› Issue (4) : 75-81.

PDF(1883 KB)
PDF(1883 KB)
振动与冲击 ›› 2018, Vol. 37 ›› Issue (4) : 75-81.
论文

一种新的声发射信号消噪及故障诊断方法

  • 张 瑞1,邓艾东1,司晓东2,刘东瀛1,李 晶3
作者信息 +

 A new method for acoustic emission signal de-noised and fault diagnosis 

  • Zhang Rui1, Deng Aidong1,Si Xiaodong2, Liu Dongying1, Li Jing,3
Author information +
文章历史 +

摘要

在旋转机械故障诊断中,声发射信号极易受到噪声的干扰。针对经验模态分解(EMD)易产生模态混叠现象,本文提出了一种基于经验小波变换(EWT)的消噪和旋转机械声发射碰摩故障诊断的方法。该方法利用了EMD和小波变换的优点,通过对傅里叶频谱进行自适应划分,并构建小波滤波器组来提取声发射信号所包含的不同固有模态分量,可有效消除模态混叠现象,同时对分量进行Hilbert变换从而实现声发射信号的消噪和故障诊断。文中采用该方法对仿真信号进行加噪声和消噪处理,在同信号源下,对比基于dB4全阈值消噪、dB4默认软阈值消噪、dB4对高频系数处理消噪和EMD消噪效果。并将该方法应用到实际的声发射碰摩信号中。仿真和实验分析结果表明: EWT方法可以有效地分解出信号的固有模态,分解出的模态少,并且不存在难以解释的虚假模态,消噪效果优于其他方法,并且在声发射故障诊断中也有较大的优势。

Abstract

Acoustic emission signals are highly susceptible to noise interference in rotating machinery fault diagnosis. The empirical mode decomposition (EMD) associates with mode mixing, this paper achieved a method that de-noising and the fault diagnosis of the rotating machinery AE signal based on empirical wavelet transform. This method takes the advantages of the EMD and wavelet transform, classifying the Fourier spectrum by its adaptive property, constructing the wavelet filter bank to extract the different intrinsic mode components of acoustic emission signal, which can eliminate the mode mixing phenomenon. Then the Hilbert transform was carried on the component of the acoustic emission signal so as to realize the de-noising and fault diagnosis. Adopting this method to de-noising the simulations signal that has been added noise, at the same condition, compared with the result of global threshold value de-noising, default threshold value de-noising, tackle high frequency coefficient de-noising based on dB4 and EMD de-noising. Applying this method in the practical AE rubbing signal. Results showed that: Intrinsic modes of the signal can be decomposed effectively through EWT method, the decomposed mode is less and there is no mode that is difficult to explain. Furthermore, de-noising effect is superior to other methods and has great advantage in AE signal fault diagnosis.
 
 

关键词

经验小波变换 / 经验模态分解 / 声发射 / 消噪

Key words

empirical wavelet transform / mode decomposition / acoustic emission signal / de-noising

引用本文

导出引用
张 瑞1,邓艾东1,司晓东2,刘东瀛1,李 晶3. 一种新的声发射信号消噪及故障诊断方法[J]. 振动与冲击, 2018, 37(4): 75-81
Zhang Rui1, Deng Aidong1,Si Xiaodong2, Liu Dongying1, Li Jing,3.  A new method for acoustic emission signal de-noised and fault diagnosis [J]. Journal of Vibration and Shock, 2018, 37(4): 75-81

参考文献

[1] WATSON S J, XIANG B J, YANG W X, et al. Condition monitoring of the power output of wind turbine generators using wavelets [J]. IEEE Transactions on Energy Conversion, 2010, 25(3): 715-721.
[2] 孟宗, 李姗姗. 基于小波改进阈值去噪和HHT的滚动轴承故障诊断[J]. 振动与冲击, 2013, 32(14): 204-214.
MENG Zong, LI Shanshan. Rolling bearing fault diagnosis based on improved wavelet threshold de-noising method and HHT [J]. Journal of Vibration and Shock, 2013, 32(14): 204-214.
[3] CHEN Q X, YE M X. Analysis of the fault diagnosis method for wind turbine generator bearing based on improved wavelet Packet-BP neural network [J]. Communications in Computer and Information Science, 2014, 463: 13-20.
[4] 许同乐, 郎学政, 张新义, 等.基于EMD相关方法的电动机信号降噪的研究[J]. 船舶力学, 2014, 18(5): 599-603.
XU Tongle, LANG Xuezheng, ZHANG Xinyi, et al. Study on the electric motor vibration signal de-noising using EMD correlation de-noising algorithm [J]. Journal of Ship Mechanics, 2014, 18(5): 599-603.
[5] 向东阳, 吴正国, 侯新国, 等. 改进的多小波变换系数相关去噪算法 [J]. 高电压技术, 2011, 37(7): 1728-1733.
XIANG Dongyang, WU Zhengguo, HOU Xinguo, et al. Improved de-noising method using the correlation of multiwavelet coeff icient [J]. High Voltage Engineering, 2011, 37(7): 1728-1733.
[6] TANG B P, LIU WENYI, SONG TAO. Wind turbine fault diagnosis based on Morlet wavelet transformation and Wigner-Ville distribution [J]. Renewable Energy, 2010, 35: 2862-2866.
[7] ZHENG H, LI Z, CHEN X, et al. Gear fault diagnosis based on continuous wavelet transform [J]. Mechanical systems and Signal Processing, 2002, 16(2/3): 447-457.
[8] 胡爱军, 唐贵基, 安连锁. 基于数学形态学的旋转机械振动信号降噪方法[J]. 机械工程学报, 2006, 42(4): 127-130.
HU Aijun, TANG Guiji, AN Liansuo. De-noising technique for vibration signals of rotating machinery based on mathematical morphology filter [J]. Chinese Journal of Mechanical Engineering, 2006, 42(4): 127-130.
[9] 李国鸿, 李飞行. STFT在航空发动机振动信号处理中的应用[J]. 测控技术, 2013, 32(4):45-49.
LI Guohong, LI Feixing. Application of STFT in the field of Aero-Engine vibration signal processing [J]. Measurement & Control Technology, 2013, 32(4): 45-49.
[10] 向玲, 唐贵基, 胡爱军. 旋转机械非平稳振动信号的时频      分析比较 [J]. 振动与冲击, 2010, 29(2): 42-45.
XIANG Ling, TANG Guiji, HU Aijun. Vibration signal’s time-frequency analysis and comparison for a rotating machinery [J]. Journal of Vibration and Shock, 2010, 29 (2): 42-45.
[11] HUANG N E. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis [C]. The Royal Society, 1998, 454(A): 903-995.
[12] 郭淑卿. EMD分解区域的数据研究 [J]. 信号处理, 2010,        02: 277-285.
GUO Shuqing. Data discussion of the EMD method in the practice [J]. Signal Processing, 2010, 02: 277-285.
[13] 王海梁, 熊华刚, 吴庆, 刘成. 一种改进的基于EMD分解          的超宽带信号消噪算法 [J]. 电讯技术, 2012, 04: 461-465.
WANG Hailiang, XIONG Huagang, WU Qing, LIU Cheng. A novel denoising algorithm for UWB signals based on empirical mode decomposition [J]. Telecommunication Engineering, 2012, 04: 461-465.
[14] 滕建方, 王俊, 李维. EMD边缘效应问题的一种处理方法[J]. 教练机, 2014(1): 50-53.
TENG Jianfang, WANG Jun, LI Wei. A disposition method for EMD edge effect problem [J]. Trainer, 2014, 04: 461-465.
[15] GILLES J. Empirical wavelet transform [J]. IEEE Transactions on signals processing, 2013, 61(16): 3999-4010.
[16] Huang D, Liu Y. Characteristics of EMD filter bank and identification of abnormal sound of ball bearing [J]. Journal of Vibration Measurement & Diagnosis, 2012, 32(2): 332-336..
[17] 邓艾东, 童航, 张如洋,等. 基于模态分析的转子碰摩声发射特征[J]. 东南大学学报(自然科学版), 2010, 40(6): 1232-1237.
DENG Ai-dong, TONG Hang, ZHANG Ru-yang, et al. Characteristics of rub-impact AE signals in rotating machinery based on modal analysis [J]. Journal of Southeast University (Natural Science Edition), 2010, 40(6): 1232-1237.

PDF(1883 KB)

Accesses

Citation

Detail

段落导航
相关文章

/