一种强噪声背景下微弱超声信号提取方法研究

为解决在强噪声背景下获取超声信号的难题,基于粒子群优化算法和稀疏分解理论提出一种强噪声背景下微弱超声信号提取方法.该方法将降噪问题转换为在无穷大参数集上对函数进行优化的问题,首先以稀疏分解理论和超声信号的结构特点为依据构建了粒子群优化算法运行所需要的目标函数及去噪后信号的重构函数,从而将粒子群优化算法和超声信号降噪联系在一起;然后根据粒子群优化算法可以在连续参数空间寻优的特点建立了用于匹配超声信号的连续超完备字典,并采用改进的自适应粒子群优化算法在该字典中对目标函数进行优化;最后根据对目标函数在字典上的优化结果确定最优原子,并利用最优原子按照重构函数重构出降噪后的超声信号.通过对仿真超声信号和实测超声信号的处理,结果表明本文提出的方法可以有效提取信噪比低至-4 dB的强噪声背景下的微弱超声信号,且和基于自适应阈值的小波方法相比本文方法表现出更好的降噪性能.     

Gaussian wavelet based dynamic filtering (GWDF) method for medical ultrasound systems

In this paper, a novel dynamic filtering method using Gaussian wavelet filters is proposed to remove noise from ultrasound echo signal. In the proposed method, a mother wavelet is first selected with its central frequency (CF) and frequency bandwidth (FB) equal to those of the transmitted signal. The actual frequency of the received signal at a given depth is estimated through the autocorrelation technique. Then the mother wavelet is dilated using the ratio between the transmitted central frequency and the actual frequency as the scale factor. The generated daughter wavelet is finally used as the dynamic filter at this depth. Frequency-demodulated Gaussian wavelet is chosen in this paper because its power spectrum is well-matched with that of the transmitted ultrasound signal. The proposed method is evaluated by simulations using Field II program. Experiments are also conducted out on a standard ultrasound phantom using a 192-element transducer with the center frequency of 5 MHz. The phantom contains five point targets, five circular high scattering regions with diameters of 2, 3, 4, 5, 6 mm respectively, and five cysts with diameters of 6, 5, 4, 3, 2 mm respectively. Both simulation and experimental results show that optimal signal-to-noise ratio (SNR) can be obtained and useful information can be extracted along the depth direction irrespective of the diagnostic objects.     

超声非线性效应表征的动态小波指纹分析方法

针对结构早期损伤超声非线性检测中损伤表征问题,发展了一种基于动态小波指纹的超声信号分析方法,从超声信号的动态小波指纹分布中提取出一种可用于结构早期损伤表征的超声非线性特征参数。研究了小波基函数及分析尺度对超声非线性效应提取效果的影响,优选出对结构早期损伤敏感的小波基函数以及尺度范围。将提出的动态小波指纹分析方法应用于二次谐波及混频非线性超声检测信号分析,结果表明,动态小波指纹分析方法可有效提取出检测信号中的二次谐波及混频分量,基于小波指纹分布的非线性特征参数可用于板结构中微裂纹的定量表征.本文研究工作为结构早期损伤超声非线性检测中的弱非线性效应提取作了有益探索。      

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 filter-based weak signature detection method and its application on rolling element bearing prognostics

De-noising and extraction of the weak signature are crucial to fault prognostics in which case features are often very weak and masked by noise. The wavelet transform has been widely used in signal de-noising due to its extraordinary time-frequency representation capability. In this paper, the performance of wavelet decomposition-based de-noising and wavelet filter-based de-noising methods are compared based on signals from mechanical defects. The comparison result reveals that wavelet filter is more suitable and reliable to detect a weak signature of mechanical impulse-like defect signals, whereas the wavelet decomposition de-noising method can achieve satisfactory results on smooth signal detection. In order to select optimal parameters for the wavelet filter, a two-step optimization process is proposed. Minimal Shannon entropy is used to optimize the Morlet wavelet shape factor. A periodicity detection method based on singular value decomposition (SVD) is used to choose the appropriate scale for the wavelet transform. The signal de-noising results from both simulated signals and experimental data are presented and both support the proposed method.     

Wavelet basis selection and feature extraction for shift invariant ultrasound foreign body classification

This paper proposes a novel technique for automatic ultrasound non-destructive foreign body (FB) detection and classification. A signal registration process is introduced to eliminate shift variations commonly encountered in ultrasound signals. Information theory based methods are then developed for wavelet basis selection and feature extraction to facilitate robust FB classification. Probabilistic neural networks are used for FB classification. Experimental results confirm that the wavelet basis selected by the proposed method improves the FB classification accuracy. It is concluded that low order wavelet bases have better ability to distinguish classes with great similarities than their higher order counterparts, while the reverse is true for more divergent classes.     

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.     

利用经验模态分解的高频水声信号滤波方法*

研究了一种高频水声信号的滤波问题,提出了一种改进的经验模态分解加小波阈值滤波方法。首先对信号进行带通滤波处理,再进行经验模态分解,将分解得到的各个模态转换为频域信号,采用小波软阈值方法对这些频域信号进行滤波,最后对信号进行重构,并转换为时域信号。经数值仿真与试验数据验证表明此方法是可行有效的,与原基于经验模态分解的小波阈值滤波方法相比,本方法滤波效果较好:对不同输入信噪比的仿真信号进行滤波后,本方法的输出信噪比最大提高17.41 d B,滤波后所得信号与加噪前纯信号的相关系数最大提高0.90;对实验数据进行滤波后,不同时间段信号的相关系数最大提高0.62。     

非线性时间序列的小波分频预测

基于噪声的小波变换特点，结合小波包分解和模极大重构来抽取含噪信号的主分量，提出了一种基于最佳尺度分解和Volterra自适应滤波的分频预测算法，使用较少的模型训练样本，同时具有强的抗噪能力.该算法克服了传统小波分解尺度选取的盲目性及单纯Volterra预测器抗噪性能的不足，数值仿真表明，针对含强噪声的非线性信号可进行有效预测. 关键词： 小波分解 Volterra自适应滤波器 分频预测     

Non-linear filtering of ultrasonic signals using neural networks

Structure noise from inhomogeneous micro-structures makes the detection of flaws present in highly scattering materials difficult. Several techniques have been applied to improve the signal-to-noise ratio (SNR) in order to make flaw detection easier. Linear filtering does not provide good results because both structure noise and flaw signal concentrate energy in the same frequency band. Non-linear filtering can be used to reduce the structure noise of ultrasonic signals. Therefore, neural networks are applied in this work for this purpose. In order to use neural networks for non-linear filtering, dynamic structures must be applied. The easiest way to implement a neural network with the capability of processing temporal patterns is to consider them spatial ones, applying the signal into a tapped delay line of finite extension, that is the input of a static neural network (for example, a multi-layer perceptron). In this work, a dynamic neural network has been built to filter ultrasonic signals with structure noise, and has been trained with the real-time back-propagation algorithm, using as inputs 3000 synthetic ultrasonic signals of 896 samples each. Target signals for training are the same as the ones used as inputs but without noise. The neural network is trained in order to generate as output the target signal when the noisy input one is applied. For testing the performance of the non-linear filter, a new set of 500 noisy signals has been used. The SNR improvement is about 6 dB average. The results show that this non-linear filtering method is quite useful as pre-processing stage in flaw detection systems.     

Ultrasonic flaw detection using threshold modified S-transform

Interference noising originating from the ultrasonic testing defect signal seriously influences the accuracy of the signal extraction and defect location. Time–frequency analysis methods are mainly used to improve the defects detection resolution. In fact, the S-transform, a hybrid of the Short time Fourier transform (STFT) and wavelet transform (WT), has a time frequency resolution which is far from ideal. In this paper, a new modified S-transform based on thresholding technique, which offers a better time frequency resolution compared to the original S-transform is proposed. The improvement is achieved by the introduction of a new scaling rule for the Gaussian window used in S-transform. Simulation results are presented and show correct time frequency information of multiple Gaussian echoes under low signal-to-noise ratio (SNR) environment. In addition, experimental results demonstrate better and reliable detection of close echoes drowned in the noise.     

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.     

A LLS operator based S-I WT de-noising algorithm applied in EDXRF

An improved shift-invariant wavelet (S-I WT) de-noising algorithm based on LLS operator is proposed for high-resolution energy dispersive X-ray fluorescence. Sym8 is chosen as the wavelet basis function and performed noise reduction on the analog signal. Comparison of the de-noising effect of S-I WT, improved WT and LLS S-I WT (where LLS is the log square root operator) method are quantitatively evaluated by using evaluation criteria signal-to-noise-ratio (SNR), root mean square error and Pearson correlation coefficient. Meanwhile, a new evaluation criterion of de-noising effect, called peak area relative difference, is also proposed to evaluate the counting deviation. The results show that the LLS-SI WT is simple and reliable, can effectively reduce pseudo-Gibbs artificial signals and statistical fluctuation. Besides, this method simplifies the calculation, reduces the running time and improves the running efficiency. The LLS-SI WT is also applied to reduce the noise after adding strong noise to the signal, the SNR has been improved from 14.0040 to 14.7552, and most of the characteristic peak information retains to the greatest extent.     

Lidar signal de-noising based on wavelet trimmed thresholding technique

Lidar is an efficient tool for remote monitoring, but the effective range is often limited by signal-to-noise ratio (SNR). By the power spectral estimation, we find that digital filters are not fit for processing lidar signals buried in noise. In this paper, we present a new method of the lidar signal acquisition based on the wavelet trimmed thresholding technique to increase the effective range of lidar measurements. The performance of our method is investigated by detecting the real signals in noise. The experiment results show that our approach is superior to the traditional methods such as Butterworth filter.     

A novel scheme of optical Meyer wavelet filter based on time lenses in nonlinear multi-span optics fiber communication system

In this work, a novel scheme to realize optical Meyer wavelet filter based on time lenses and Mach–Zehnder modulators in optical domain was proposed and the transfer functions of Meyer wavelet decomposition and reconstruction processed in frequency domain were derived to realize Meyer wavelet de-noising for optical signal. The filter could be used to reduce the nonlinear noise induced by the interaction of EDFA’s ASE noise, fiber’s dispersion and nonlinearity in high bit rate multi-span optical communication systems without photo-electric conversion. The bit error rate curves of the optical bit sequence without and with optical Meyer wavelet filter were plotted to show the effectiveness of the optical Meyer wavelet filter, which can achieve better result and improve the optical communication quality in dispersion compensation links.     

Study on the denoising method for the electromagnetic ultrasonic echoes from multiple interfaces

The electromagnetic ultrasound is used in the detection of interfaces of the adhesive multilayer structures to solve the unstable coupling problem in ultrasonic testing by traditional piezoelectric transducers. Based on the analysis of the transforming mechanism of electromag-netic ultrasound energy and the resultant dead zone from mutual inductance of the transducer, the wavelet filtering by soft-thresholding and adaptive noise canceling methods are used simul-taneously to the detected electromagnetic ultrasonic signals to overcome the drawbacks of the low signal to noise ratio （SNR） and the wide intrinsic dead zone of the transducer. Processed results in the interface detection of a three layered adhesive sample of steel and rubber materials demonstrate that the wavelet filtering enhances the SNR about 12dB while the adaptive noise canceling narrows the dead zone effectively.     

基于小波包变换的复合材料超声波检测信号特征提取

本文以碳纤维复合材料常见缺陷分层、孔隙、疏松的超声波检测缺陷信号为研究对象，对超声波检测信号进行小波包变换，提取包含信号绝大部分能量的近似系数波形特征及细节系数的统计量作为样本的特征值。应用BP神经网络分类器进行分类识别验证，取得较好的识别效果。该方法能以较小的特征维数表征原始信号特点。     

声发射衰减特性管道泄漏定位方法

声发射技术具有灵敏度高、实时性强、覆盖范围大等优点.泄漏产生的声发射波沿管壁传播会发生衰减,通过研究衰减系数与金属晶粒散射和热流损失的关系,建立准确的声发射能量衰减模型.在此基础上,针对声发射频带宽的特点对传统衰减定位模型进行改进,提出宽频带声发射源定位模型,该方法先通过实验确定泄漏信号频带,再将滤波后的信号经过小波包...     

Algorithms for suppressing ultrasonic backscattering from material structure

In pulse-echo ultrasonic inspection the backscattering from the material structure appears in the received ultrasonic images as clutter, often referred to as grain noise, which impairs the inspection results. A toolbox including algorithms for suppressing ultrasonic clutter is presented in the paper. Several processing algorithms capable of suppressing grain noise have been proposed, of which the split spectrum processing (SSP) probably is the most renowned. The classical SSP technique applies a filter bank to some frequency band that has to be precisely known in advance, to obtain a set of narrow-band signals that are tested for mutual correlation using some statistical operation. A number of SSP algorithms with different statistical operations are included in the toolbox. A completely different approach is to use explicit statistical models of grain noise and defects and to design an optimal filter based on those models. A simple such algorithm, based on noncoherent detection (NCD) known from communications, is also included in the toolbox. The toolbox, implemented in Matlab, is provided with a user-friendly graphical interface facilitating comparison of the algorithms.     

小波包节点分段阈值降噪在水声监听中的应用*

水声目标信号在发送、传播过程中,易受到环境噪声、系统自噪声等影响,因此水声监听过程中目标信号会掺杂大量噪声信息。为提高获取目标信号的准确性和可靠性,降低噪声,在已有小波分析基础上,提出小波包节点相对能量判断最优分解层,最优分解层节点系数分段阈值处理重构方法,实现水声监听信号分频段去噪。将0.1 kHz~8.4 k Hz实验数据按节点频率排序划分为5个强弱不同的频段信号实现消噪提取,结果表明该方法可将噪声信号与目标信号有效分离,与全局单一阈值相比,具有较好降噪能力。该方法打破了小波阈值去噪高频处理的局限性,提高了识别精度,改善了全局单一阈值去噪存在的短板,在鱼类分析识别、舰船监听、深海探测等方面具有一定的推广和应用价值。