基于小波分析的超声导波管道裂纹检测方法研究

利用LS-DYNA970动态有限元分析软件对考虑横应变下的管道纵向超声导波损伤检测进行了数值模拟,分析了不同的激发频率对管道中导波频散现象的影响.选择适当的尺度和小波基函数,利用小波变换实现了对微弱且无法直接观测的缺陷检测信号的识别.同时通过时频分析研究了噪声信号在检测信号中的分布规律,并运用小波包分解和重构算法将信噪分离,实现高噪条件下管道微缺陷的损伤识别.     

噪声对管道超声纵向导波损伤检测的影响

在管道超声导波检测技术的基础上,对不同程度纵向缺陷的损伤检测进行了数值模拟.利用缺陷反射信号对损伤的定位,通过改变管壁局部刚度,研究了缺陷反射信号强度变化的相关规律以及超声导波对缺陷的敏感度,分析了噪声信号对损伤识别的影响,并利用小波的分解和重构算法将信噪分离.结果表明不同程度的信噪比会对超声导波检测法造成一定的影响,通过低通滤噪的方法对信号进行信噪分离,可实现高噪条件下的损伤检测.     

基于LabVIEW的便携式锚杆长度检测系统开发

基于LabVIEW软件平台开发了便携式锚杆长度检测系统。该系统包括一个带有信号激励接收功能的便携式工控机、探头和上层控制软件。首先通过软件设置硬件参数,TB-1000信号激励接收卡在探头上激励出超声导波信号并采集反射回波,经模拟滤波器处理后进入NIPCI-5102数据采集卡,将模拟信号转换为数字信号输入PC计算机。本系统软件可对激励接收卡设置激励信号参数,并具有对采集到的信号进行频域变换、希尔伯特变换、小波降噪、快速傅利叶变换(STFT)、扫频成像等处理功能。通过实验,证明了该软件系统具有较高的实时性和可操作性,较好地实现了锚杆长度的快速检测,扩大了超声导波在此领域的应用。     

铝板中激光Lamb波信号的模态分析与缺陷检测研究

激光激励的Lamb波信号具有较宽的频带,且包含多个模态信息。本文采用二维傅里叶变换和时频分析等信号分析技术用于检测信号中的模态成分及缺陷信息识别。首先,对200组激光Lamb波信号进行二维傅里叶变换,得到信号的频率-波数图,可识别出激光Lamb波信号中的低阶A0、S0和高阶模态,并且A0模态能量高,可用于缺陷检测。随后对有、无缺陷状态下Lamb波信号进行连续小波变换,从时频图中识别出缺陷信号的频率成分,进一步提取特定频率下的小波系数幅值信号,实现了缺陷信息的识别。结果表明,二维傅里叶变换能较好地识别激光Lamb波的模态成分,而提取出的连续小波变换系数图,能准确实现缺陷定位。     

有限单元法在超声导波检测技术中的应用

超声导波检测技术具有对波导结构中的缺陷进行远距离无损检测的能力,多年来一直是无损检测领域关注的热点之一.有限单元法具有对各种复杂动力学问题进行计算的能力,已成为超声导波检测技术研究的重要工具.本文结合超声导波检测技术研究领域中的热点问题,对相关的有限单元法进行了简要综述.介绍了有限单元法的发展及其在多物理场耦合机制下导波的激励与接收、线弹性和黏弹性结构中导波的传播特性、非线性超声导波等多个方面的应用研究情况. 最后,基于超声导波检测技术研究趋势展望了相关有限单元法的未来研究重点和发展方向.      

一种基于深度学习的超声导波缺陷重构方法

超声导波检测因其传播效率高、耗能少等优势成为了无损检测领域的重要研究方向。目前已有的利用超声导波进行结构缺陷探测和定量化重构的方法主要由相关的导波散射理论推导得出。然而,由于导波散射问题本身的高复杂性,使得在推导上述理论方法时引入一些近似假设,降低了重构结果的质量。另外,有些方法通过优化迭代的方式提高重构精度,又会增加检测的时间成本。有鉴于此,本文探索了一种将卷积神经网络与导波散射理论模型以局部融合的方式实现缺陷定量化重构的新方法。应用样本数据训练后的神经网络实现缺陷定量化重构,弥补缺陷重构过程中的理论模型误差,同时去除在实际检测过程中所存在的环境噪声。本文以利用SH导波重构平板中的减薄缺陷为研究对象,通过数值模拟验证了该方法在缺陷重构时具有高效率和高精度的特点,特别是对矩形缺陷的重构,新方法的结果精度比波数空间域变换法的精度提高了近200%。     

基于小波多分辨滤波特性的结构损伤识别

小波分析作为一个很好的损伤识别工具,可以看作是传统傅立叶变换的扩展,小波变换采取了可调整的时频窗口,因此小波变换的优势是它具有了“可变焦”性能对局部信号进行多尺度的刻画。小波基的伸缩和平移系列,使小波变换可看作是一组带通滤波器。本文全面分析了小波变换多尺度带通滤波器特性以及在结构在线损伤识别中的应用。结构损伤的出现体现在结构物理参数的改变,相对应的动力响应信号将会产生局部时变特性。利用小波分析的多尺度带通滤波器在不同尺度下对结构振动信号作滤波分析。通过观察不同带宽内振动信号的时频变化来判断结构损伤的存在。     

超声柱面导波技术及其应用研究进展

综述无损检测中的超声柱面导波技术及其应用研究进展。给出 导波的频散及多模式特征,着重评述超声导波的模式和频率选择、导 波的激励和接收方法、导波与缺陷的相互作用、信号处理与特征提取 以及导波技术在无损检测中的应用前景。     

基于小波变换的撞击流压力波动信号分析

通过小波变换对信号进行多尺度细化分析,解决了傅里叶变换不能解决的许多问题．从分析噪声和信号本身奇异点的区别入手,利用小波在时频两域突出信号局部特征能力的重要性质,即噪声信号小波变换的极大值随尺度的加大而显著减少的特点,对撞击流反应器撞击区压力波动信号进行处理分析．分析结果表明:小波变换可以较好地去除信号中的噪声;通过消噪后信号的功率谱可知,撞击流压力波动信号具有自相似的分形特征,与前期的实验研究相吻合．     

基于模态振型和平稳小波变换的悬臂梁微小缺陷识别研究

结构损伤识别方法有很多种,通常结构振动模态振型对缺陷的损伤识别较为敏感,振型体现结构的固有属性及结构局部的特征,可以用于检测缺陷的存在及其位置。但是缺陷比较微小时,仅仅通过振型难以进行损伤识别。因此本文通过对振型进行平稳小波变换处理来检测悬臂梁的微小缺陷。通过有限元模态分析获得含不同缺陷深度、不同缺陷宽度、不同缺陷位置的悬臂梁模型的振型并利用平稳小波变换进行分析处理。结果表明:该方法可以准确判断缺陷的存在及其位置,并且平稳小波细节系数突变峰值随着缺陷深度增大而增大,随着缺陷宽度增大而增大;另外,该方法受振型节点影响,在工程实际应用时应综合前几阶次振型进行缺陷识别。     

Noise processing of flaw reconstruction by wavelet transform in ultrasonic guided SH waves

The ultrasonic guided wave technique is a potential and useful tool for nondestructive testing. The scattered or reflected wave from a flaw can give its qualitative or even quantitative information such as location, size and severity. However, in experiments and in situ tests, the noises always exist together with signals. In this paper, we propose a wavelet-transform based noise processing approach for reflected wave signals to the quantitative reconstruction of surface flaws on a plate using guided SH waves. We suggest two different denoising methods based on wavelet transform (WT) in time and wavenumber domains respectively. Numerical results show that wavenumber-domain WT operation gives a better denoising effect than direct time-domain WT denoising. Using the former, one can successfully perform the inverse reconstruction of flaw by reflected signals with signal noise ratio as high as ?5 dB. This research can act as a theoretical reference for practical applications of ultrasonic guided SH waves in quantitative nondestructive evaluation.     

基于细观混凝土模型的时间逆转损伤成像方法

提出了一种针对混凝土结构损伤检测的时间逆转损伤成像方法.以检测混凝土结构中与骨料尺寸相近的微小损伤为目的,引入细观混凝土随机骨料模型,该模型将混凝土结构视为由水泥浆基底、骨料及粘接层组成的三相复合材料,基于Monte Carlo随机样本原理并结合真实试件的骨料级配曲线建立.在数值模拟分析中,将生成含损伤的细观模型导入有限元分析软件进行超声波场模拟,同时采用自适应性强的时间逆转模型(time reversed model,TRM)进行损伤定位.TRM分为正向检测和逆时成像两个部分:正向检测过程得到包含损伤的一系列散射回波信号,从数值角度进行时间反演并作为逆时过程的输入信号;逆时成像过程选用等效弹性参数模型,几何尺寸与随机骨料模型相同,时反信号在相应几何位置同时加载形成时反波场,时反波场在损伤位置会发生干涉叠加从而导致能量峰值的出现,通过确定干涉峰值时刻,并获取该时刻对应原始波场以及小波变换能量场完成成像.与原始数据波场图相比,小波变换处理成像结果消除了杂波干扰,成像结果更加清晰.进一步对等效弹性参数的取值进行讨论,并且在骨料尺寸范围内调整损伤大小,结果显示成像结果匹配度高,对于非均质混凝土结构的损伤检测能很好满足损伤定位需求.由此证明,时间逆转成像方法对于具有复杂结构的混凝土材料的损伤检测具有较好的适用性.     

A novel physics-informed framework for reconstruction of structural defects

The ultrasonic guided wave technology plays a significant role in the field of non-destructive testing as it employs acoustic waves with the advantages of high propagation efficiency and low energy consumption during the inspect process. However, the theoretical solutions to guided wave scattering problems with assumptions such as the Born approximation have led to the poor quality of the reconstructed results. Besides, the scattering signals collected from industry sectors are often noised and nonstationary. To address these issues, a novel physics-informed framework (PIF) for the quantitative reconstruction of defects by means of the integration of the data-driven method with the guided wave scattering analysis is proposed in this paper. Based on the geometrical information of defects and initial results obtained by the PIF-based analysis of defect reconstructions, a deep-learning neural network model is built to reveal the physical relationship between the defects and the noisy detection signals. This learning model is then adopted to assess and characterize the defect profiles in structures, improve the accuracy of the analytical model, and eliminate the impact of the noise pollution in the process of inspection. To demonstrate the advantages of the developed PIF for the complex defect reconstructions with the capability of denoising, several numerical examples are carried out. The results show that the PIF has greater accuracy for the reconstruction of defects in the structures than the analytical method, and provides a valuable insight into the development of artificial intelligence (AI)-assisted inspection systems with high accuracy and efficiency in the fields of structural integrity and condition monitoring.     

An analytical approach to reconstruction of axisymmetric defects in pipelines using T(0, 1) guided waves

Torsional guided waves have been widely utilized to inspect the surface corrosion in pipelines due to their simple displacement behaviors and the ability of longrange transmission. Especially, the torsional mode T (0, 1), which is the first order of torsional guided waves, plays the irreplaceable position and role, mainly because of its non-dispersion characteristic property. However, one of the most pressing challenges faced in modern quality inspection is to detect the surface defects in pipelines with a high level of accuracy. Taking into account this situation, a quantitative reconstruction method using the torsional guided wave T (0, 1) is proposed in this paper. The methodology for defect reconstruction consists of three steps. First, the reflection coefficients of the guided wave T (0, 1) scattered by different sizes of axisymmetric defects are calculated using the developed hybrid finite element method (HFEM). Then, applying the boundary integral equation (BIE) and Born approximation, the Fourier transform of the surface defect profile can be analytically derived as the correlative product of reflection coefficients of the torsional guided wave T (0, 1) and the fundamental solution of the intact pipeline in the frequency domain. Finally, reconstruction of defects is precisely performed by the inverse Fourier transform of the product in the frequency domain. Numerical experiments show that the proposed approach is suitable for the detection of surface defects with arbitrary shapes. Meanwhile, the effects of the depth and width of surface defects on the accuracy of defect reconstruction are investigated. It is noted that the reconstructive error is less than 10%, providing that the defect depth is no more than one half of the pipe thickness.     

Wave propagation in multi-wire strands by wavelet-based laser ultrasound

Methods based on guided ultrasonic waves are gaining increasing attention for the non-destructive inspection and condition monitoring of multi-wire strands used in civil structures such as prestressing tendons and cable stays. In this paper we examine the wave propagation problem in seven-wire strands at the level of the individual wires comprising the strand. Through a broad-band, laser ultrasonic setup and a time—frequency wavelet transform processing, longitudinal and flexural waves are characterized in terms of dispersive velocity and frequency-dependent attenuation. These vibrating frequencies propagating with minimal losses are identified as they are suitable for long-range inspection of the strands. In addition, the wave transmission spectra are found to be sensitive to the load level, suggesting the potential for continuous load monitoring in the field.     

基于小波变换的爆破振动信号能量分布特征分析

为了深入研究爆破地震波特性,应用小波变换方法对具有短时非平稳特点的爆破振动信号进行了能量分布特征分析。根据小波变换的时-频特性和分层分解展开关系,将爆破振动时间历史信号用分层重构信号进行扫描,应用这些信号得到了不同频率带上爆破振动的相对能量分布和振动强度的时间变化规律。爆破振动信号实测结果分析表明,基于小波变换的能量分布特征分析可以更准确地给出爆破振动信号的细节信息。研究结果为分析爆破振动结构安全性提供了新的途径。     

Spectral-temporal filtering of NDT data using wavelet transform modulus maxima

In this paper, a method is described for the filtering of non-destructive testing (NDT) signals. By employing wavelet transform modulus maxima filtering in the time–frequency plane, it allows the high frequency components associated with features within the signal to be retained while high frequency components associated with both signal ring down and noise are removed. The method is applied to sonic echo signals associated with the NDT of piled foundations. The method allows for spectral-temporal filtering of the signal and offers a superior partitioning of the signal of interest from both noise and ringdown artefact. The method has applications in the elucidation of other signals.