K-SVD和OMP算法在超声信号去噪中的应用

针对在线采集时超声波检测信号中存在大量噪声,降低了材料内部缺陷诊断准确性的问题,提出了一种基于广义K+奇异值分解算法(K-SVD)和正交匹配追踪算法(OMP)相结合的超声回波信号去噪算法。该算法利用K-SVD算法将Gabor字典训练成能够最有效反映信号结构特征的超完备字典,然后基于训练完成的超完备字典,用OMP算法把一定数量的字典原子进行线性组合来构成原始信号,从而实现信号的去噪。通过仿真实验将本文方法与传统的小波阈值去噪方法进行了对比研究。实验结果表明,该方法对超声回波信号的去噪效果优于小波阈值去噪方法,且噪声越大对比越明显,不仅可更有效地滤除信号中的高斯白噪声,提高信噪比,且尽可能保留了原始信号有用信息。     

相关检测技术在单脉冲激光测距中的应用

采用雪崩光电二极管探测微弱激光脉冲回波信号,依据相关检测技术对微弱的激光回波进行处理,从而获得较高信噪比回波信号.首先通过软件仿真验证了该实验方法的可行性,并在实验中采用消光比测量法对激光测距机测距能力进行了测试,将信噪比为-2.1 dB原始信号经过相关检测后提升至10 dB,消光比从原始激光测距模式的30 dB提升至44 dB.实验结果表明采用相关检测方法可以提高激光测距机测距能力及精准度,具有一定实用价值.     

卷积神经网络的缺陷类型识别分析

该文提出一种基于卷积神经网络直接对阵列超声检测原始信号进行缺陷类型识别的方法,该方法无需对超声回波原始信号进行特征提取.文章研究对比了不同卷积神经网络及其优化的识别性能.首先采用超声相控阵系统对不同试块上的平底孔、球底孔、通孔三种缺陷进行超声检测,然后利用LeNet5、VGG16和ResNet三种卷积神经网络对一维和二...     

强散射材料超声探伤的模糊检测

本文将模糊检测技术用于增强强散射材料的超声探伤信号，这里非缺陷散射体引起的结构噪声对缺陷回波干扰较大。我们在模糊检测中选择了互相关、相位差及分数维作为信号的特征量。实验结果表明，这种新方法较之常用的相关检测具有更好的性能。     

利用Duffing系统周期跳跃的弱超声导波识别

在超声导波检测中,由小缺陷产生的弱回波信号有可能淹没在噪声信号中从而造成漏检风险,通过分析Duffing系统随策动力幅值变化的分岔特性,获得了系统发生周期跳跃的临界状态,将与策动力同频率的弱回波信号作为干扰项叠加到Duffing系统临界状态中,相当于改变了策动力的幅值,从而引起临界状态的周期跳跃,依据系统的周期跳跃实现了弱超声导波回波信号的识别。进一步研究发现,超声导波导致策动力幅值增加或减小取决于截取信号与策动力的相位差,且两者之间互为异相位,同周期内近似相差π。以此构造了小尺度周期态和大尺度周期态两种检测模型,并利用仿真信号和实验信号检验了检测模型识别弱导波信号的有效性,同时对比了两种检测模型的抗噪声干扰能力,结果表明小尺度周期态检测模型具有结果唯一、抗噪声能力强的优点,该方法可有效的提高超声导波检测小缺陷的灵敏度。     

DPSD在低场核磁共振回波信号检测中的应用

核磁共振回波信号幅度微弱,容易受到来自电子器件和外部环境噪声的干扰,要求回波检测方法既能实时采集回波数据,又能较好的抑制噪声. 对比分析不同微弱信号检测方法的优缺点,归纳出DPSD(Digital Phase-Sensitivity Detection)方法在回波检测应用中的优越性. 通过仿真计算, 提出适用于回波信号检测的最佳滤波器窗函数. 在自制的核磁共振仪器上进行实验,结果表明,DPSD方法在满足回波信号实时测量的同时,有效压制了噪声,能准确的提取出低场核磁共振回波信号的幅度和相位信息.     

基于分数阶傅里叶变换混响抑制的目标回波检测方法

提出了一种声呐发射信号为线性调频信号时,基于分数阶傅里叶变换混响抑制的目标回波信号检测方法。通过计算混响的缓慢时变包络,对混响进行时间域的平稳化处理。对平稳化的混响信号滑动窗截取,对截取信号进行分数阶傅里叶变换,然后在分数阶傅里叶域进行滤波处理,再进行逆分数阶傅里叶变换恢复出时间域信号,最后输出该信号的能量。当滑动窗截取到目标回波信号时,窗内的混响噪声得到抑制,系统输出目标回波的能量,从而实现混响背景下的信号检测。通过计算机仿真和湖试实验结果,表明所提方法可以准确的在混响背景下检测到目标回波信号,并且在混响噪声背景条件下相比于匹配滤波器具有更好的检测性能。     

纳米包膜造影微泡的小波检测技术研究

用小波变换的方法检测人体组织的微小血流灌注包膜造影微泡回波。结合超声造影剂微泡振动的物理模型,构造了不同外加声场条件下的气泡小波,对原始信号进行小波变换,将变换后得到的小波系数分离;进而从微小血流灌注组织杂波中检测出造影剂微泡回波信号。心肌条件下的计算机模拟以及对灌注组织的仿体实验结果表明:与普通母小波相比,基于气泡振动模型的小波,因为是通过理论模型构造所得,已先验表征了造影剂微泡在声场中的回波特性,因此与实验中产生的回波信号具有更为紧密的相关性,从而经小波变换后,造影剂微泡回波信号与组织杂波产生了更高强度的信噪比,及更明显的可分离和对比效果。同时,构造了一个完备的母小波函数库,进一步提高了本方法的适用性以及健壮性。     

增强颗粒散射中缺陷回波信号的相关加权分离谱方法

粗晶材料的超声无损检测受背散射的影响,使得其信噪比很低,且这里的噪声是与发射超声波相干的噪声,不能用简单的时间平均来消除。分离谱技术已被证明是一种抑制背散射信号、提高信噪比的良好方法,许多人为此发展了各种理论作为分离谱的后处理算法。本文介绍了一种增强超声回波信号的相关加权前处理算法。这里,取自粗晶材料标准缺陷的窄脉冲超声回波被定义为标准子波,利用子波与超声检测信号的互相关作为权系数对检测信号进行加权,此技术与分离谱处理结合起来能使提高信噪比的性能更优。实验结果显示了本文所述方法可改善诸如奥氏体不锈钢一类粗晶材料超声检测的信噪比     

激光主动探测中回波信号的小波去噪方法

在分析激光主动探测中回波信号的噪声特性和小波变换去噪原理的基础上，提出了一种基于最大信噪比准则的小渡阈值去噪方法。首先用最大信噪比准则对小波变换系数进行阈值选取，然后采用软阂值方法对小波系数进行量化处理后再重构。仿真结果表明最大信噪比准则小波去噪方法改善信噪比效果十分显著，检测下限达到-16．2dB。证明了该方法在激光主动探测系统回波信号检测中的有效性。     

Signal-to-noise ratio enhancement based on the whitening transformation of colored structural noise

In the ultrasonic testing and evaluation of highly scattering materials (i.e. non-homogeneous media such as composites, layered and clad materials) structural noise is an important limitation to the visibility of flaw echoes. This noise cannot be reduced by conventional linear filtering or by time-averaging techniques. In order to enhance the defect-to-background noise ratio (SNR), many different algorithms have been developed over the years. This work analyzes three new strategies for SNR enhancement based on the whitening transformation of the colored structural noise. By using this transformation, the small spectral differences between noise and flaw echoes are exploited, thereby allowing an improvement in the visibility of the flaw.     

An improved automated ultrasonic NDE system by wavelet and neuron networks

Despite of the widespread and increasing use of digitized signals, the ultrasonic testing community has not realized yet the full potential of the electronic processing. The performance of an ultrasonic flaw detection method is evaluated by the success of distinguishing the flaw echoes from those scattered by microstructures. So, de-noising of ultrasonic signals is extremely important as to correctly identify smaller defects, because the probability of detection usually decreases as the defect size decreases, while the probability of false call does increase. In this paper, the wavelet transform has been successfully experimented to suppress noise and to enhance flaw location from ultrasonic signal, with a good defect localization. The obtained result is then directed to an automatic Artificial Neuronal Networks classification and learning algorithm of defects from A-scan data. Since there is some uncertainty connected with the testing technique, the system needs a numerical modelling. So, knowing the technical characteristics of the transducer, we can preview which are the defects that experimental inspection should find. Indeed, the system performs simulation of the ultrasonic wave propagation in the material, and gives a very helpful tool to get information and physical phenomena understanding, which can help to a suitable prediction of the service life of the component.     

Speckle reduction by energy time-frequency filtering

Structural noise is a very important limitation to the visibility of flaw echoes in ultrasonic testing and evaluation of highly scattering materials. In order to enhance the signal-to-noise ratio, different algorithms have been developed. One of these techniques is based on filtering the spectrum low band of the received echo to obtain a significant improvement of the defect visibility. Based on this idea, in this work a new time-frequency technique is presented. In this method, block-processing autoregressive techniques are used to estimate the instantaneous center frequency of the traveling wave. From this information, a time-frequency filter is designed tuned at half the estimated instantaneous center frequency. Experimental results and the comparison with the non-time-frequency filtering technique are also included, showing that the proposed method has an excellent performance on SNR enhancement.     

Signal-to-noise ratio enhancement based on wavelet filtering in ultrasonic testing

In ultrasonic non-destructive testing of materials with a coarse-grained structure the scattering from the grains causes backscattering noise, which masks flaw echoes in the measured signal. Several filtering methods have been proposed for improving the signal-to-noise ratio. In this paper we present a comparative study of methods based on the wavelet transform. Experiments with stationary, discrete and wavelet packet de-noising are evaluated by means of signal-to-noise ratio enhancement. Measured and simulated ultrasonic signals are used to verify the proposed de-noising methods. For comparison, we use signal-to-noise ratio enhancement related to fault echo amplitudes and filtering efficiency specific for ultrasonic signals. The best results in our setup were achieved with the wavelet packet de-noising method.     

Wavelet based noise suppression technique and its application to ultrasonic flaw detection

The noise suppression techniques with wavelet transform (WT) are widely used in non-destructive testing and evaluation (NDT&E), especially in ultrasonics. Complete reconstruction theory with hard or soft thresholds, reconstruction technique based on the singularities of noise and signal, matched filter with an impulse response, and optimal frequency-to-bandwidth ratio of wavelet technique have all been used to analyze ultrasonic signals for noise suppression. But a more simple and effective technique has been pursued for decades. This paper develops a new technique using WT for the right purpose. In this work, WT is treated as a band-pass filter whose central frequency and frequency bandwidth (CF&FB) are determined by the spectra distribution of an ultrasonic signal captured from real testing situation. For the purpose of matching their CF&FB well, a technique for evaluating the optimal scale of a daughter wavelet is carried out too. By acting this daughter wavelet as a band-pass filter, we can obtain excellent de-noising results, even when the signal to noise ratio (SNR) is below -18 dB. The performance of the technique has been done by ultrasonic signals with computer generated white noises. Finally, the experimental verification is performed on a pipeline specimen with man-made small flaws with good results obtained. The results show that the technique is more suitable for processing heavy noised ultrasonic signals, and it can also be used in automatic flaw detection.     

Flaw-to-grain echo enhancement by split-spectrum processing

A split-spectrum processing technique for an ultrasonic flaw detection system has been developed which improves the flaw-to-grain echo ratio in large-grained materials. The enhancement is achieved by partitioning a wide-band received spectrum to obtain frequency shifted bands, which are then processed to suppress the grain echoes with respect to the flaw echo, using a novel signal minimization algorithm. Experimental data for titanium and stainless steel samples are presented which show superior flaw detection capabilities for the minimization algorithm with respect to frequency averaging techniques.     

基于双高斯衰减模型的超声回波处理方法

信号降噪与特征提取是超声检测数据处理的关键技术.基于超声信号有特定结构而噪声和超声信号的结构无关,本文提出一种旨在解决强噪声背景下超声回波的参数估计和降噪问题的方法.该方法将超声回波的参数估计和降噪问题转换为函数优化问题,首先根据工程经验建立超声信号的双高斯衰减数学模型,然后根据观测回波和建立的超声信号模型确定目标函数,接着选择人工蜂群算法对目标函数进行优化从而得到参数的最优估计值,最后由估计出的参数根据建立的超声信号数学模型重构出无噪的超声估计信号.通过仿真和实验表明本文方法可以准确估计出信噪比大于-10 dB的含噪超声回波中的无噪信号,且效果优于基于自适应阈值的小波降噪方法和经验模态分解方法;此外相比常用的指数模型和高斯模型,本文提出的双高斯衰减超声信号模型与实测超声信号更接近,其均方误差为9.4×10~(-5),波形相似系数为0.98.     

利用人工神经网络实现缺陷类型识别

本文在对各向同性均匀固体中横穿孔，平底孔和裂和裂缝缺陷超声散射特性分析的基础上，分别用回波幅度谱和去郑积幅度谱作为特征量，利用人工神经网络对缺陷类型进行识别。     

超声检测中人工神经网络对缺陷定量评价

分析了均匀各向同性介质的超声检测中,耦合因素和仪器因素造成的脉冲回波幅度的差异,提出了误差校正方法。以校正后的缺陷回波和底面反射波的峰-峰值为特征量,利用人工神经网络进行缺陷类型识别和大小评价。
为模拟自然缺陷的二基本要素——光滑曲面和带棱边的平面,用有机玻璃制作了代表性的横穿孔和平底孔缺陷样品共18个.对18个缺陷样品的缺陷回波和底面反射波的峰-峰值测量了四次,并进行的校正.用人工神经网络对这组缺陷样品进行的处理结果表明:(1)设定的缺陷类型全部准确识别。(2)估计缺陷大小与标称孔径吻合较好。最后,对测量误差和缺陷大小估计误差进行了分析。     

超声探头的响应特性及暂态回波模型

建立检测系统的数学模型,可以更好地理解超声检测的物理本质。分析了超声波从产生、介质中传播、缺陷耦合以及接收的全过程,将缺陷回波表示为探头响应函数与缺陷响应的时域卷积。利用空间脉冲响应和基尔霍夫近似建立了超声波与平面型缺陷的耦合模型,用大平面试块底面回波和大平面响应进行反卷积求得了探头的响应函数,并详细分析了探头在不同偏置位置时不同大小缺陷响应的特点,发现缺陷回波由直达回波和边缘回波组成,直达回波和边缘回波极性相反,且直达回波的幅值远远大于边缘回波。