基于平稳小波变换的相空间重构方法

针对非线性信号特征提取问题,提出一种基于平稳小波变换的相空间重构方法.对信号进行多层平稳小波分解,利用得到的不同尺度小波系数进行相空间重构,通过局部切空间变换方法提取蕴涵在相空间高维数据集中信号的低维形态特征.仿真试验结果表明,非线性信号经过平稳小波变换后,吸引子轨迹与原有轨迹具有相似的结构.利用局部切空间变换可获得带有高斯白噪声的非线性信号的低维形态特征,该低维形态特征与原有非线性信号的吸引子轨迹相似.这种相空间重构方法较传统的方法具有一定的优势,可以用于提取含有噪声的机械振动信号的故障特征.     

基于FastICA的超声A波信号降噪研究

为便于缺陷可视化和提高缺陷定量分析精度,必须降低超声检测过程中多种未知噪声源对超声A波信号的干扰。首先将超声A波信号进行相空间重构,得到一个相空间重构矩阵;然后进行基于FastICA算法的独立分量分析;最后从独立分量中提取出所需的超声信号。为了验证降噪效果,将该降噪方法与小波去噪方法进行对比。实验结果表明:该方法与小波去噪方法效果接近,极大地提高了超声信号的信噪比。同时,该方法与小波去噪方法相比具有自适应能力强、简单且易于实现等优点。     

基于小波包分解与主流形识别的非线性降噪

为解决工程实际中强噪声、非线性且频率成分复杂的振动信号降噪问题,提出了基于小波包分解和主流形识别的非线性降噪方法。采用小波包分解将原始振动信号正交无遗漏地分解到各频带范围内,根据各子频带中信噪空间分布,分别采用相应参数对小波包分解系数进行相空间重构;采用局部切空间排列(local tangent space alignment,LTSA)主流形识别方法在高维相空间中实现信号与噪音的分离,并重构出降噪后的一维小波包分解系数,最后进行小波包分解重构得到降噪后的振动信号。通过仿真实验和实例应用对本文所提方法的有效性进行了验证,试验结果表明本文方法具有良好的非线性降噪能力。     

基于小波包的全信息解调方法及其应用

针对旋转机械故障信号的振动特点,将小波包络解调与基于数据融合技术的全矢谱相结合,提出一种诊断旋转机械调制信号的分析方法。首先,对安装在转子同一截面不同方向上的传感器信息同步整周期采样,对来自不同方向的时域信号分别采用小波包进行分解并重构,以实现带通滤波的效果;然后,采用全矢谱技术对两组重构信号进行数据融合;最后,对合成后的信号做包络解调分析。通过仿真研究和工程实例分析可以得出,对来自同一截面、不同方向的时域信号分别作小波包络谱分析时,两者在能量分布和频谱结构上存在着较大差别,以致造成提取故障信息的不完整或造成误判、漏判。基于小波包的全信息解调分析方法通过对同源的双通道信号的有效融合,可全面地反映出信号中包含的不同调制信息。与基于全矢谱的传统包络解调分析进行对比分析,具有较好的分析结果和可信度。     

基于稀疏小波变换的超宽带低信噪比信号检测算法

脉冲超宽带信号是时域瞬态脉冲,功率谱密度极低,在远距离通信时,信号淹没在噪声中较难检测,对前端采样率要求较高.针对脉冲超宽带低信噪比检测问题,提出了一种在脉冲波形先验信息已知条件下,基于稀疏小波变换的低信噪比检测方法.针对超宽带信号在小波域具有稀疏分布这一特征,依据压缩传感理论,分析并仿真了稀疏基矩阵选择时域采样矩阵和小波矩阵时,信噪比对于性能的影响,提供了重构算法中迭代终止门限的选择方法.仿真实验表明,相对于稀疏基矩阵为时域采样矩阵,采用小波变换矩阵可以在较低信噪比条件下实现超宽带信号的降噪重构.     

高速铣削力测量与研究分析

在高速铣削条件下，利用PCI-1712高速数据采集卡进行数据采集，应用MATLAB软件中信号处理工具箱（SPTOOL）和小波分析工具箱（Wavelet）进行数据处理，从时域和频域两个方面研究原始切削力信号和其不同频率段的重构切削力信号，说明切削力信号的小波系数分析和功率谱分析对于研究切削力有重要意义。     

压气机信号分析的小波脊线方法和软件实现

根据Carmona的多脊线识别理论,连续小波变换时频平面上模极大值线所在位置就是脊线位置,其小波系数与时域信号之间存在简单的对应关系,利用Crazy Climber算法提取小波变换系数的脊,就可以重构时域信号.将该方法用于航空发动机压气机的失速信号分析,并做成了软件,有较高的应用价值.     

液压泵故障的小波变换诊断方法

分析了小波变换的时 -频局部化特性及基于多分辨分析的信号小波分解重构算法 ,研究了信号局部奇异性在小波变换下的特性。根据故障信号和噪声的局部奇异性在小波变换下的模极大值在不同尺度上的传播特性不同的特点 ,并利用小波分解重构算法 ,对泵壳振动加速度信号进行了分解、去噪和重构。大大改善了监测信号的信噪比 ,对故障特征信号进行了时域定位 ,提取了故障特征频率。     

基于相空间重构的独立分量分析及其工程应用

针对单通道信号无法满足独立分量分析的欠定问题,将相空间重构与独立分量相结合,通过相空间重构实现单通道信号的升维,并将升维信号进行独立分量分析,可以有效地分离出原信号中包含的独立信源。以提取冲击特征为例,通过仿真验证了该方法的有效性。将该方法运用于滚动轴承故障特征提取中,成功地从早期滚动轴承故障信号中提取了周期性故障冲击成分。与传统的小波包-峭度故障特征提取方法对比,此方法更有效。     

采样信号时频特性的复合材料损伤检测

一种时频数据处理方法被应用于复合材料损伤检测。根据小波变换的框架重构理论和小波变换时频相空间理论,提取了信号的时频域特征,通过比较原信号的时频空间和小波变换相空间的相同部分,得到了能反映原信号同样时频特征的小波级数展开项和,应用Ｇｒａｍ－ｄｓｃｈｍｉｄｔ正交化方法,按照一定准则,对所得到的小波级数展开项的线性组合进行正交处理,用代数数值方法,从已知采样数据分布集合得到了对应于曲线本身时频特征,经过     

Bearing fault diagnosis based on amplitude and phase map of Hermitian wavelet transform

The rolling element bearing characteristic frequencies contain very little energy and are usually overwhelmed by noise and higher level of structural vibrations. The continuous wavelet transform enables one to look at the evolution in the time scale joint representation plane. This makes it very suitable for the detection of singularity generated by localized defects in a mechanical system. However, most applications of the continuous wavelet transform have widely focused on the use of the Morlet wavelet transform. The complex Hermitian wavelet is constructed based on the first and the second derivatives of the Gaussian function to detect signal singularities. The Fourier spectrum of Hermitian wavelet is real, which the Fourier spectrum has no complex phase and the Hermitian wavelet does not affect the phase of a signal in complex domain. This gives the desirable ability to detect the singularity characteristic of a signal precisely. In this study, the Hermitian wavelet amplitude and phase map are used in conjunction to detect and diagnose the bearing fault. The Hermitian wavelet amplitude and phase map are found to show distinctive signatures in the presence of bearing inner race or outer race damage. The simulative and experimental results show that the Hermitian wavelet amplitude and phase map can extract the transients from strong noise signals and can effectively diagnose bearing faults.     

Application of Hermitian wavelet to crack fault detection in gearbox

The continuous wavelet transform enables one to look at the evolution in the time scale joint representation plane. This advantage makes it very suitable for the detection of singularity generated by localized defects in the mechanical system. However, most of the applications of the continuous wavelet transform have widely focused on the use of Morlet wavelet transform. The complex Hermitian wavelet is constructed based on the first and the second derivatives of the Gaussian function to detect signal singularities. The Fourier spectrum of Hermitian wavelet is real; therefore, Hermitian wavelet does not affect the phase of a signal in the complex domain. This gives a desirable ability to extract the singularity characteristic of a signal precisely. In this study, Hermitian wavelet is used to diagnose the gear localized crack fault. The simulative and experimental results show that Hermitian wavelet can extract the transients from strong noise signals and can effectively diagnose the localized gear fault.     

Selective noise filtration of image signals using wavelet transform

This paper examines the technique of denoising and signal extraction using wavelet transform scale space decomposition. The noisy signal is decomposed into multiple scales by the dyadic wavelet transform. At a given position, the level of correlation is used to deduce the presence or absence of significant feature of signals or images, which is then passed through the filter. By comparing the correlation information of the data at that scale with those at larger scales, noise is preferentially removed from the data. In the wavelet transform domain, the desired features are identified and retained because they are strongly correlated across scales compared to noise. This denoising algorithm can be used to reduce noise to a high degree of accuracy, while preserving most of the important features of the signal. In this paper, this technique of scale space filtering is applied to sample signals and images. Representative results are presented which shows that considerable noise content in signals and images can be reduced while preserving the value of the desired signal.     

超声无损检测中的缺陷识别与噪声抑制

在传统的小波信号处理器基础上,根据解析小波变换能准确提取信号相位的特性,利用超声检测信号的相位信息,提出一种新的多缺陷识别与噪声抑制算法。该算法充分运用超声信号的时域、频率和相位信息,能检测多个具有不同频谱特性的缺陷。实验结果表明该算法不仅消噪性能好,而且提高了缺陷的纵向分辨率。     

基于复数小波变换的图像滤噪

小波变换是一种在科学研究上有着广泛应用的工具。由于复数小波变换在某些方面比实数小波变换具有更多的优点,如：平移不变性、更好的方向性和精确的相空间信息等,可提高图像的去噪能力。本文采用二树复数小波变换,在基于H—Curve准则确定阈值的基础上进行图像去噪。此准则不需要提前知道噪声标准偏差,在实际应用中适用于不同类型的噪声,并且和目前多数方法去噪后的图像过于平滑相比,它还能产生较好的视觉效果。典型去噪试验表明：本文采用的方法在去噪能力、取得的视觉效果和确定阈值的广泛性方面都优于目前多数方法．     

基于小波及非线性预测的轴承故障诊断方法

在非线性时间序列预测研究的基础上,提出非线性预测效果的特征提取方法.首先对采集到的足够长轴承数据采用小波变换进行消噪处理及边界延拓,使其满足预测需要的无限长、无噪声的条件,这样延迟时间取任意值均能重构原系统相空间;然后采用基于可预测性的选取嵌入维数的方法确定轴承各种状态信号的嵌入维数,进行相空间重构.应用实验结果表明:该方法提取的特征值能明显地区分轴承各种状态信号,且对数据分段长度的稳定性好,可以作为识别轴承故障的一种新途径.     

基于信号的正交小波构造及其在电机故障特征提取中的应用分析

本文讨论了小波变换的基本理论及小波函数与信号的相关系数对信号去噪效果的影响。在此基础上提出了基于信号的正交小波构造方法:根据信号的频域特征,对各频带系数进行加权处理,确定多尺度分析的生成元,从而构造L2空间的正交小波函数。应用不同小波函数提取750W整数槽化纤电机的振动信号的故障特征,实验结果表明采用此方法构造的小波函数能更加有效地从强噪声中提取故障特征,从而实现对电机故障的精确诊断。     

Similarity of signal processing effect between Hankel matrix-based SVD and wavelet transform and its mechanism analysis

It is pointed out that signal processing effect of singular value decomposition (SVD) is very similar to that of wavelet transform when Hankel matrix is used. It is proved that a signal can be decomposed into the linear sum of a series of component signals by Hankel matrix-based SVD, and essentially what the component signals reflect are projections of original signal on the orthonormal bases of m-dimensional and n-dimensional vector spaces. The similarity mechanism of signal processing between SVD and wavelet transform is analyzed from the angle of basis of vector space and characteristic of Hankel matrix. The orthogonality of the component signals got by SVD and wavelet transform is also studied. It is discovered that singularity of signal can also be detected by Hankel matrix-based SVD, and compared with wavelet transform, there are two characteristics in SVD for singularity detection, one is that the order of vanishing moment of SVD component signals is increased progressively and the one of the nth SVD component signal is ‘n?1’, so singular points with different Lip index can all be detected, the other is that the width of impulse indicating the position of singularity will always keep the same throughout all SVD components and this width is determined by the column number of Hankel matrix.     

小波变换在摆式列车倾摆控制系统故障诊断中的应用研究

小波变换在故障诊断中得到较广泛的应用，但采用不同的小波，分析结果往往会有很大差异。对常用的正交、半正交、双正交小波提取信号特征的能力进行分析比较，表明半正交B样条小波因具有线性相位和采用较长的分解系列，而具有较好的局部化特性和较小的变换误差，是摆式列车倾摆控制系统故障诊断中的较佳小波基。提出一种新的确定故障诊断阈值的方法，并通过实验证明了方法的有效性，为小波变换在摆式列车实车倾摆控制系统故障诊断中的应用提供理论依据。