Ultrasonic defect detection of a petroleum pipeline in a viscoelastic medium

During detection of the defects in the inner wall of a petroleum pipeline via an ultrasonic nondestructive evaluation method, the defect echo and the inner-wall echo often overlap. It is difficult to identify the arriving time of the defect echo. When an ultrasonic wave propagates in a viscoelastic medium, the stress relaxation and creep deformation result in a signal characterized by frequency. The wave speed and attenuation rate are dependent on the frequency; thus, the ultrasonic signal has different shapes along the wave propagation path. Sometimes the wave shape is wry. The empirical mode decomposition technology used for separating the overlapping echo signals has been presented in this paper. With such technology, the original ultrasonic signal can be decomposed and then some intrinsic mode functions (IMFs) and a residue can be obtained. Different IMFs contain different echo signals. Some useful IMFs are selected to reconstruct the ultrasonic signal. The peaks of the reconstructed ultrasonic signal envelope indicate the arriving time of the echo signals. The experimental results show that this method is effective for detection of the defects in the inner wall of a petroleum pipeline. The text was submitted by the authors in English.     

面向超声导波检测的管道腐蚀建模及仿真实现

超声导波技术被大量应用于管道腐蚀的检测与评估。腐蚀是实际管道中的主要缺陷形式,管道腐蚀缺陷形貌多样且复杂,针对管道腐蚀导波检测的很多研究是通过仿真手段开展的。常用的缺陷简化模型不能充分反映实际腐蚀缺陷的复杂程度,有可能造成分析结果的偏差。本文在分析腐蚀特点基础上提出了基于W-M分形函数的腐蚀仿真模型,研究了管道腐蚀缺陷的有限元自动建模仿真方法,并通过分析讨论超声导波检测不同腐蚀缺陷的仿真结果对模型的有效性进行了验证,结果证明基于本文提出的腐蚀模型所得缺陷回波可提供更丰富的缺陷信息,有利于揭示管道腐蚀特征与导波信号之间的量化关系。本文的研究成果可为进一步分析管道腐蚀缺陷的检测评估建立理论基础。     

自适应解卷积方法抑制管道 TOFD 周向扫查盲区

利用超声衍射时差法(TOFD)沿管道外表面实施周向扫查时,受管道曲率和直达纵波脉冲宽度共同影响,在近表面区域形成分层盲区。本文采用自适应解卷积方法,选取与混叠信号主频接近的子带直达纵波信号作为参考信号,进行解卷积与自回归谱外推处理,拓宽有效频带范围,实现时域信号脉冲压缩,并结合周向扫查图像中端点衍射波确定缺陷深度。实验结果表明,对于外壁半径100.0 mm、壁厚30.0 mm,以及外壁半径148.0 mm、壁厚27.0 mm的碳钢管道,在中心频率5 MHz、探头中心距87 mm的检测条件下,自适应解卷积方法能够将管道近表面分层盲区范围减少约60%,且到直达纵波声线距离不小于4.0 mm缺陷的深度定量误差不超过10.6%。同常规频谱分析方法和自回归谱外推方法相比,自适应解卷积方法具有更优的盲区抑制效果,且能够准确定量前者难以检测的缺陷,测量误差不超过5.8%。     

金属材料小缺陷超声反射信号建模及识别

为了能从含噪声金属材料超声检测信号中有效识别出微小缺陷回波,建立了金属材料超声反射信号模型并提出了基于相关系数的微小缺陷回波识别方法。对含微小缺陷金属材料超声脉冲反射信号的成分进行分析,建立了基于散射声场与高斯回波理论的优化超声回波模型。设计了超声缺陷回波位置识别方法。该方法对超声脉冲反射信号去噪后,取探头发射脉冲信号为参考信号;然后与去噪后的信号逐段求解相关系数;最后对该相关系数序列进行阈值化处理,获得缺陷回波在超声回波信号中的位置。将利用上述优化超声回波模型生成的超声反射信号及其频谱与实验获得的金属材料超声反射信号及其频谱进行了对比,结果表明:两者的时频域特征具有一致性。当将阈值设定为相关系数序列最大值的60%时,能够有效从超声背散射信号中识别出金属材料微小缺陷回波。     

A fractal-dimension-based signal-processing technique and its use for nondestructive testing

A fractal-dimension-based signal-processing technique has been extensively applied to various fields, but the use of the method to characterize discrete time-domain ultrasonic signals reflecting defects and any other structural-material inhomogeneities has not been fully investigated. The fractal features of the ultrasonic echoes with fractal dimensions and their implementation in nondestructive testing are investigated. In order to obtain a faithful representation of the fractal dimensions, two improved fractal dimension algorithms are presented: the box-counting method and the R/S (range/standard deviation) method. Their capabilities are evaluated with two kinds of fractal signals: the FBM (fractal Brownian motion) and WM (Weierstrass-Mandelbrot) signals. A new method to guarantee the feasibility of the calculated fractal dimensions is proposed on the basis of the analysis of the results simulated above. Then, the fractal dimensions of ultrasonic signals measured from a pipeline sample and from carbon-steel and aluminum specimens are calculated and statistically analyzed to find the fractal properties of the ultrasonic signals. The experimental results show that ultrasonic signals have the property of scale invariance that the fractal set possessed. The fractal dimension is indicative of the complexity and degree of irregularity of the waveform of an ultrasonic signal. The fractal dimensions of ultrasonic signals from various defects and microstructures are found to possess solid distribution intervals, which can be used to identify the presence of defects and the features of materials. The potential of the technique for testing defects and assessing the microstructure of materials via the use of ultrasonic echoes is revealed. The text was submitted by the authors in English.     

Ultrasound Imaging of Pipeline Crack Based on Composite Transducer Array

Cracks, especially small cracks are difficult to be detected in oil and gas transportation pipelines buried underground or covered with layers of material by using the traditional ultrasonic inspection techniques. Therefore, a new composite ultrasonic transducer array with three acoustic beam incidence modes is developed. The space model of the array is also established to obtain the defect reflection point location. And the crack ultrasound image is thus formed through a series of small cubical elements expanded around the point locations by using the projection of binarization values extracted from the received ultrasonic echo signals. Laboratory experiments are performed on a pipeline sample with different types of cracks to verify the effectiveness and performance of the proposed technique. From the image, the presence of small cracks can be clearly observed, in addition to the sizes and orientations of the cracks. The proposed technique can not only inspect common flaws, but also detect cracks with various orientations, which is helpful for defect evaluation in pipeline testing.     

A hybrid signal pre-processing approach in processing ultrasonic signals with noise

Ultrasonic techniques have the potential to be used to detect sub-surface defects in aluminium castings. However, ultrasonic sensing techniques have not been successfully used to detect sub-surface defects in aluminium die castings with rough surfaces or in the ‘as-cast’ state due to the poor quality of signals. Ultrasonic signal noise caused by rough surfaces and grain size variations of the castings is difficult to eliminate. Hence, there is a need to process noisy ultrasonic signals to identify defects within the rough surface castings. This paper documents an investigation of ultrasonic signal analysis using artificial neural networks and hybrid signal pre-processing approaches for the purpose of detecting defects from noisy ultrasonic signals. In this investigation, ultrasonic signals were obtained from aluminium castings with different levels of surface roughness. The signals were first pre-processed using hybrid signal analysis techniques and then classified using an artificial neural network classifier. The hybrid pre-processing techniques utilised various combinations of fast Fourier transform (FFT), wavelet transform (WT) and principal component analysis. The best signal classification performance was generally achieved with a hybrid WT/FFT signal pre-processing technique.     

厚截面碳纤维复合材料远表面微缺陷超声检测

为了识别厚截面碳纤维复合材料(CFRP)远表面的微缺陷,使用递归分析方法对超声检测信号进行分析。首先在厚截面CFRP材料上打孔以模拟微缺陷,采用水浸超声脉冲反射法对不同大小的模拟缺陷进行检测。然后选取缺陷位置附近信号段,确定嵌入维数m、延迟时间τ、阈值ε等参数,对各信号段进行递归分析,得到递归图及递归定量分析结果。比较无缺陷信号和有缺陷信号的递归图,从宏观上定性确定微缺陷对超声信号的影响;比较无缺陷信号和有缺陷信号的递归定量分析结果,根据每个递归定量参数的物理意义,对缺陷产生的影响作出合理的解释。最后,使用不同中心频率探头进行实验,确定合适的探头参数。分析结果表明,使用7.5MHz高分辨率超声探头时检测效果最好;当嵌入维数为7、延迟时间为2、阈值为2时,递归图中出现异常白色区域、递归点增多且对角线结构变长,同时所选取的递归定量参数随缺陷增大而上升,表明厚截面CFRP远表面超声信号可能存在混沌结构,而微缺陷的存在会改变原有信号结构。所研究内容为实际微缺陷的定量识别及分类打下基础。     

承压管道磁记忆检测缺陷信号量化数值研究

利用Ansys数值模拟软件,对含埋藏缺陷的承压管道进行磁记忆检测有限元模拟,分析磁记忆信号值在不同缺陷参数、不同应力状态下的变化规律.结果表明,磁记忆信号可以对管道埋藏型裂纹及气孔产生的应力集中进行表征,随着缺陷埋深的减小与内压载荷的增加,磁记忆信号变化幅度增大,法向梯度极值增加.通过法向和切向信号协同分析,可以实现对...     

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。     

管道损伤磁梯度共振稀疏分解与BMSR辨识方法

为更好地提取埋地钢质管道地磁环境下由应力集中产生的磁力信号,克服现有磁力检测缺乏有效高灵敏度多元探头阵列和信号处理技术的问题,提出一种磁梯度张量共振稀疏分解结合偏置单稳随机共振(bias monostable stochastic resonance,简称BMSR)的辨识方法对管道损伤进行有效评估。首先,探头布置采用十字张量阵列形式;其次,针对管道缺陷和现场干扰信号特点,用不同品质因子对信号进行共振稀疏分解,剔除掉一部分干扰信号;最后,加入不同时域恢复的随机共振系统结合量子遗传算法进行参数寻优。将该辨识方法用于实际站场管道张量检测信号,比较传统低通滤波、不同随机共振系统结合共振稀疏分解的处理结果,验证了磁梯度张量共振稀疏分解加偏置单稳处理算法在提取管道损伤磁场和表征管道应力集中的有效性。     

Ultrasound Imaging of Pipeline Crack Based on Composite Transducer Array

Cracks, especially small cracks are di cult to be detected in oil and gas transportation pipelines buried underground or covered with layers of material by using the traditional ultrasonic inspection techniques. Therefore, a new com?posite ultrasonic transducer array with three acoustic beam incidence modes is developed. The space model of the array is also established to obtain the defect reflection point location. And the crack ultrasound image is thus formed through a series of small cubical elements expanded around the point locations by using the projection of binariza?tion values extracted from the received ultrasonic echo signals. Laboratory experiments are performed on a pipeline sample with di erent types of cracks to verify the e ectiveness and performance of the proposed technique. From the image, the presence of small cracks can be clearly observed, in addition to the sizes and orientations of the cracks. The proposed technique can not only inspect common flaws, but also detect cracks with various orientations, which is helpful for defect evaluation in pipeline testing.     

基于双磁场管道复合型缺陷应力信号提取方法研究

缺陷和应力共同存在的复合型缺陷是影响管道安全运行的重要因素之一。双磁场管道内检测法可用于复合型缺陷处应力损伤程度判断,但应力信号提取方法亟待解决。本文将J-A理论中的磁力学关系引入磁荷模型中,解析计算了不同应力、外磁场下复合型缺陷磁信号,建立基于双磁场信号比值的复合型缺陷应力信号提取模型,提出比值因子用于缺陷处应力水平的评估,并进行了系统的实验验证。研究结果表明,强磁信号对缺陷处应力大小不敏感,信号主要包括缺陷尺寸信息;弱磁信号对缺陷处应力大小敏感,信号包括缺陷尺寸信息和缺陷处应力信息。提出的比值因子可反映缺陷处应力情况,弱磁场强度较低时,比值因子随缺陷处应力的平均变化率大于9%,随着弱磁场强度的增加,比值因子随应力变化幅度变小。     

管道壁缺陷超声波在役检测的量化分析研究

介绍了管道壁缺陷超声波在役检测原理和技术性能，指出超声波在役检测技术具有直接测量和量化的特点，特别适用于管道壁腐蚀减薄缺陷和其它减薄缺陷的在役检测，对管道壁厚度的检测精度高。介绍了管道壁缺陷的超声波在役检测方法，给出了四种典型的管道壁缺陷型式，指出建立并完善管道壁缺陷超声波在役检测的量化分析模型，对于提高检测精度具有十分重要的意义。在对四种典型管道壁缺陷型分析的基础上，通过对四次反射回波时间和相对幅值的分析识别，建立了管道壁缺陷超声波在役检测的量化分析模型，并给出了计算框图。最后，对四种典型管道壁缺陷型式进行了模拟试验分析。     

基于反褶积与编码激励的长输管道损伤检测

制约应力波导波技术应用于长输管道损伤检测的两个主要原因是信号处理技术和导波激励技术,针对应力波频散效应和外界噪声干扰使得管道损伤检测定位精度低的问题,采用了基于子波估计的反褶积技术处理管道导波测量数据。数值模拟和试验结果均表明,该方法可以有效地抑制导波频散,提高了管道损伤检测的定位精度。针对单一激励能量有限、检测信号的信噪比低等问题,采用了编码激励技术,设计了两种不同的编码激励信号,并将其应用到管道纵向导波传播模型中。数值模拟结果表明,编码激励技术能够有效地提高管道损伤检测的抗干扰能力和分辨率。     

Investigation of deep neural network with drop out for ultrasonic flaw classification in weldments

Ultrasonic signal classification of defects in weldment, in automatic fashion, is an active area of research and many pattern recognition approaches have been developed to classify ultrasonic signals correctly. However, most of the developed algorithms depend on some statistical or signal processing techniques to extract the suitable features for them. In this work, data driven approaches are used to train the neural network for defect classification without extracting any feature from ultrasonic signals. Firstly, the performance of single hidden layer neural network was evaluated as almost all the prior works have applied it for classification then its performance was compared with deep neural network with drop out regularization. The results demonstrate that given deep neural network architecture is more robust and the network can classify defects with high accuracy without extracting any feature from ultrasonic signals.     

经验模态分解在管道缺陷漏磁检测信号处理中的应用研究

针对管道缺陷漏磁检测信号中存在严重噪声干扰的问题，将经验模态分解方法用于漏磁检测信号的噪声分离和有效信号提取，对实际测试的与输油管道材质相同且具有人为模拟缺陷的漏磁信号进行处理，结果表明，该方法可以很好地抑制噪声从而得到清晰的、表征缺陷特征的有用信号，达到与小波变换相同的处理效果，同时克服了小波方法中基函数选择困难的问题。     

Adaptive methods for resolution enhancement of ultrasonic NDT signals: Comparative exploration

Adaptive filters, with their efficiency and simplicity, have been used successfully in various ultrasonic NDT signal processing contexts. Of these, the adaptive deconvolution with the conventional least-mean-squares (LMS) adaptive filter has improved time resolution. However; the convergence speed of LMS is restricted by the eigenvalue spread of the input correlation matrix. This paper explores the potential of other adaptive algorithms, namely, normalized least-mean-squares (NLMS), recursive least squares (RLS) and QR-decomposition-based RLS (QR-RLS) to handle the deconvolution of ultrasonic NDT signals and compare their performances with that of the conventional LMS algorithm. Furthermore, the mean square error (MSE) behavior in the different adaptive filtering algorithms for ultrasonic NDT signals deconvolution is briefly introduced. Experiments results are explained by graphs and discussed based on the performance criteria. The proposed methods enhanced the resolution quality, offering more alternatives for this application according to specific case requirements.     

基于非线性小波收缩的超声信号缺陷识别方法

超声检测回波中的噪声信号是影响焊接缺陷无损检测的主要因素之一。为了更好地抑制回波中的噪声成分,从而有效地识别缺陷信号,在传统非线性小波收缩(Nonlinear wavelet shrinkage,NWS)方法的基础上,提出一种基于子波相关的改进噪声抑制方法。改进方法具备小波分析的多分辨特性,同时兼顾相邻测点回波中缺陷信号间具强相关性的特点。分别利用传统及改进的NWS方法对计算机仿真信号及焊缝超声检测信号进行处理,研究不同母小波及阈值估计方法对信号处理效果的影响。结果表明,改进方法对不同母小波的敏感性小,受不同阈值估计方法的影响小,且对复杂成分的噪声更具适应性。改进方法的噪声信号抑制及缺陷信号恢复效果明显优于传统方法,为缺陷的有效识别及精确量化测量提供可靠的依据。     

Sparsity enhancement for blind deconvolution of ultrasonic signals in nondestructive testing application

The received signal in ultrasonic pulse-echo inspection can be modeled as a convolution between an impulse response and the reflection sequence, which is the impulse characteristic of the inspected object. Deconvolution aims at approximately inverting this process to improve the time resolution so that the overlap between echoes from closely spaced reflectors becomes small. This paper presents a modified minimum entropy blind deconvolution algorithm for deconvolving ultrasonic signals. Enhancement of the resolution is achieved by using the presented method. In addition, the presented approach will, in many cases, lead to a faster computation. A nonlinear function is the key point to the efficiency of the modified blind deconvolution algorithm, which is used to increase the sparsity of the iteration output and to decrease the influence of the added noise by replacing each iteration output by output of the nonlinear function. Simulations showed the efficiency of the modification as compared with minimum entropy deconvolution when deconvolving synthetic ultrasonic signals. Experimental results using real ultrasonic data evaluated further that the exact solution consistently yields good performance. The thickness of a thin steel sample can be calculated by the modified blind deconvolution filter with a reasonable accuracy.