基于小波改进阈值消噪的缸盖振动信号分析

提出了一种改进的小波阈值新方法对内燃机缸盖振动信号进行消噪,进而实现特征向量的提取。试验表明采用改进小波阈值方法能有效地消除信号中噪声的干扰,提高信号的信噪比。用小波包提取消噪后信号的能量作为特征向量,来表征内燃机故障特征,可为神经网络的自适应故障诊断提供新的故障样本。     

改进的EEMD算法及其在多相流检测中的应用

针对低信噪比下EEMD法在分解过程中产生模态混叠导致滤波效果变差的问题,提出了一种改进的EEMD滤波方法.该方法首先设计最优带通滤波器,滤除信号中的脉冲干扰,再将滤波后的信号进行EEMD分解,然后采用SG滤波器对筛选的IMF再进行滤波,最后对滤波后IMF进行重构.最后将改进的EEMD法和EEMD法分别用于多相流测量信号滤波及含水率测量,结果表明,改进的EEMD法与EEMD法相比,信噪比提高了约2～3 dB,滤波效果更好;含水率平均测量精度提高了约3％,测量误差更小.     

Flexible time domain averaging technique

Time domain averaging(TDA) is essentially a comb filter,it cannot extract the specified harmonics which may be caused by some faults,such as gear eccentric.Meanwhile,TDA always suffers from period cutting error(PCE) to different extent.Several improved TDA methods have been proposed,however they cannot completely eliminate the waveform reconstruction error caused by PCE.In order to overcome the shortcomings of conventional methods,a flexible time domain averaging(FTDA) technique is established,which adapts to the analyzed signal through adjusting each harmonic of the comb filter.In this technique,the explicit form of FTDA is first constructed by frequency domain sampling.Subsequently,chirp Z-transform(CZT) is employed in the algorithm of FTDA,which can improve the calculating efficiency significantly.Since the signal is reconstructed in the continuous time domain,there is no PCE in the FTDA.To validate the effectiveness of FTDA in the signal de-noising,interpolation and harmonic reconstruction,a simulated multi-components periodic signal that corrupted by noise is processed by FTDA.The simulation results show that the FTDA is capable of recovering the periodic components from the background noise effectively.Moreover,it can improve the signal-to-noise ratio by 7.9 dB compared with conventional ones.Experiments are also carried out on gearbox test rigs with chipped tooth and eccentricity gear,respectively.It is shown that the FTDA can identify the direction and severity of the eccentricity gear,and further enhances the amplitudes of impulses by 35%.The proposed technique not only solves the problem of PCE,but also provides a useful tool for the fault symptom extraction of rotating machinery.     

基于正态反高斯模型的自适应小波消噪方法

提出一种基于正态反高斯分布模型局部逼近小波系数的降噪算法。该算法以db5小波作为振动信号的分解小波,对噪声信号进行分解。对于分解过程中包含大量噪声的小波系数,利用具有良好细节逼近性能的正态反高斯分布构造先验模型,在先验模型的基础上,运用贝叶斯最大后验概率估计从含噪的小波系数中估计出真实的小波系数。在后验估计的过程中,对于估计模型中的关键系数采用粒子群算法进行优化选取。利用估计的小波系数来重构信号,得到降噪后的信号。通过仿真实验和实际轴承的故障信号对该方法进行了验证,结果表明,该方法具有较好的降噪效果,可以有效的消除信号的噪声。     

应用小波收缩去除电机控制系统中的噪声

本文应用小波变换可以降低电机控制系统中噪声影响的技术,通过变换分解出高频噪声部分,利用小波收缩的阈值量化方法去掉高频部分,再重构信号从而达到去噪目的。通过实验,得出SYM12结合软阈值处理法具有最优的信号重构及去噪效果。     

基于ICA在强背景噪声振动信号中的去噪研究

由于小波模极大值去噪方法在强背景噪声的情况下提取碰摩信号的能力变弱甚至失效,在本文中提出应用独立分量分析(ICA)方法对碰摩信号进行特征提取。通过对转子模拟实验台模拟的强背景噪声下的碰摩信号进行ICA去噪方法和小波去噪方法仿真实验,结果表明,本方法明显优于小波去噪方法,为强背景噪声下的弱振动信号的检测提供了新的途径。     

De-noising of diesel vibration signal using wavelet packet and singular value decomposition

The vibration signals of diesel include excess noise that must be eliminated before extraction of characteristic parameters. Firstly, the effects of vibration-signal de-noising among Fourier transform, wavelet decomposition and wavelet packet decomposition are compared. Secondly, singular value decomposition is applied to de-noising vibration signals. Finally, a new de-noise method integrated with wavelet packet and singular value is presented. In this method, vibration signals are decomposed by wavelet packet, and the wavelet packet coefficient is de-noised by singular value decomposition again. The results indicate that the new de-noising method is the best. The SNR (signal-to-noise ratio) of the vibration signals of a diesel cylinder lid is the highest. The diesel vibration waveforms of combustion and valve become clear and the extracted characteristic parameters become more precise. __________ Translated from Journal of China University of Petroleum (Natural Science Edition), 2006, 30(1) (in Chinese)     

小波降噪在测井信号处理中的应用

针对测井仪器研制过程中存在的微弱信号检测问题,在分析微弱信号检测原理的基础上,提出了将小波降噪方法引入测井信号处理。应用小波降噪方法的关键是分解层数的确定和阈值门限的选取。采用了一种基于白化检验的小波分解层数确定方法,并对现有的多种阈值选取方法进行了分析与比较。认为小波降噪方法在测井仪器的微弱信号检验中有着非常好的适用性,尤其在信号噪声比非常低时比传统的傅里叶方法有着更好的降噪性能。     

基于Contourlet变换自适应阈值的图像去噪算法

综合利用Contourlet变换和图像在各个尺度各个方向上的轮廓细节的大小,一定程度上改进了Donoho阈值"过扼杀"其分解系数的缺点,同时还考虑图像的轮廓细节.实验结果表明,与小波阈值,Contourlet阈值和多尺度Contourlet阈值相比,这两种方法更好的提取了图像的轮廓细节,提高了图像的PSNR值.     

振动基座下光纤陀螺信号消噪方法对比研究

为了减小动态环境下光纤陀螺信号的随机误差,采取振动试验对光纤陀螺的动态误差进行激发,通过对试验数据的Allan方差分析,得到了振动过程中光纤陀螺信号随机误差的变化特性。采用基于AR(2)模型的Kalman滤波方法和小波滤波方法对光纤陀螺信号进行消噪处理,分析结果显示上述两种方法都能够有效地消除振动基座下FOG信号的随机误差,并且小波滤波方法要优于Kalman滤波方法。