强脉冲干扰非线性混叠转子故障信号分离

由于旋转机械在运行过程中,传感器测得的振动信号是各振源的混叠信号且含有很强的噪声,常规的信号处理方法难以分离混叠信号,对设备的状态监测和故障诊断造成了很大的困难。针对这一问题,介绍了盲源分离基本原理和方法,指出源分离算法在脉冲噪声环境下失效。针对强脉冲噪声环境下的混叠振动信号,首先,通过中值滤波降噪方法对振动信号进行降噪;然后,通过盲源分离算法对降噪后的信号分离;最后,利用该方法对实测混叠转子振动信号成功实现了降噪和故障信号分离。仿真结果验证了提出方法的有效性。     

一种改进的转子振动信号消噪方法研究

为提高转子振动信号消噪方法的性能,通过分析噪声成分和对应消噪方法的特点,提出了一种基于改进中值滤波与小波包消噪技术相结合的信号降噪新方法.该方法首先根据信号采样频率计算中值滤波器的窗口宽度,从而可以有效滤除含噪信号中的脉冲噪声和部分白噪声;然后再用阈值及其处理函数都经过改进的自适应小波包消噪方法去除残留在信号中的白噪声,最终得到信噪比提高的振动信号.通过仿真信号和转子实验振动信号的降噪处理,对新方法的性能进行了验证.降噪结果表明,该方法在有效消除混合复杂噪声对振动信号干扰的同时,保留了故障信号的细节特征,比一般的小波域中值滤波降噪方法更为有效.     

基于最优小波基的轴承故障状态特征提取方法研究

针对轴承故障诊断中最优小波基的选取问题,通过计算SUMVAR值选取最优小波基。用不同小波基对轴承故障仿真信号和故障实验信号进行降噪处理,分析降噪后信号与原信号的能量比值,降噪后信号与原信号标准差,峭度等指标,验证所选小波基的优越性。并对使用最优小波基降噪后信号做希尔伯特包络解调分析,结果表明,该方法能准确提取轴承故障特征频率。     

基于Morlet小波与最大似然估计方法的降噪技术

采用与冲击信号匹配的Morlet小波作为小波基对信号进行小波变换,利用冲击信号的概率密度特征,结合最大似然估计的阈值方法进行降噪,以提取周期性的冲击信号。通过对减速箱故障信号进行降噪,提取出周期性的故障特征信号,表明该方法可以有效地去除强噪声干扰,提取振动冲击信号     

基于过完备字典的基追踪法在轴承故障诊断中的应用

滚动轴承故障具有信号复合和环境噪声大的特点。通过构建一个对复合信号敏感的过完备字典,利用基追踪法对故障信号进行最优化的稀疏表示,获得简洁的故障冲击时频特征结构,并实现阈值降噪。通过对重构信号谱分析后得到明确的故障特征频率及其倍频。仿真与实验结果表明:该方法具有良好的降噪功能,能够准确地提取信号中的冲击特征。     

基于小波包域噪声能量分布的脉冲噪声消除

针对信号被平稳脉冲噪声污染的场合,提出了一种从含噪信号中检测脉冲噪声并降噪的脉冲噪声消除方法.该方法选择脉冲噪声能量相比含噪信号能量最为显著的小波包树节点,并利用该节点小波包系数重构信号进而检测脉冲噪声的时域分布.在降噪中,该方法于小波包域内估计脉冲噪声的能量分布并依据估计结果计算每段脉冲噪声的滤波阀值.将该方法用语音增强实验验证,结果表明所提出的检测算法能获得较好的检测结果,而提出的降噪算法能显著地改善信噪比,获得较好的降噪效果.将文中的检测算法应用于轴承故障检测的信号预处理,进一步验证了方法的有效性.     

基于小波变换的齿轮箱振动信号降噪处理

应用了小波变换理论和小波降噪的原理,对齿轮箱的振动信号进行了小波降噪处理,有效的从含有噪声的齿轮箱振动信号中提取出该信号更加准确和真实的故障特征,从而为提高齿轮箱故障诊断的准确性以及检测齿轮箱的早期微弱故障信号提供了重要的参考价值。通过对仿真信号的降噪处理,然后进行FFT变换,并且和没经过信号降噪处理就进行FFT变换的对比,显示了小波降噪的优越性。最后通过对齿轮箱的实际振动信号的降噪处理,进一步表明了小波降噪在消除噪声干扰方面有效性。     

基于改进LMD与小波包降噪对故障弱信号的提取

针对微弱故障信号易被强噪声淹没的难题,提出了一种基于小波包降噪与改进LMD相结合的提取微弱信号特征向量的方法。首先选择恰当的小波基进行小波包分解,再根据计算出的最优小波包树进行信号重构,实现对原始信号的降噪处理。然后对重构的信号进行LMD分解,再计算PF分量的互相关系数和峭度值,减少虚假分量同时增强故障信号幅值。最后对真实的PF分量进行包络谱分析,提取弱信号的故障特征。实例研究结果表明:该方法能够有效地提取出淹没在强噪声中的故障弱信号的特征向量。     

基于CYCBD和包络谱的滚动轴承微弱故障特征提取

针对在强噪声的干扰下,滚动轴承微弱故障特征难以有效地提取的问题,提出一种基于最大2阶循环平稳盲解卷积(Maximum Second-order Cyclostationarity Blind Deconvolution,CYCBD)和包络谱相结合的微弱故障特征提取方法。首先,由故障特征频率设置合理的循环频率集,使用CY-CBD对含有强噪声的微弱故障冲击信号进行降噪处理,增强信号中的周期性冲击成分;然后,对降噪信号进行Hilbert包络谱分析来识别故障特征频率。通过仿真和实验,结果证明,该方法能有效地提取被强噪声淹没的微弱故障特征。     

基于EEMD与空域相关降噪的滚动轴承故障诊断方法

针对滚动轴承早期故障信号非平稳、非线性,强噪声的特点,提出了一种将集成经验模态分解(EEMD)和空域相关降噪相结合的滚动轴承故障诊断方法。该方法首先采用EEMD对滚动轴承故障信号进行分解,得到若干个IMF分量,其次,采用峭度—度量因子准则筛选出有效的IMF分量进行信号重构,然后,采用空域相关降噪方法对重构信号进行降噪处理,最后,提取降噪后信号的故障特征频率对轴承故障进行诊断。采用建立的方法对某轴承内圈、外圈故障实验数据进行了分析与诊断,结果表明,方法能够有效克服信号分解的模态混叠效应,对故障信号噪声抑制效果明显,并能准确有效地提取出轴承的故障特征频率,实现对滚动轴承故障的精确诊断。     

Fault diagnosis of a gearbox using the sliced Wigner fourth order time frequency method smoothed by a new kernel function

The detection of impulsive signals embedded in the broadband noise is useful for the fault diagnosis of a gearbox. The sliced Wigner fourth-order time frequency method (SWFOTFM) has been used for the detection of impulsive signals embedded in the broadband noise. However, one disadvantage of SWFOTFM is that the non-oscillating cross-terms cannot be smoothed by conventional kernel functions. In this paper, a new kernel function is developed to reduce the non-oscillation cross-terms. The SWFOTFM using the new kernel function is successfully applied to the fault diagnosis of a gearbox.     

基于K-奇异值分解和层次化分块正交匹配算法的滚动轴承故障诊断

利用层次化分块正交匹配算法（HBW-OOMP）的高稀疏性和运算速度快等优点,提出了一种基于K-奇异值分解（K-SVD）字典和HBW-OOMP算法的故障轴承诊断方法。首先利用K-SVD自学习训练方法得到包含冲击成分的冗余字典,克服了固定结构字典适应性不强的缺点。然后采用基于分块思想的HBW-OOMP算法进行原子的选取和稀疏系数的求解,以重构信号包络谱峭度最大为终止条件,自适应确定分解次数。最后应用所提方法对仿真信号和故障轴承实验信号进行故障特征提取,结果表明该方法能够有效提取强背景噪声下故障特征成分,具有一定的应用前景。     

基于自适应多尺度自互补Top-Hat变换的轴承故障增强检测

针对实际工程中滚动轴承冲击性故障特征难以提取的问题,提出一种自适应多尺度自互补Top-Hat(Adaptive multi-scale self-complementary Top-Hat, AMSTH)变换方法用于轴承故障的增强检测。自互补Top-Hat变换在消除信号中背景噪声的同时,能有效增强故障振动信号的冲击特性,而构造的多尺度自互补Top-Hat变换方法,可以较有效地兼顾抗噪性能和信号的细节保持。在分析形态学滤波的基础上,提出采用特征幅值能量比(Feature amplitude energy radio, FAER)的方法自适应确定最优结构元素的尺度,并应用于轴承的故障增强检测。通过对仿真信号和实测轴承滚动体、内圈故障信号进行分析,结果表明该方法可有效增强滚动轴承的故障检测,并且在运算效率和提取效果方面优于基于信噪比标准的多尺度形态学开-闭和闭-开组合变换方法。     

Application of improved morphological filter to the extraction of impulsive attenuation signals

Rotating machinery response is often characterized by the presence of periodic impulses modulated by high-frequency harmonic components. It can be defined with three parameters, which are natural frequency, fault frequency and decay coefficient. In this paper, we propose an improved morphological filter for feature extraction of the above signals in the time domain. Firstly, an average weighted combination of open-closing and close-opening morphological operator, which eliminates statistical deflection of amplitude, is utilized to extract impulsive component from the original signal. Then, according to the geometric characteristic of impulsive attenuation component, the structure element is constructed with an impulsive attenuation function, and a new criterion is put forward to optimize the structure element. The proposed method is evaluated by simulated impulsive attenuation signals with different natural frequencies and vibration signals measured on defective bearings with outer race fault and inner race fault, respectively. Results show that the background noise can be fully restrained and the entire impulsive attenuation signal is well extracted, which demonstrates that the method is an efficient tool to extract impulsive attenuation component from mechanical signals.     

基于盲均衡理论的弱冲击故障的检测研究

弱冲击故障检测是故障诊断中既困难又十分重要的研究课题,是实现故障早期诊断的基本手段。本文在分析了现有的冲击检测技术存在问题的基础上,基于盲均衡理论提出一种独特的直接提取和检测弱冲击故障信号的方法。首先根据动力学理论研究和建立了机械系统早期冲击响应模型,分析了较小冲击下的振动响应特征;然后根据盲均衡理论研究和建立了提取弱冲击信号的盲均衡模型和算法,并进行改进;最后用两个冲击实验和一个实际工程实例进行实际检验。研究结果表明本方法能够十分有效地从有噪声和干扰中检测期望的信号。     

Detection and recovery of fault impulses via improved harmonic product spectrum and its application in defect size estimation of train bearings

The detection and recovery of impulsive signature play a vital role in the diagnosis and prognosis of rolling element bearings. Though different approaches have been proposed to deal with this problem so far, challenges still exist when they are applied to the bearings operating under harsh working conditions. The difficulties mainly come from the multi-resonance and multi-modulation characteristics of bearing vibration signals. To overcome this limitation, a new methodology for the detection and recovery of fault impulses is presented in this paper. First, an improved harmonic product spectrum (IHPS) is proposed to detect and identify the multiple modulation sources buried in a vibration signal. With this method, the fault-related impulsive features could be recognized, while the influence caused by non-fault modulation is eliminated. On this basis, a harmonic significance index is further established to quantify the diagnostic information contained in a narrow band signal. By utilizing this index, the optimal resonance band where the fault impulses are most significant could be accurately determined. Finally, IHPS and sideband product spectrum are integrated to reduce the in-band noise and further recover the fault impulses. The performance of this method is evaluated by both simulated data and real vibration data measured from a train wheel bearing with a naturally developed defect. Compared with Kurtogram and Protrugram, the proposed method can detect the resonance band more precisely even in the presence of heavy noise and other impulsive vibration sources. Moreover, with the impulses recovery scheme, the double impact phenomenon caused by a distributed defect is extracted successfully. Benefiting from this, the defect size of a bearing can be estimated from its vibration signal without dismantling, which makes it a promising tool for the bearing diagnosis and prognosis in industrial applications.     

基于复小波块阈值的降噪方法及其在机械故障诊断中的应用

提出一种基于对偶树复小波块阈值的信号降噪方法,并将其成功应用于机械故障诊断中.机械设备的振动信号都或多或少地含有噪声,导致弱故障信息的提取一直是故障诊断的难点和热点.提出的降噪方法充分利用对偶树复小波变换的平移不变性和块阈值法的更优估计特性,可以获得比常规的小波降噪方法以及基于常规离散正交小波变换的NeighBlock降噪法更高的信噪比,不仅能有效抑制高斯白噪声,还能够去除冲击信号中的脉冲噪声.对实际信号的研究表明:这种降噪方法可以提取齿轮箱早期故障信息和强噪声背景情况下的隐含故障信息,特别对提取弱冲击故障信号非常有效.     

基于最优Morlet小波的滚动轴承故障诊断

将最优Morlet小波和阈值降噪法相结合,进行强噪声背景下滚动轴承故障诊断.依据峭度最大准则确定最优Morlet小波基.利用连续小波变换和软阈值法对振动信号降噪.试验表明,该方法具有良好的去噪性能,并能更好地提取滚动轴承振动信号中的故障特征.     

Impulsive sources localisation in noisy environment using modified beamforming method

When a machine has faults in its rotating part, it normally generates periodic vibration or acoustic signals. These signals are often periodic but impulsive. This paper addresses the way in which we can find where the impulsive sources are. We propose a signal processing method that can identify impulsive sources’ location. The method is robust with respect to noise; spatially distributed noise. Numerical simulation and experiments are performed to verify the method. Results show that the proposed technique is quite powerful for localising the sources in noisy environments. The method also required less microphones than conventional beamforming method.     

基于形态差值滤波和差分熵的滚动轴承故障诊断

针对滚动轴承内外圈的早期故障,提出了一种新的诊断方法,该方法融合了数学形态学对非线性信号的滤波和信息熵理论在信号表征方面的优越性。首先,利用数学形态差值滤波器对实测的轴承内外圈轻重损伤的故障信号进行消噪处理,充分突出了有用的故障特征信息;然后,利用差分熵提取该信号中的突变特征信息,对其进行不确定性和复杂性度量;最后,根据突变点的冲击时间间隔和内外圈故障周期性冲击的时间间隔一致的思想来完成对滚动轴承的故障诊断。通过对仿真信号和滚动轴承实测内外圈两种故障程度的振动信号的诊断分析,证明该方法能够很好地识别轴承内外圈早期故障的类型,且具有很高的准确率。