Influence of thresholding procedures in ultrasonic grain noise reduction using wavelets

Wavelet transform based techniques are used for signal-to-noise ratio (SNR) enhancement in ultrasonic non-destructive testing and evaluation of strong sound scattering materials. The overall denoising performance of a wavelet signal processor is conditioned by several processing parameters, including the type of wavelet, thresholding method, and threshold selection rules. Different thresholding procedures and threshold selection rules are analysed in this paper using the discrete wavelet transform and decomposition level dependent thresholds. Global performance is evaluated by means of the SNR enhancement using synthetic grain noise registers with an incrusted flaw signal, with different values of the input SNR, and experimental ultrasonic traces acquired from a carbon fibre reinforced plastic composite block.     

Relevant modes selection method based on Spearman correlation coefficient for laser signal denoising using empirical mode decomposition

Empirical mode decomposition (EMD) is a recently proposed nonlinear and nonstationary laser signal denoising method. A noisy signal is broken down using EMD into oscillatory components that are called intrinsic mode functions (IMFs). Thresholding-based denoising and correlation-based partial reconstruction of IMFs are the two main research directions for EMD-based denoising. Similar to other decomposition-based denoising approaches, EMD-based denoising methods require a reliable threshold to determine which IMFs are noise components and which IMFs are noise-free components. In this work, we propose a new approach in which each IMF is first denoised using EMD interval thresholding (EMD-IT), and then a robust thresholding process based on Spearman correlation coefficient is used for relevant modes selection. The proposed method tackles the problem using a thresholding-based denoising approach coupled with partial reconstruction of the relevant IMFs. Other traditional denoising methods, including correlation-based EMD partial reconstruction (EMD-Correlation), discrete Fourier transform and wavelet-based methods, are investigated to provide a comparison with the proposed technique. Simulation and test results demonstrate the superior performance of the proposed method when compared with the other methods.     

Detecting small flaws near the interface in pulse-echo

Flaw detection near the interface surface is a common problem in many pulse-echo NDT applications due to interference with the interface echo, orders of magnitude above the flaw echoes. Several digital signal processing techniques like deconvolution, Hilbert transform and cepstrum analysis have been proposed to improve axial resolution. However, they require strict linearity, which takes a large portion of the system dynamic range just to fit the interface echo, thus reducing the dynamic range available for flaw detection. This work presents a new alternative based on the time-domain phase analysis of the received signals. Differently from conventional approaches, it works quite well with saturated signals resulting when a high gain is applied to detect small flaws. These can be detected in a range of a fraction of one wavelength from the interface surface, even using narrow-band transducers, as it has been experimentally verified. The method can be easily hardware implemented for real-time processing.     

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.     

基于小波域可导阈值函数与自适应阈值的脉冲星信号消噪

脉冲星辐射信号具有极低的信噪比, 传统降噪方法难以在抑制噪声的同时保留其细节信息. 为此, 提出了一种小波域脉冲星信号消噪方法. 在小波域, 利用一种可导的阈值函数和一种自适应阈值选取方法, 对含噪脉冲星信号进行小波域阈值去噪处理, 并利用信噪比(SNR)、 均方根误差(RMSE)、 峰值相对误差(REPV) 以及峰位误差(EPP) 四项指标来对去噪效果进行评价. 实验结果表明, 与软阈值与硬阈值法相比, 该方法能在有效去除含噪脉冲星信号中的噪声、 显著提高其信噪比的同时, 更有效地保留原始信号中的有用信息, 同时可以获得更小的均方根误差、 脉冲峰值相对误差与峰位误差.     

采用视觉细胞响应模型的小目标复杂背景抑制

为了提高地面和云层等红外复杂背景下弱小目标的检测性能,提出了一种基于视觉细胞响应模型的红外弱小目标背景抑制新方法.首先利用简单细胞的感受野计算模型将原始图像采用Gabor函数卷积获得相同大小的两幅图像|然后采用设计的复杂细胞响应的非线性汇聚策略函数对获得的两幅图像进行融合处理,从而将红外图像中弱小目标和背景杂波分离,达到抑制背景的目的|最后采用自适应阈值分割技术得到目标点,实现了对红外弱小目标的检测跟踪.实验结果显示,与去局部均值和最大中值滤波两种滤波方法相比较,该方法能有效地检测出信杂比较低的弱小目标信号.     

Accurate segmentation of leukocyte in blood cell images using Atanassov's intuitionistic fuzzy and interval Type II fuzzy set theory

In this paper automatic leukocyte segmentation in pathological blood cell images is proposed using intuitionistic fuzzy and interval Type II fuzzy set theory. This is done to count different types of leukocytes for disease detection. Also, the segmentation should be accurate so that the shape of the leukocytes is preserved. So, intuitionistic fuzzy set and interval Type II fuzzy set that consider either more number of uncertainties or a different type of uncertainty as compared to fuzzy set theory are used in this work. As the images are considered fuzzy due to imprecise gray levels, advanced fuzzy set theories may be expected to give better result. A modified Cauchy distribution is used to find the membership function. In intuitionistic fuzzy method, non-membership values are obtained using Yager's intuitionistic fuzzy generator. Optimal threshold is obtained by minimizing intuitionistic fuzzy divergence. In interval type II fuzzy set, a new membership function is generated that takes into account the two levels in Type II fuzzy set using probabilistic T co norm. Optimal threshold is selected by minimizing a proposed Type II fuzzy divergence. Though fuzzy techniques were applied earlier but these methods failed to threshold multiple leukocytes in images. Experimental results show that both interval Type II fuzzy and intuitionistic fuzzy methods perform better than the existing non-fuzzy/fuzzy methods but interval Type II fuzzy thresholding method performs little bit better than intuitionistic fuzzy method. Segmented leukocytes in the proposed interval Type II fuzzy method are observed to be distinct and clear.     

Ultrasonic flaw detection in NDE of highly scattering materials using wavelet and Wigner-Ville transform processing

In ultrasonic non-destructive evaluation of highly scattering materials the backscattering noise may attain peak values greater than the searched flaw pulse and the mean value of noise spectrum is very similar to the searched echo spectrum. Several specific methods have been proposed for the reduction of this type of noise, but the comparison of the performance of different methods is still an open problem. In this paper, we make a comparison among some methods based on simultaneous representations in time and frequency/scale domains of the ultrasonic traces. Synthetic and experimental traces are de-noised using a discrete wavelet processor with decomposition level-dependent threshold selection and a method that combines Wigner-Ville transform and filtering in the time-frequency domain. The results are comparatively evaluated in terms of signal to noise ratio and probability of detection.     

Time-varying prediction filter for structural noise reduction in ultrasonic NDE

Predominant physical phenomenon in highly scattering materials is the attenuation due to dispersion. Therefore, received echo has high frequencies more severely attenuated than low frequencies and the structural noise can be modeled as a non-stationary random process. Most of the proposed techniques for enhancing the flaw visibility do not exploit the frequency dependency of the incoming flaw signal, assuming homogeneous behaviour of the insonified material. In this work, a new technique based on exploiting the non-stationary nature of the incoming UT signal is presented. Proposed technique is based on the prediction error obtained with a linear and time-varying parametric model of the noise. By this method, when the analyzed UT echo has only structural noise, the prediction error is low, however, if it contains a flaw, high prediction error occurs because a flaw is a non-predictable alteration of the material structure. Experiments with stainless steel show that this method has an excellent performance on SNR enhancement.     

微陀螺滑模控制器中的积分算法设计（英）

为了去除不定积分环节产生的直流偏置项,提出了一种基于一次移动平均法的改进积分算法。利用一个整数周期的积分信号值构成一个集合,计算出集合的算术平均值作为瞬时直流偏置,将原积分信号减去该直流偏置,最终可得到无直流偏置项的积分信号。为实时更新直流偏置,在每个时钟上升沿将一个积分信号的新采样值加入集合尾部,并去除集合首部的数值点,从而确保集合长度维持固定值并能实时反映出最新的直流偏置。以FPGA为信号处理平台,采用Modelsim仿真和实验测试相结合的方法验证了该算法的可行性。该方法已成功用于电磁式微陀螺仪的滑模控制器之中。同已有方法相比,该积分算法复杂度低、实时性好、易于实现。     

Multivariate Calibration with the Delaunay Triangulation Method: Definition of the Calibration Domain

The Delaunay triangulation (DT) method for multivariate calibration is a topological multivariate calibration method. In this paper, we present methods for the definition of the calibration domain. Outliers in the calibration set must be found and deleted and clusters detected. When clusters are found, it may be advantageous to make separate local models. Two methods are proposed. The first, called the DT calibration domain algorithm, is based on finding a kernel of samples that is then extended according to local rules. An alternative is to first eliminate gross outliers and then divide the data set in clusters, if such clusters exist, with Dbscan, a density‐based clustering method. The cluster(s) is (are) then used as kernels(s) and extended with the same rules as the DT calibration domain algorithm to develop DT models for each cluster. The two methods and some of the difficulties that can be encountered with them are demonstrated with three simulated data sets and tested with three real NIR data sets (one agricultural, one food, and one industrial). It is shown that the methods perform well and are at least comparable in prediction performance to partial least squares (PLS).     

基于激光多普勒频移的钢轨缺陷监测

针对现阶段我国铁路上应用的探伤设备只能在天窗时间进行人工巡检,无法在线监测的问题,提出一种基于超声导波的激光多普勒频移法钢轨内部缺陷监测方法。首先,引入环境温度作为变量改进了半解析有限元方法,并应用该方法获得了我国无缝线路CHN60钢轨在特定温度下的频散曲线。通过分析振型并结合激励响应算法确定了适于检测缺陷的模态及其激励方式,从而激励该超声导波模态使其在钢轨中传播。然后,应用半反半透玻璃镜将激光分为参考光和测量光,测量光通过Bragg Cell进行频偏照射钢轨表面,通过反射光产生的多普勒频移与参考光干涉得到光强度变化曲线,经过信号处理及标定测得钢轨内部缺陷的回波速度信号,再经过数字化处理和计算得到缺陷的位置。最后,在北京环形铁路试验基地进行了现场实验,以钢轨接地孔模拟钢轨内部核伤,得到缺陷定位误差均小于0. 5 m,验证了该方法的可行性。使用激光多普勒频移方法检测导波信号从而定位缺陷的方法可以有效避免由于换能器接触性测量而产生的误差。该方法在不影响列车的正常运营的同时,实现了全天候无间断的在线监测,提高了检测效率。     

An amplitude correlation and differencing method for the monitoring of flaws in repeat ultrasonic inspections

A new amplitude correlation and differencing (ACD) method for the detection of flaw changes using digital pulse-echo ultrasonic data is proposed. The ACD method is based on signal processing techniques applied to digitized pulse amplitudes from scans of the same transducer recorded during two separate ultrasonic inspections. Changes in flaws with a wide variety of sizes and other characteristics can be detected, provided the change is in a direction perpendicular to the centre line of the ultrasonic beam.Practical experience of this method was gained by analysis of ultrasonic data from two inspections of a weld, separated by a year. The effectiveness of the ACD method for flaw monitoring is compared with that of the 6 dB drop sizing method, before and after resolution improvement by the synthetic aperture focusing technique (SAFT).     

基于小波变换的压缩感知理论对水质检测紫外-可见光谱数据的去噪研究

消除噪声影响对提高直接光谱法水质检测系统的测量稳定性和精度都具有重要意义。直接光谱法在线水质检测系统通常采用长寿命、无需预热的脉冲氙灯和适用于复杂检测环境的工业级光谱探测装置。针对整个光谱探测系统受到光源、光路和光电转换器件的严重影响,测定的光谱数据含有大量噪声这一实际问题,提出了基于小波变换的压缩感知去噪算法,并与传统小波阈值去噪方法进行了比较实验。针对化学需氧量为200 mg·L-1的邻苯二甲酸氢钾标液的紫外-可见光谱数据进行去噪处理,采用压缩感知去噪算法,将信号在小波域内分解,得到含噪高频系数;采用随机高斯矩阵作为压缩感知算法的观测矩阵,压缩比设置为2,对高频系数进行观测;选择正交匹配追踪算法恢复高频小波系数的稀疏性,从而达到去噪目的。同时针对传统的小波阈值去噪,采用daubechies4作为小波基的软阈值滤波方法对光谱数据进行去噪处理。为验证去噪算法的可行性,采集某溪水和城市生活污水的光谱信号分别采用以上两种方法进行去噪处理,实验结果表明：基于小波变换的压缩感知去噪算法适用于紫外-可见光谱法在线水质检测系统,该方法能在保留水样原始光谱信号的吸收特征的前提下有效地去噪,且去噪效果优于小波阈值去噪算法。与小波阈值去噪算法相比,信噪比提高了12.201 5 dB,均方根误差减小了0.009 3,峰值信噪比增加了5.299 dB。不仅避免了小波阈值去噪过程中阈值的选取依靠主观判断问题,而且在重构过程中有效地抑制了噪声,为直接光谱法检测水质参数提供了一种新的解决方案。     

New techniques of local damage detection in machinery based on stochastic modelling using adaptive Schur filter

Vibration signal analysis is one of the most effective techniques of monitoring machinery and detecting local damage in their parts, e.g. bearings and gearboxes. However, such detection is sometimes difficult, especially in heavy industrial machines, because of a small proportion of damage-induced components in relation to the remaining components of registered signals. Therefore, more effective signal processing algorithms are being looked for. Moreover, local damage (cracking, pitting, spalling, breakage, etc.) in bearings and gearboxes generates broad-spectrum impulse signals, while the other type can be effectively modelled as a sum of narrowband signals. In this article, techniques based on Schur adaptive filter are proposed for local damage detection. In such an approach, the analysed signal is modelled by means of autoregressive process and the filter is described by so-called reflection coefficients. Schur algorithm is an effective algorithm with very good numerical properties and it is capable of tracking rapid changes in second order statistics of the analysed signal. Thus, the method is well-suited to analysing non-stationary signals and it is potentially interesting for use in bearing and gearbox monitoring.Reflection coefficients describing the signal model, defined with the use of Schur algorithm, may be applied in a variety of ways, giving a chance of employing different solutions in different conditions. In the first proposed solution, detection is based on the weighted sum of derivatives of reflection coefficients, while in the other one – on new signal obtained as power in frequency bands calculated from a parametric spectrogram, whose starting point are reflection coefficients. All these operations are aimed at enhancing changes that occur in the signal at the moments when damage-induced impulses appear. The article also presents guidelines for methods of determining parameter values in the employed analyses. The proposed solutions have been applied for analysing signals coming from a two-stage gearbox of a large machine driving a mining belt conveyor and the obtained results were analysed. They prove the effectiveness of the proposed techniques. It is worth emphasizing that these techniques can be easily adapted for monitoring machinery in varying operating conditions.     

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.     

背景和装甲目标毫米波被动探测统计特性研究

根据背景和装甲目标不同的毫米波辐射机理,采用不同的统计方法得到了背景和装甲目标的不同统计特性。针对背景的不均匀性造成的背景辐射特性的非单一性,提出了采用假设检验的方法得到背景的统计分布;针对装甲目标难以得到统计分布的特点,提出了以目标信号复杂度为特征量的装甲目标辐射特性的统计方法。为毫米波被动探测地物及装甲目标识别方法的研究提供了必要的理论依据,具有切实可行的指导意义。     

基于多模态信号的金属材料缺陷无损检测方法

金属材料缺陷检测对于经济发展具有重要意义.针对现有无损检测技术信号模态单一、检测范围有限等不足,提出了一种基于多模态信号的金属材料缺陷无损检测方法.该方法以光声无损检测方法为主体,首先利用有限元方法分析了缺陷对激光能量吸收量和光声表面波传播的影响,提出了基于激光吸收量和光声表面波的缺陷检测方法;然后搭建了多模态信号检测平台,采集了缺陷的光学、光声和超声三种模态的信号,检测出了裂纹的宽度和分布信息,以及深度和在内部的延伸状况.研究结果表明,本文提出的基于金属材料多模态信号的无损检测方法能够准确、全面地检测出金属材料的杂质和裂纹的尺寸信息,弥补了现有无损检测方法在检测范围上的不足,为缺陷定量检测和全面诊断提供了一个新的思路.     

Detecting Doppler ultrasound embolic signals using the wavelet-based feature extraction algorithm

The non-invasive detection of circulating emboli with the Doppler ultrasound technique is of active significance in clinical applications. In order to eliminate drawbacks of artifacts brought by the movement of probes or patients and detect emboli accurately, relevant feature parameters are extracted from two angles of the wavelet transform of Doppler signals. The singularity of the signal waveform is analyzed based on its wavelet scalogram; then transverse and longitudinal parameters are extracted to represent the scalogram characteristics. A novel method is proposed based on the adaptive wavelet packet basis, from which several parameters such as the energy, the scale, etc. are extracted to represent the optimized signal approximation features. With all feature parameters in two aspects, a classification system is established for Doppler Ultrasound embolic signals by solving the generalized Fisher discriminant plane. From experiments on 300 simulated and 298 clinical Doppler ultrasound signals of cerebral arteries, it is shown that the proposed system can achieve the emboli detection rates of 99.0% and 98.5% for the training set and the testing set respectively. Therefore this method makes an improve- ment of emboli detection compared to traditional in the automatic detection of clinical emboli. methods and has the possibility to be applied     

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.