Estimating ultrasonic time-of-flight through echo signal envelope and modified Gauss Newton method

The accurate estimation of ultrasonic Time-Of-Flight (TOF) is essential in ultrasonic non-destructive testing (NDT). In this paper, a novel method for TOF estimation through envelope is proposed. Firstly, the wavelet denoising technique is applied to the noisy echo to improve the estimation accuracy. Then, the Hilbert transform (HT) is used in ultrasonic signal processing in order to extract the envelope of the echo and to reduce the computational burden. Finally, the echo parameters are estimated by using a Modified Gauss Newton (MGN) based nonlinear Least Squares (LS) estimation method. And, a fast algorithm is adopted to estimate the parameters of M-superimposed echoes. Numerical simulation and experimental results have been carried out to show the performances of the proposed method in estimating TOF of ultrasonic signal.     

Matching pursuit for decomposition and approximation of ultrasonic pulse-echo wavelet and its application in ultrasonic nondestructive evaluation

The estimation of the time of arrival (TOA) and/or time of flight of the ultrasonic echoes is essential in ultrasonic nondestructive evaluation. In this paper, matching pursuit method has been used to analyze noisy ultrasonic pulse-echo wavelet and decompose the noisy pulse-echo wavelet into unit-norm vectors. The error ratio of the mean square is used to monitor the acceptability of the unit-norm vector obtained by the matching pursuit performance. A matched filter can be designed by using the time reversal of the approximation pulse-echo wavelet obtained by the reconstruction result with several large coefficient decomposed unit-norm vectors. After performing the matched filter, the part of the signal related to the electrical noise will be removed leaving only the correlated portion of the ultrasonic echoes. Then no initial phase have been contained in the processed ultrasonic echoes. Therefore, the TOA can be estimated directly from the peaks of the representation of the processed signal. Numerical simulations and experimental results make evidence the good performance of the proposed approach which has good effect of noise suppression and results in much improved accuracy in the estimation of echo arrival time.     

Denoising ultrasonic pulse-echo signal using two-dimensional analytic wavelet thresholding

When an ultrasonic angle-beam pulse-echo setup is used, two kinds of noise are present in the received signal: (1) wedge noise, and (2) random noise. In this study, we propose a method for removing both random and wedge noises using a two-dimensional stationary wavelet transform (2D SWT). To improve the performance of the 2D SWT, we employ analytic wavelet thresholding. This requires calculating the analytic signal for a 2D signal and extracting the envelope of the signal, but no regular generalization of analytic signals to multi dimensions is available. We prove that under some certain conditions, the envelope of the ultrasonic B-scan can be extracted exactly by introducing a reference vector. The performance of the proposed denoising method is assessed using simulated and experimental data. The experimental results show that our proposed denoising method is able to reveal the echoes from a surface breaking crack as small as 0.1 mm.     

基于盲反卷积和参数化模型的超声参数估计

在超声检测中,往往需要获得传播时间(TOF)、回波个数、中心频率、幅值等全面的信息,来综合评判缺陷的位置、大小和类型。通过建立多回波的卷积模型和参数化模型,给出一种结合最小熵盲反卷积(MED)和期望值最大(EM)算法思想的超声回波参数估计方法。首先基于卷积模型,采用最小熵反卷积,实现了重叠多回波信号的有效分离;再基于参数化模型和所获取的回波个数,给出了基于期望值最大算法思想的参数估计算法;最终实现了重叠多回波超声信号TOF、回波个数、中心频率、幅值等参数的精确估计。仿真和实验验证了该方法的有效性和优点。     

Ultrasonic flaw detection during NDE of oil pipelines via a resonance filter

Ultrasonic nondestructive evaluation (NDE) is widely used for detecting flaws in oil pipelines. The backscattering noise may attain peak values greater than the sought flaw pulse. For improving the signal-to-noise ratio (SNR) and extracting a flaw echo corrupted by noise, a resonance demodulation technique and a resonance filter are proposed. The impulse response of the resonance filter can be represented as a superposition of Gaussian echoes. The analysis results show that this approach is more effective in extracting the information of a small flaw during pipeline NDE. The amplitude envelope of the processed ultrasonic signal after envelope detection obtains a notable SNR enhancement. The text was submitted by the authors in English.     

Ultrasonic signals processing base on parameters estimation

Many techniques, such as transform-domain techniques, parameter extraction techniques and so on, have been applied to process the ultrasonic 1D signal. Parameter extraction techniques using Matching Pursuit (MP) has been proposed to compress 1D ultrasonic signal. MP algorithm can iteratively decompose the analyzed ultrasonic signal into elementary functions, called dictionary and select the best matched functions to represent original signal. In this paper, Gaussian echo model function is used as MP dictionary to represent ultrasonic signal. Genetic Algorithm (GA) is applied to estimate parameters of the elementary functions in the dictionary which can best represent the ultrasonic signal. Computer simulation and experimental results were performed to evaluate the efficiency of the proposed method. The article is published in the original.     

High-accuracy ultrasonic temperature measurement based on MLS-modulated continuous wave

Ultrasonic temperature measurement has the potential to improve measurement accuracy by increasing the length of a received signal due to its excellent performance with noise resistance. However, when the distance between the transmitter and receiver is limited, the received signal can be polluted by strong multiple echoes, which can significantly degrade temperature accuracy. This paper proposes a method for high-resolution ultrasonic temperature measurement. With the use of a maximum length sequence (MLS)-modulated continuous wave, the obstructive effect of echoes is effectively suppressed. A hybrid method is employed for accurate time-of-flight (TOF) estimation by incorporating both cross-correlation and phase shift (PS), which is the basis of highly accurate temperature measurement. The experimental results in distilled water show that the proposed method estimates TOF with a standard deviation of less than 0.3 ns, and temperature errors consistently remain within ±0.04 °C.     

Matching-pursuit-based adaptive wavelet-packet atomic decomposition applied in ultrasonic inspection

A novel method of time-frequency analysis, wavelet-packet atomic decomposition based on the matching-pursuit (MP) algorithm, is proposed for improvement of ultrasonic flaw detection during ultrasonic NDT. The MP algorithm is used to decompose a nonlinear and nonstationary signal into given atoms in an overcomplete wavelet-packet dictionary. The wavelet-packet dictionary with Daubechies wavelet functions, which well match the observed ultrasonic flaw echoes, is selected. By means of adaptively selecting the optimum atoms to reconstruct a signal, it is possible to obtain spare approximations of the original signal with less complexity and to efficiently improve a signal corrupted by noise. A distinct SNR enhancement for an ultrasonic echo during flaw detection is verified by computer simulation and experimental results. The text was submitted by the authors in English.     

基于自适应脉宽的超声信号FRI采样方法及应用

脉冲超声信号求取包络后可视为具有有限新息率(FRI)的高斯脉冲串信号,对于FRI采样框架下的高斯脉冲串信号,时延和幅值的准确估计是以高斯脉冲宽度准确先验为前提的。针对此问题,研究了FRI采样框架下信号脉宽、时延和幅值的关系,并提出一种基于信号峰值先验信息的FRI采样方法,该方法可自适应调整脉冲宽度参数,无需准确先验脉宽参数,解决了传统FRI采样中脉宽准确先验的难题。通过仿真试验和在管道缺陷检测中的应用表明,即使在无法预知信号脉宽真实值的情况下,仍能准确从稀疏采样数据中估计得到信号的准确参数。     

基于同步挤压小波和脉冲压缩的钢轨踏面裂纹电磁超声表面波检测方法研究

钢轨踏面上的疲劳裂纹严重影响着列车行车安全。 针对如何快速有效地检测出踏面斜裂纹的问题,本文提出了一种快 速检测钢轨踏面裂纹的方法。 首先分别建立了含高斯白噪声、正弦信号加高斯白噪声干扰的数学模型,分析了编码脉冲压缩、 同步挤压小波变换和先同步挤压小波变换后脉冲压缩共 3 种信号处理方法的噪音抑制效果。 其次,为了验证上述方法对噪音 的抑制能力,使用激励频率为 1 MHz 的表面波电磁超声换能器对含裂纹的钢轨踏面进行检测。 最后,以检测得到裂纹的超声 回波为研究对象,比较了希尔伯特黄方法处理单一频率脉冲对应的超声回波信号和先同步挤压后脉冲压缩方法对应的降噪能 力和超声成像效果。 实验结果表明:本文所提方法可以获得钢轨踏面裂纹的位置信息及其数量。 希尔伯特黄变换在处理无同 步平均的原始超声回波时,由于回波信噪比低,经验模态分解(EMD)失效。 在以巴克码为激励信号且无同步平均采集的条件 下,先进行同步挤压小波变换后脉冲压缩处理,得到的超声回波信噪比相较于只采用相位编码脉冲压缩提高了 6. 82 dB,相比于 只做同步挤压小波变换提高了 11. 02 dB,能明显提升检测速度和 B 扫图像分辨率。     

Noise suppression and flaw detection of ultrasonic signals via empirical mode decomposition

In ultrasonic nondestructive testing, the precise detection of flaw echoes buried in backscattering noise caused by highly scattering materials is a problem of great importance. In this paper, a new signal decomposition method for analyzing nonstationary or nonlinear data, empirical mode decomposition, is proposed to deal with ultrasonic signals. A new denoising technique that combines empirical mode decomposition and filtering simultaneously in the time domain and frequency domain is designed to suppress noise and enhance flaw signals. Synthetic and experimental signals are denoised with this EMD-based filtering technique. Simulated results are presented and analyzed, showing that the proposed technique has an excellent performance even when the signal-to-noise ratio is very low (−23 dB). The improvement in flaw detection was experimentally verified on a pipeline sample with artificial flaws. The text was submitted by the authors in English.     

基于经验与变分混合分解的超声回波信号噪声消除方法

超声缺陷检测结果易受超声回波信号中复杂噪声的干扰,为了提高超声缺陷检测的准确度,提出一种基于混合分解的 超声回波信号噪声消除方法。 采用经验模态分解算法结合相关系数指标对超声回波信号进行预处理,得到消除低频噪声分量 的超声回波预处理信号。 基于变分模态分解将该预处理信号分解为一系列窄带本征模态函数,引入互信息指标估计变分模态 分解的最优模态数量,并根据窄带本征模态函数与预处理信号的相关系数提取有用的模态分量,实现对超声回波信号去噪结果 的重构。 通过仿真和实测超声回波信号验证了本文方法的去噪性能,并与现有方法进行了对比。 结果表明,本文方法可同时消 除超声回波信号中的高频和低频噪声,在不同信噪比条件下 EMD、VMD 和本文方法去噪结果的 SNR 均值分别为 10. 01、9. 48 和 16. 09 dB,验证了本文方法对于超声回波信号噪声消除的优越性。     

应用序列二次规划的波达方向与多普勒频率联合估计

为了快速准确地联合估计阵列信号波达方向(DOA)与多普勒频率,提出了将序列二次规划(SQP)应用于最大似然函数优化的联合谱估计算法。该方法利用空时信号模型通过Hankel矩阵构造出阵列流型中包含DOA与多普勒频率信息的广义天线阵模型,并推导出其最大似然函数,从而将参数估计问题转化为非线性函数优化问题。然后,将SQP方法应用于似然函数的优化求解中,得到DOA与多普勒频率的估计值。最后应用SQP方法、微分进化法、遗传算法和量子粒子群算法分别进行了优化的仿真对比实验。结果表明:提出的算法具有寻优时间短,估计精度高,参数自动配对等特点,在信噪比为0dB时估计两个目标信号源的DOA与多普勒频率的均方根误差分别为0.263 6°和0.007 6rad,基本达到了阵列信号处理中参数联合估计方法的设计要求。     

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.     

A new velocity estimation method using spectral identification of noise

In all measurement techniques one seeks accuracy and precision. In ultrasonic Doppler velocimetry, those qualities strongly depend on signal to noise ratio of the Doppler signal and on the performance of the velocity estimator. The most widely used estimation method in ultrasonic coherent Doppler velocimetry is the pulse pair method. Its success is due to the computation efficiency of the algorithm combined to an unbiased estimator. Unfortunately, for a wide range of experimental fluid flows, the pulse pair estimation is less efficient, especially for clear water or concentrated mud where the signal to noise ratio can be very low, or for highly turbulent flows where the Doppler signal has a broad spectrum. Our approach is based on the treatment of the Doppler spectral information. It uses a simple parametric identification inspired by theoretical models and experimental observations. It acts through noise subtraction and subsequent cutting. Thus, we have developed a fast velocity estimation algorithm superior to the pulse pair one in terms of accuracy. The robustness of the method was evaluated by adding different levels of white Gaussian noise to an experimental Doppler signal. The results demonstrate an increase of noise immunity up to one decade compared to the pulse pair method.     

A New ultrasonic signal processing scheme for detecting echoes of different spectral characteristics in concrete using empirical mode decomposition

In this work, a new signal processing scheme is proposed to improve ultrasonic echoes detection in concrete, a construction material and inhomogeneous propagation medium, in which the backscattering and the grain noise mask the useful signals. Firstly, the time-domain ultrasonic signal is measured. Then the original signal is separated into Intrinsic Mode Functions (IMF) using Empirical Mode Decomposition. Secondly, the Fourier analysis is performed on each IMF. Examining the behaviors of the IMFs in the time and frequency domain, one can judge which IMF represents noise, echoes wave. Then we use nonlinear processing techniques to recombine the selected IMFs. Experimental results are presented and analyzed on a trapezoidal concrete/mortar/cement-paste prism specimen, showing that the proposed technique has an excellent performance. The improvement in defect detection was experimentally verified too on a prism based specimen of mortar with artificial defect.     

激光超声信号去噪的经验模态分解实现及改进

考虑激光超声检测过程中噪声对缺陷和材料特征分析和检测的影响,本文以激光超声信号去噪为目的,研究了基于经验模态分解（EMD）的激光超声信号时间尺度滤波过程。针对分解过程中固有模态函数（IMF）上有用信号与噪声的混叠现象对重构信噪比的影响,结合信号多模态和宽频带的特点,提出了基于峰度检验策略的时域加窗方法。该方法通过局部峰度检验判断重构起点附近IMF中有用信号的位置及信噪分界点,利用Turkey-Hanning窗保存有用信号,抑制噪声,实现信号与噪声的解混叠,改善重构信号质量。仿真和实验结果表明,该方法具有良好的自适应性,有效识别并分离了信号和噪声成分,信噪改善比达14 dB以上,相对原始方法提升了3 dB,相对性能增强了20%,并且改进效果随信号受污染程度的加重而愈发突出,有望在高噪声水平下发挥优势。     

湍流信道下光空间调制信号的压缩感知检测

在光空间调制系统中采用最大似然检测算法可以获得最优的误码性能,但算法的译码复杂度限制了其实际应用。本文依据光空间调制激光器映射向量,并结合脉冲位置调制方式下的脉冲向量构建了具有稀疏特性的发送信号。基于该稀疏特性,采用正交匹配追踪算法提出了一种基于压缩感知理论的光空间调制信号检测方法。仿真结果表明:该方法以少量误码性能损失为代价极大地降低了信号检测的复杂度。当激光器数目为64时,相比于最大似然检测算法,本文所提方法的复杂度降低了99.54%。同时,由于稀疏性的引入,该方法更适合于具有大规模激光器的无线光通信系统。     

肝硬化的超声特征分析

利用肝脏硬度的无创估计系统，可以及时计肝脏的弹性状况，对了解和控制肝病进程有积极的意义，本文先在常用超声仪上获得肝脏的M型超声图像，接着从图像中分别提取肝脏包膜的运动包络和A型扫描信号相关系数随时间变化的曲线，然后从运动包络和相关曲线中提取出相应的特征参量，最后用Fisher判别法进行肝脏是否硬化的分类决策，结果表明：该方法可以为简单，有效地诊断肝硬化提供新的手段。     

基于训练局部字典的倒装芯片高频超声检测信号稀疏去噪方法

针对高频超声检测倒装芯片缺陷的精度易受噪声影响以及高频超声信号维度高的问题,提出一种基于K-奇异值分解(K-Singular value decomposition, K-SVD)训练局部字典的高频超声信号稀疏去噪方法。采用K-SVD训练字典来减小信号与字典中原子之间的误差,并针对K-SVD不能训练高维度字典的问题,将高频超声信号分段,在低维度字典上对局部信号进行稀疏分解,从而降低训练字典和稀疏分解的计算复杂度;利用信号的全局最大后验概率(Maximum a posteriori probability, MAP)估计重构信号,消除因局部处理带来的信号跳变,实现高频超声信号的去噪。仿真和试验结果证明,提出的方法能够有效的去除高频超声信号中的噪声,与在全局字典上进行高频超声信号的稀疏分解相比,采用局部训练字典对信号进行稀疏分解在保证去噪性能的同时降低了计算复杂度。