Maximum kurtosis principle for the parameter selection of Gabor wavelet and its application to ultrasonic signal processing

The noise suppression techniques with wavelet transform (WT) are widely used in nondestructive testing and evaluation (NDT&E), especially in ultrasonics. But the wavelet based filter has the property of equal Q-factor, so, it is impossible to choose the central frequency and the bandwidth arbitrarily at the same time. This paper develops a new technique using WT to eliminate this drawback. In this paper, a weak ultrasonic signals identification method by using the optimal parameter Gabor wavelet transform is proposed. We address the choice of the optimal central frequency and bandwidth of the Gabor wavelet using the kurtosis maximization algorithm. The central frequency and bandwidth of the optimal parameter Gabor wavelet matched that of the ultrasonic signal very well. Numerical and experimental results have been presented to evaluate the effectiveness of the optimal parameter Gabor wavelet transform on ultrasonic flaw detection. This technique is a simpler and effective technique for processing heavy noised ultrasonic signals.     

基于ICA和小波变换的滚动轴承故障诊断方法研究

提出了结合独立分量分析(ICA)和小波变换进行滚动轴承故障诊断的方法。在设计的系统平台上,首先对冲击脉冲信号进行预处理,使信号较好地满足独立分量分析的前提条件。然后,应用独立分量快速算法分离故障轴承的冲击脉冲信号,通过小波快速算法完成信号重构,实现滚动轴承故障的识别。实验结果表明,利用独立分量分析方法提取的故障状态特征向量与小波快速算法相结合可以有效、准确地识别滚动轴承的故障信号。     

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。     

Vibration analysis of rotating machinery using time-frequency analysis and wavelet techniques

Time-frequency analysis, including the wavelet transform, is one of the new and powerful tools in the important field of structural health monitoring, using vibration analysis. Commonly-used signal analysis techniques, based on spectral approaches such as the fast Fourier transform, are powerful in diagnosing a variety of vibration-related problems in rotating machinery. Although these techniques provide powerful diagnostic tools in stationary conditions, they fail to do so in several practical cases involving non-stationary data, which could result either from fast operational conditions, such as the fast start-up of an electrical motor, or from the presence of a fault causing a discontinuity in the vibration signal being monitored. Although the short-time Fourier transform compensates well for the loss of time information incurred by the fast Fourier transform, it fails to successfully resolve fast-changing signals (such as transient signals) resulting from non-stationary environments. To mitigate this situation, wavelet transform tools are considered in this paper as they are superior to both the fast and short-time Fourier transforms in effectively analyzing non-stationary signals. These wavelet tools are applied here, with a suitable choice of a mother wavelet function, to a vibration monitoring system to accurately detect and localize faults occurring in this system. Two cases producing non-stationary signals are considered: stator-to-blade rubbing, and fast start-up and coast-down of a rotor. Two powerful wavelet techniques, namely the continuous wavelet and wavelet packet transforms, are used for the analysis of the monitored vibration signals. In addition, a novel algorithm is proposed and implemented here, which combines these two techniques and the idea of windowing a signal into a number of shaft revolutions to localize faults.     

Higher-density dyadic wavelet transform and its application

This paper proposes a higher-density dyadic wavelet transform with two generators, whose corresponding wavelet filters are band-pass and high-pass. The wavelet coefficients at each scale in this case have the same length as the signal. This leads to a new redundant dyadic wavelet transform, which is strictly shift invariant and further increases the sampling in the time dimension. We describe the definition of higher-density dyadic wavelet transform, and discuss the condition of perfect reconstruction of the signal from its wavelet coefficients. The fast implementation algorithm for the proposed transform is given as well. Compared with the higher-density discrete wavelet transform, the proposed transform is shift invariant. Applications into signal denoising indicate that the proposed wavelet transform has better denoising performance than other commonly used wavelet transforms. In the end, various typical wavelet transforms are applied to analyze the vibration signals of two faulty roller bearings, the results show that the proposed wavelet transform can more effectively extract the fault characteristics of the roller bearings than the other wavelet transforms.     

A fast algorithm for computing and correcting the CTF for tilted, thick specimens in TEM

Today, the resolution in phase-contrast cryo-electron tomography is for a significant part limited by the contrast transfer function (CTF) of the microscope. The CTF is a function of defocus and thus varies spatially as a result of the tilting of the specimen and the finite specimen thickness. Models that include spatial dependencies have not been adopted in daily practice because of their high computational complexity. Here we present an algorithm which reduces the processing time for computing the ‘tilted’ CTF by more than a factor 100. Our implementation of the full 3D CTF has a processing time on the order of a Radon transform of a full tilt-series. We derive and validate an expression for the damping envelope function describing the loss of resolution due to specimen thickness. Using simulations we quantify the effects of specimen thickness on the accuracy of various forward models. We study the influence of spatially varying CTF correction and subsequent tomographic reconstruction by simulation and present a new approach for space-variant phase-flipping. We show that our CTF correction strategies are successful in increasing the resolution after tomographic reconstruction.     

反射型衍射CT中的迭代法图像重构

基于衍射的计算机层析成像术是建立在Fourier衍射投影定理基础上的.衍射CT图象重构可看作由非均匀频率样点重建信号的问题.提出一种用于反射型衍射CT的图像重构算法,此方法利用反向散射数据进行2D非均匀Fourier反变换.由于直接的非均匀Fourier反变换不易实现,所以采用基于min-max优化准则的非均匀快速Fourier正变换,通过迭代实现非均匀Fourier逆变换的快速有效计算.为了减少迭代次数加快收敛速度,首先用频域插值法得到重构图像的初值,然后根据min-max准则,每经过一次迭代得到重构图像的一个更新版本,重复多次迭代直至得到可接受的重构结果.给出了数值实验结果.与传统重构算法如Gridding方法相比,该算法计算复杂度相当而重构精度较高.     

纺织丝线张力信号实时消噪技术的研究

提出了基于提升框架的小波变换算法在丝线张力信号实时消噪过程中的应用,给出了该算法的流程图,通过对仿真信号与实测信号的消噪试验,验证了该算法的有效性,该算法可以应用于对实际信号的消噪处理,能够满足对张力信号处理的实时性要求,且易于实现。     

基于小波分析的悬臂梁裂纹参数识别方法研究

通过对含裂纹悬臂梁的应变能信号进行小波分析,悬臂梁的裂纹位置可由小波系数的局部极大值给出,并通过小波系数局部极大值定义集中因子和裂纹深度之间的关系,以此估计裂纹深度.数值算例表明, 利用sym4小波对含裂纹梁的应变能信号进行小波分析,可以准确识别出裂纹的位置和深度,这一方法很容易推广应用到结构的在线监测中.     

2D input signal reconstruction from results of measuring by digital optical systems

The algorithm for reconstructing a 2D input signal (the image measured) by a finite realization of a 2D signal at the digital optical recording system output with the use of the fast Fourier transform is analyzed. The image reconstruction error is estimated.     

基于Gabor变换的欠定盲信号分离新方法

结合Gabor变换和盲信号分离的各自优点,提出了一种基于Gabor变换的欠定盲信号分离新方法.首先通过混合信号的Gabor变换系数之间的相互关系,得到了源信号个数的估计;然后对Gabor变换后的信号进行阈值处理,并进行Gabor逆变换得到新的混合信号,从而实现混合信号的升维.再利用现有的盲信号分离方法进行处理,该方法不受源信号个数的限制,因此属于一种欠定盲信号分离方法;最后,通过一组仿真信号的欠定盲分离验证了该方法的有效性.     

用小波变换提高散斑能见度光谱测量法的灵敏度

在分析被动层颗粒温度含噪特点的基础上模拟了低信噪比的方波信号,根据变化规律,采用Mallat快速算法分析低信噪比的方波信号,并根据噪声分布特性设计了用于抑制被动层颗粒温度中干扰噪声的算法。对所设计算法进行仿真实验,结果表明,该算法可以最大限度地滤除信号中的噪声。通过搭建滚筒实验装置,测量滚筒被动层的颗粒温度,对测量数据进行分析,有效地测出了内部颗粒温度状态变化,表明了小波变换能有效提高测量被动层颗粒温度的信噪比。     

基于小波包的滚动轴承故障特征提取

在深入分析离散小波包变换快速算法的基础上，给出了离散小波包变换快速算法中产生频率混淆的原因，即由正交镜像滤波器的非理想截止特必, 隔点采样和隔点插零的特性共同作用产生的，提出了一种消除频率混淆的算法，利用该算法和原算法，分别对某型滚动轴承内环剥落故障的振动信号进行处理，提取其故障特征，结果表明，原算法由于存在频率混淆，可能掩盖故障特征，提出的新算法，由于很好地消除了频率混淆，能有效地提取滚动轴承局部故障的特征。     

稀疏度拟合的自适应机械振动信号压缩感知

针对利用机械振动信号进行设备故障诊断和状态监测过程中,存在采样数据量多、存储容量大、传输带宽高和信号重构精度低等问题,提出一种稀疏度拟合的自适应机械振动信号压缩感知方法。首先,对机械振动信号进行多尺度小波包变换,再将小波包系数按一定阈值进行置零处理并求取其稀疏度;然后,采用迭代方法求取各稀疏度下满足重构信号精度条件的最低采样率,并对信号的稀疏度和采样率采用最小二乘法进行拟合,消除信号测量误差,求取最佳信号采样率;最后,采用K-奇异值分解算法构造与各信号块相适应的过完备字典,并利用正交匹配追踪算法实现信号重构。实验证明,与传统压缩算法相比较,该算法的信号压缩率和重构精度均得到较大提高。     

图像增强技术在超声成像检测中的应用

研究图像增强技术在粗晶材料超声成像检测中的应用。为了克服粗晶材料检测时噪声对成像质量的影响 ,利用中值滤波的快速算法和小波变换的分解重构算法来降低噪声的干扰 ,提高超声图像增强技术的实用性。实验结果表明 :在粗晶材料超声成像检测时采用这两种算法能够得到高质量的超声检测图像     

Mallat算法的光学实现方法

现有的光学小波变换方法均基于连续小波变换,基于离散信号的小波变换算法（Mallat算法）的光学小波变换还没有出现,这阻碍了光学小波变换应用的发展。针对这一问题,分析利用光学4f系统实现Mallat算法的基本原理,提出Mallat算法的光学实现方法。针对空间光调制器只能实现非负的实函数,且CCD只能记录光的强度,给出一种应用于光学4f系统的光学小波滤波器的设计方法。使用该种光学小波滤波器,利用光学4f系统实现Mallat算法的小波分解部分,并通过数值计算实现Mallat算法的小波重构部分。仿真分析和光学实验结果验证了方法的正确性。     

基于提升模式的非抽样小波变换及其在故障诊断中的应用

由于传统离散小波变换在分解信号时采用抽样操作,使原始信号的部分时域特征不能保留在分解结果中;另外,分解结果的平移可变,使得分解结果不能完美地描述故障的时域特征。为了克服上述缺陷,根据非抽样小波变换的原理,提出一种基于提升模式的非抽样小波变换框架。首先,通过信号变换方法去除提升小波变换的剖分环节,得到提升模式下的非抽样小波变换框架;在此基础上,建立提升模式下非抽样小波变换与抽样小波变换的预测器和更新器之间的转换关系,提出非抽样提升小波变换的分解和重构算法。采用这种非抽样小波变换从齿轮箱的振动信号中有效提取幅值调制和瞬态冲击的摩擦故障特征。     

Numerical fringe pattern demodulation strategies in interferometry

The performance of two different numerical frequency demodulation strategies for evaluating sampled fringe patterns in interferometric applications and optics is discussed. Namely, these techniques involve traditional Fourier filtering techniques and a strategy based on the Gabor wavelets. While the latter is found to be more precise, it is generally difficult to implement wavelet-based frequency demodulation with equal performance as methods based on the fast Fourier transform. Here, we demonstrate a specialized fast wavelet algorithm that outperforms Fourier-based strategies for array sizes up to a few thousand data points and is yet more precise. The performance is investigated in numerical examples, indicating that the required choice of a global filter bandwidth is one of the main problems of the Fourier filtering strategy. Wavelet frequency demodulation, in contrast, always appears to perform slightly better, does not require judicious choice of filtering, and can often be made equally fast without loss of precision. Finally, applying this new algorithm to an ideal sinusoidal signal without noise, the precision of the numerical frequency demodulation is increased by nearly two orders of magnitude.     

基于小波变换的涡街流量计信号处理方法

涡街流量计有许多优点,应用比较广泛。但是,涡街流量计易于受到由管道振动和流场扰动引起的噪声干扰。涡待流量计中的处理电路不能保证仪表在工业现场的测量精度。本文研究基于小波变换的涡街流量计信号处理方法。本文介绍小波变换的基本原理和快速算法,分析小波滤波器的幅频特性,研究调整滤波器中心频率的方法,给出涡街信号的处理过程,进行仿真和实验测试。仿真和实验结果表明,小波变换能有效地减小了噪声影响,使频率测量的精度高,处理实时。小波变换是涡街流量计信号处理的一种新方法。