基于提升小波变换和AdaBoost的超声信号消噪技术

为了提高超声无损检测(UNDT)和无损评价(UNDE)中基础数据的信噪比(SNR),提出了一种基于提升小波变换和AdaBoost模式识别理论的超声信号消噪技术.该技术在研究材料内部散射体引起的结构噪声产生机理,以及分析传统裂谱分析算法局限性的基础上,利用提升小波变换将原始超声检测信号分解到小波空间后,通过采用AdaBoost算法构成的信噪分离器对信号和噪声进行识别、分离来消除噪声,得到高信噪比的超声回波信号.实验结果表明,与传统裂谱分析算法相比,该技术提高了消噪性能的稳定性,增强了湮没材料内部各种散射体散射中的缺陷回波信号能力.     

基于经验模态分解的超声波管外测压信号去噪

在超声波管外测压中,回波信号往往受到噪声的干扰,所以在提取信号特征时,需对回波信号进行去噪。提出了一种基于经验模态分解（EMD）的超声波信号去噪方法,首先利用EMD求出信号的本征模态函数（IMF）,然后通过对比分析,利用反映信号主要特征的模态分量对信号进行重构以实现去噪。实验结果表明,该方法能够有效地去除噪声,提高超声回波信号的信噪比。     

基于EMD的微裂缝超声回波信号去噪算法

为了提高激光外差干涉法检测微裂缝的灵敏度和可靠性,研究了利用经验模态分解(EMD)算法对解调出的激光超声回波信号进行去噪处理的算法,并将EMD算法运用到了实验获得的超声回波信号,提取了微裂缝的位置信息.实验结果表明:EMD算法能够有效去除超声回波信号中的噪声信号,得到的被测样件微裂缝的距离与实际距离的误差率为1.02%,并为判断微裂缝的大小提供了有效的帮助.     

基于提升小波变换的超声信号消噪技术

为了提高超声无损检测（UNDT）和无损评价（UNDE）中基础数据的信噪比（SNR）,提出了一种基于提升小波变换多分辨率分析的超声信号消噪新技术.在分析传统裂谱分析（SSP）方法原理及其局限性的基础上,通过采用提升小波变换多分辨率分析能力将原始超声回波信号进行子带分解,然后按照一定的信噪分离规则来消除噪声,达到提高信噪比的目的.实验结果表明,与传统裂谱分析方法相比,该方法增强了消噪性能的稳定性,提高了超声回波信号的信噪比.     

采用交替投影算法重构超声信号

建立了超声探测缺陷回波的数学模型，讨论了信号奇异性同其小波变换之间的关系以及通过小波变换模极大值精确重构原信号的原理和方法，利用Mallat的交替投影算法对仿真的超声信号进行了精确重构和对实际检测到的超声信号进行了消噪处理。结果表明，利用小波变换模极大值重构信号的交替投影算法来重构超声信号，重构精度高，实现速度快，用于处理染噪信号，消噪效果好，是一种较为理想的处理超声信号的方法。     

基于EMD和小波熵阈值算法的超声回波信号降噪

超声检测信号中通常包含大量噪声,而其中材料晶界散射的噪声是一种相关噪声。鉴于传统的方法难以将这种噪声和缺陷回波信号区分,提出一种EMD和小波熵阈值联合降噪的算法。该算法首先对目标信号进行EMD分解,提取具有噪声特性的IMF分量进行小波分解,利用含噪系统熵增的特性,在分解各尺度层的细节部分选用小波熵自适应阈值降噪,然后将剩余分量和降噪处理后的信号进行重构。仿真信号结果表明:该降噪方法(EMD-WET)输出信号的信噪比(SNR)为7.9 d B、均方根误差(RMSE)为18.1、相似系数(NCC)为0.92,优于传统的小波软、硬阈值方法。对实测信号进行处理,该方法降低信号中的大部分噪声,更好地还原回波信号的波形。     

果蝇优化小波阈值超声检测信号去噪

由于材料结构的复杂性,超声检测回波信号往往存在很多干扰噪声。针对钢制结构中平底孔的超声检测信号传统小波去噪方法中小波阈值难确定的问题,结合小波良好时频特性和果蝇的全局优化能力,提出基于果蝇算法(FOA)优化小波阈值函数的超声检测信号去噪方法。对原始信号叠加5d B高斯白噪声,通过测试最大信噪比改善量获得最佳小波基和分解层数,采用sym5小波对超声检测信号进行6层分解后,利用果蝇算法对小波阈值进行参数优化,对比传统4种阈值确定方法,提高小波阈值的精度。验证结果表明:该方法对超声检测信号去噪后信噪比、均方根误差和相关性等参数具有满意的效果,去噪效果明显。     

高精度超声测距系统中自相关小波去噪法

超声测距的精度主要取决于飞行时间的提取精度,由于受噪声影响,接收信号的始点位置通常无法精确提取.传统方法常采用设计滤波器降噪,然而由于滤波器的参数无法随超声信号变化而改变,导致其适应性较差.本文提出了一种自相关小波阈值去噪的自适应去噪方法,通过计算各层小波细节分量与近似分量的相关性系数,自动确定最优分解层数,并通过采集分析噪声信号的小波分量,选择最优去噪阈值,达到了良好的去噪效果,增强了回波信号的信噪比,极大地提高了回波飞行时间的提取精度.经实验验证,信噪比可提高6~7 d B,在1 900 mm的范围内测量误差小于0.3 mm,测量不确定度小于0.17 mm.与传统方法相比,本方法具有适应性强、始点识别率高、测距精确等优点.     

ITD改进信号子空间超声检测信号去噪

金属材料超声检测信号中存在的干扰噪声严重影响实际缺陷的检测精度,因此必须对采集的检测信号进行有效去噪处理。针对传统信号子空间去噪方法的不足,提出固有时间尺度分解(ITD)改进信号子空间信号增强算法的超声检测信号去噪方法,融合ITD方法和信号子空间的优势。通过现场超声检测缺陷信号数据的对比验证表明,信噪比、均方根误差和相关性等参数都比较满意,对含有有色噪声的超声检测信号去噪效果明显。     

奥氏体不锈钢焊缝超声回波信号的匹配追踪处理

研究了奥氏体不锈钢焊缝组织的金相显微结构及其对超声检测的影响,利用超声相控阵检测技术对定制的奥氏体不锈钢对接焊接接头对比试块中不同深度(10、30、50、70 mm)、φ2 mm×30 mm的横通孔缺陷进行了不同波型(横波和纵波)的检测,采用匹配追踪后处理方法对超声回波信号进行了处理。结果显示:奥氏体不锈钢焊缝组织结构复杂,晶粒粗大,各向异性明显,对超声检测产生严重的声能衰减,纵波检测奥氏体不锈钢焊缝中较深缺陷(50 mm)的能力强于横波检测,且匹配追踪对奥氏体不锈钢焊缝超声检测回波信号的处理不仅能有效抑制噪声信号、提高信噪比,还能提取出被淹没在噪声信号中的缺陷信号。     

基于EEMD和低秩稀疏分解的超声缺陷回波检测方法

针对应用超声对金属材料微小缺陷检测时缺陷回波容易被噪声干扰的问题,提出了一种基于集合经验模态分解(EEMD)和低秩稀疏分解相结合的检测方法,以避免传统基于经验模态分解(EMD)的去噪方法难以消除结构噪声的问题.首先对缺陷检测信号进行EEMD得到一系列本征模态函数(IMF),采用基于概率密度函数的相似性测量方法选取相关模...     

基于小波变换的裂谱分析法

 为了提高超声无损检测（UNDT）和无损评价（UNDE）中基础数据的信噪比（SNR）,提出了一种基于小波变换多分辨率分析的裂谱分析新方法．该方法在分析传统裂谱分析（SSP）方法原理及其局限性的基础上,通过采用小波变换多分辨率分析能力将原始超声回波信号进行等Q子带分解,然后按照一定的信噪分离规则来消除噪声,达到提高信噪比的目的．实验结果表明,与传统裂谱分析方法相比,该方法提高了消噪性能的稳定性,增强了湮没晶粒(或其他散射体)散射中的缺陷回波信号能力．     

Echography using correlation techniques: choice of coding signal

Theoretical studies made in the early 1980's suggest that ultrasonic imaging using correlation technique can overcome some of the drawbacks of classical pulse echography. Indeed by transmitting a continuous coded signal and then compressing it into a short, high resolution pulse at the receiver the total signal to noise ratio (SNR) is improved. The target location is determined by cross correlation of the emitted and the received signal. The band compression allows, by increasing SNR, the retrieval of echo signals buried in the receiver noise. Thus in medical-type echography, where the signal attenuation at fixed depth is proportional to the frequency, the SNR improvement allows the use of higher frequency signals and leads to improved resolution. We report here the results of comparative experimental studies of simple echo B type images as obtained by the classical pulse echo and correlation techniques. Because the optimisation of the coded signal plays a crucial role in the performance of the correlation technique we will also present a comparative study of the performances of the most common codes (m-sequences and complementary series). In particular we shall emphasise the following points: the relative importance of the central lobe as compared to the side lobes of the correlation function, which is directly related to the dynamic of the imaging system, the width of the correlation peak which is directly related to the axial resolution of the system, the facility of the realisation. The merit of B-mode images obtained with the coded signals will be discussed showing that as far as signal modulation is used the best results are obtained with periodic m-sequences     

EMAT noise suppression using information fusion in stationary wavelet packets

An important issue in ultrasonic nondestructive testing is the detection of flaw echoes in the presence of background noise created by instrumentation and by clutter noise. Signal averaging, autoregressive analysis, spectrum analysis, matched filtering, and the wavelet transform have all been used to filter noise in ultrasonic signals. Widely-used wavelet threshold estimation algorithms are not designed for electromagnetic acoustic transducer (EMAT) pulse-echo signals, and therefore do not exploit their unique impulse nature. The approach to ultrasonic signal filtering proposed in this paper is based on stationary wavelet packet denoising with a threshold influenced by several information sources: a statistical echo detection, the amplitude distribution of the wavelet transform coefficients, and a priori known system frequency characteristics. The proposed method was evaluated on signals measured with EMAT probes and under various SNR conditions; it outperforms the wavelet transform with the Stein unbiased risk estimate (SURE) threshold estimation method and split-spectrum processing (SSP). The results indicate SNR enhancement of 19 dB with real EMAT data.     

A successive parameter estimation algorithm for chirplet signal decomposition

In ultrasonic imaging systems, the patterns of detected echoes correspond to the shape, size, and orientation of the reflectors and the physical properties of the propagation path. However, these echoes often are overlapped due to closely spaced reflectors and/or microstructure scattering. The decomposition of these echoes is a major and challenging problem. Therefore, signal modeling and parameter estimation of the nonstationary ultrasonic echoes is critical for image analysis, target detection, and object recognition. In this paper, a successive parameter estimation algorithm based on the chirplet transform is presented. The chirplet transform is used not only as a means for time-frequency representation, but also to estimate the echo parameters, including the amplitude, time-of-arrival, center frequency, bandwidth, phase, and chirp rate. Furthermore, noise performance analysis using the Cramer Rao lower bounds demonstrates that the parameter estimator based on the chirplet transform is a minimum variance and unbiased estimator for signal-to-noise ratio (SNR) as low as 2.5 dB. To demonstrate the superior time-frequency and parameter estimation performance of the chirplet decomposition, ultrasonic flaw echoes embedded in grain scattering, and multiple interfering chirplets emitted by a large, brown bat have been analyzed. It has been shown that the chirplet signal decomposition algorithm performs robustly, yields accurate echo estimation, and results in SNR enhancements. Numerical and analytical results show that the algorithm is efficient and successful in high-fidelity signal representation.     

Signal matching wavelet for ultrasonic flaw detection in high background noise

The wavelet transform (WT) is widely used in ultrasonic flaw detection (UFD) systems because of its property of multiresolution time-frequency analysis. Those traditional WT-based methods for UFD use a wavelet basis with limited types to match various echo signals (called wavelet matching signals), so it is difficult for those methods to achieve the optimal match between echo signal and wavelet basis. This results in limited detection ability in high background noise for those WT-based methods. In this paper, we propose a signal matching wavelet (SMW) method for UFD to solve this problem. Unlike traditional UFD systems, in the proposed SMW, the transmitted signal is designed to be a wavelet function for matching a wavelet basis. This makes it possible to obtain the optimal match between the echo signal and the wavelet basis. To achieve the optimal match from the aspect of energy, we derive three rules for designing the transmitted signal and selecting the wavelet basis. Further, the parameter selection in applying the proposed SMW method to a practical UFD system is analyzed. In addition, a low-rate discrete WT structure is designed to decrease the hardware cost, which facilitates the practical application of the proposed SMW. The simulation results show that the proposed SMW can efficiently detect flaws in high background noise even with SNR lower than -20 dB, outperforming the existing methods by 5 dB.     

基于超声相控技术的船舶吃水测量方法

为了解决内河船舶因超吃水引起的通航阻塞以及船舶安全问题,提出一种基于超声相控技术的船舶吃水测量方法。以超声相控技术为理论基础,通过控制一维线型超声相控阵列换能器发射聚焦声波,实现对目标船舶的相控扫描,获取各扫描点的回波信号;利用匹配滤波算法对回波信号进行滤波,改善信噪比;利用阈值法提取回波信号的时延;利用渡越时间法,计算出扫描点到各发射振元中心的距离,利用双曲交汇法计算出扫描点的空间坐标;分析回波信号幅值和扫描点位置坐标即可得到船舶吃水深度。为验证方法的可靠性,搭建了小比尺船模吃水测量实验系统,分析了110~140 mm不同吃水深度下的实验结果,计算了实际吃水与测量吃水间的相对误差。实验结果表明,使用匹配滤波法处理后的回波信号信噪比从15.26 dB提高至36.39 dB,实验时,最大相对误差出现在船舶实际吃水130 mm时,绝对误差为2.3 mm,相对误差为1.7%。     

基于递归分析的微裂纹缺陷超声检测技术研究

为准确检测金属构件微裂纹缺陷,采用超声脉冲反射法对金属试块进行检测,并提取超声背散射信号进行分析。通过对背散射信号进行简单的建模,说明其组成要素。由于背散射信号的非线性特征,缺陷回波信号会对系统的递归特性产生影响;在此基础上提出了基于递归分析的方法分别对含人工微裂纹缺陷金属试块的无缺陷区、单裂纹区及双裂纹区背散射信号进行检测研究;通过合理的参数选择,对采样信号进行递归分析并绘制递归图;对比含缺陷信号与无缺陷信号,发现前者会在递归图中产生明显的白色交叉条纹带。使用递归定量分析进一步研究了含缺陷背散射信号的递归特征量。结果表明多种特征量对缺陷回波信号比较敏感,其中递归率(RR)和捕获时间(TT)在缺陷位置有明显的特征。