Corrosion is one of the most common types of bridge cable damage, which can further develop into cable breakage. In this work, a novel corrosion degradation assessment method based on multiply scattered guided waves is proposed for directing the management and maintenance of bridge cables. The results reveal that simulated corrosion surfaces based on fractal theory can accurately describe steel wire surfaces with different degrees of corrosion. As the corrosion degree increases, multiple scattering echoes form multiple energy gathering points in the time–frequency domain, and the time–frequency energy of the signal gradually transfers from an end echo to multiple scattering echoes. With an increase of the total mass-loss rate, our proposed evaluation indicator rises monotonously. We show that the evaluation indicator, constructed by multiple scattering echoes, can effectively characterize the corrosion degree and is sensitive to identifying early signs of corrosion. Compared with other indicators, such as the phase velocity, the group velocity and the attenuation rate, the proposed indicators show the superior ability to characterize corrosion degradation. High-frequency guided waves have high resolution and sensitivity for characterizing degradation caused by corrosion. However, the optimal detection frequency still needs to be determined based on the attenuation of guided waves, because high-frequency guided waves attenuate more rapidly than lower-frequency waves. The interaction between wires in bridge cables has little effect on corrosion degradation assessment. With an increase of the technical grade of bridge cables, the amount of corrosion and the evaluation indicator rise gradually. The variation law of the measured evaluation indicator as a function of corrosion degree is in good agreement with our finite element analysis results, and the corrosion degradation assessment results of bridge cables using guided waves agrees well with our results based on visual inspection. Our study indicates that the corrosion degradation assessment of wires inside steel cables can be realized without damaging the outer sheath when using our assessment indicator based on the multiply scattered guided waves.

Fluid-saturated porous media plays an increasingly important role in emerging fields such as lithium batteries and artificial bones. Accurately solving the governing equations of guided wave is the key to the successful application of ultrasonic guided wave nondestructive testing technology in fluid-saturated porous media. This paper derives the Lamb wave equation in layered fluid-saturated porous materials based on Biot theory and proposes the spectral method suitable for solving complex wave equations. The spectral method reconstructs the fundamental wave equations in the form of a matrix eigenvalue problem using spectral differentiation matrices. It introduces boundary conditions by replacing corresponding rows in the wave equation matrix with stress or displacement in matrix form. For complex differential equations, such as the governing equations of guided waves in porous media, the spectral method has the significant advantages of faster computation speed, less root loss, and easier encoding process. The spectral method is used to calculate the acoustic field characteristics under different boundary conditions and environments of the layer fluid-saturated porous media. Results show that the surface treatment details and environment of fluid-saturated porous materials play an important role in the propagation of guided waves.

Automated pavement crack detection is of great significance to the efficiency of road maintenance. Benefit from the development of convolutional neural networks (CNNs), automatic crack detection has been gradually developed. While the convolutional neural network improves the accuracy of crack detection, the calculation amount and parameter amount of the model are greatly enhanced. This limits the application of crack detection methods on some mobile devices. In order to solve this problem, We propose a lightweight network which uses a lightweight feature extraction module combined with an attention mechanism to extract features from crack images. We use the multi-scale feature fusion module to achieve the fusion of different scale crack features. Through these modules, We built a network with nearly 0.94M parameters and only 6G FLOPs while achieving comparable crack detection performance. Extensive experiments on DeepCrack dataset show that the proposed network is superior to other comparison networks.

Pipeline corrosion quantification plays a vital role in guaranteeing the safety of critical industrial structures and thus significant work has been carried out to address such an issue. Although quantitative imaging is crucial for non-destructive testing, research in guided wave pipeline testing has primarily centered on qualitative approaches. Here, we propose a deep neural network built upon physical model to reconstruct pipe wall thickness from ultrasonic guided wave (UGW) signals. The workflow of reconstruction contains three layers, where each layer consists of a fixed forward network and a residual inversion network. The forward model is represented by an agent convolutional neural network which would be embedded into the entire inversion network. The residuals between data from the forward model and real signals are then mapped into velocity profile differences through sub-inversion network. Numerical experiments were conducted to verify the inversion performance of the deep neural network using thickness maps obtained from guided wave frequency domain information. Results show that inversion images are capable to reveal the positions, shapes, and depths of corrosion with high resolution and precision, yielding an average inversion of 87.37% in the test set. In addition, by utilizing the periodicity of the pipeline, the inversion accuracy of eight pairs of transducers were improved from 67.7% to 89.43% with high-order helical guided wave. Compared with traditional high-precision inversion methods such as full waveform inversion, the proposed method achieved approximately 300 times faster inversion speed at the cost of some accuracy. The research demonstrates that real-time quantitative imaging of defects on pipes can be achieved accurately by physics embedded network. Furthermore, an experimental verification of the method was carried out through UGW pipeline testing, demonstrating its feasibility. The mean squared error of wall thickness reconstruction was 0.0070, achieving a high level of precision.

The prediction of the initial stress in composites is essential for the non-destructive testing (NDT) and structural health monitoring (SHM) of carbon fibre reinforced polymer (CFRP). This paper examines the potential of Lamb waves in the inverse of initial stress by calculating the influence of initial stress on the dispersion characteristics of Lamb waves propagating in multilayered CFRP laminates. By introducing the mechanics of incremental deformation into the linear three-dimensional elasticity theory, the Legendre orthogonal polynomial expansion (LOPE) method is used to mathematically model the Lamb wave propagating in multilayered CFRP laminates subjected to horizontal and vertical homogeneous initial stresses. Then, a three-hidden-layers Feed Forward Deep Neural Network (DNN) with Back Propagation (BP) algorithm is constructed to invert the magnitude and di-rection of the initial stresses. The input features are the phase velocities of fundamental Lamb wave A0 mode at five different frequencies. Both training and testing samples are obtained by LOPE forward calculation. An ablation experiment is presented to compare the two different activation functions. Finally, the accuracy of the inverse is verified by comparing with the available outcomes of LOPE forward calculation.

Guided wave testing is now a widely accepted method for detection of structural damage in many different types of components, from pipelines to pressure vessels to tanks. Torsional wave modes (T modes) in pipes and shear horizontal (SH) mode guided waves in plates are good candidates for finding areas with generalized corrosion, due to the absence of fluid coupling effects and their lack of dispersion. However, from our field test experience, certain types of defects are difficult to detect with conventional T mode or SH mode guided wave probes. Gradual wall thinning is one such type of defect; another is crack-like defects in or close to welds or penetrations in the pipe. Recently, Southwest Research Institute (SwRI) has developed a new sensor configuration and scanning system that overcomes these limitations. We have recently developed a linear scanning magnetostrictive transducer (MsT) probe system, in which a FeCo strip wound with radio frequency (RF) coils is attached to the structure under test with shear wave couplant, and a moving permanent magnet driven by a motor is used to excite SH guided waves at predefined positions along this strip. The probe is designed to operate over a wide frequency range (20 – 500 kHz) and can be used to generate dispersive shear wave mode (SH1) in addition to the nondispersive SH0 mode. The use of different modes with the sensor at multiple positions allows detection of a range of defect types. In this paper, the performance of linear scanning MsTs is presented, including experimental evaluation of detection of gradual wall thinning patches of different depths and locations on a steel pipe and plate mockups. Another set of experiments evaluates detection of notches in seam welds. Indications from real-time B-scan and SAFT (synthetic aperture focusing technique) processing will be presented.

Storage tanks are ubiquitous in various industries, and corrosion in tank bottoms is a major threat to their normal operation. A variety of nondestructive testing (NDT) methods have been proposed for corrosion detection, of which the ultrasonic guided wave (UGW) technique is widely considered efficient due to the large propagation distance and high sensitivity to defects. To obtain complete mapping of tank bottom defects, UGW tomography is usually preferred; however, the requirement to place transducers at fine angular increments can cause the measurements to be time consuming. In this paper, we report the usage of a newly designed omni-directional magnetostrictive guided wave system for tank bottom corrosion detection. Different from existing omni-directional system designs, which are typically based on sparse array elements, the system proposed here excites guided waves using a single probe in a single predominant direction that covers the area determined by its beam characteristics. The omni-directional coverage is achieved by rotating the probe with a servo motor and acquiring data at predefined angles. In this fashion, the beam directionality is improved due to a larger transducer aperture compared with those based on a sparse array of small elements, and the beam directionality remains the same at every angle. The performance of the proposed omni-directional system for corrosion inspection is evaluated experimentally by introducing drilled holes of different depths and locations in the bottom of a mock-up tank. The system was placed at several locations along the tank bottom chime plate edge (skirt), where triangulation using appropriate angular measurements can be applied to locate corrosion in the tank bottom.

The fiber composite materials fabricated by stacking the lamina structure obtain increasing attention in the industry, and at the same time, damage analysis is essential for safe operation. A proposed way to detect defects such as debonding and delamination is to induce circumferential guided waves. Accurate determination of the dispersion characteristics and wave fields is a prerequisite for using acoustic guided waves. The aim of this work is to provide such an accurate dispersion results in an arbitrarily thick cylindrically orthotropic homogeneous cylindrical shell of uniform thickness. Compared with the traditional SAFE method, the proposed method, which combines the two numerical methods, namely Floquet Boundary Condition (Floquet BC) Method and Sweeping Frequency Finite Element Modeling (SFFEM) method has obvious advantages. Through Floquet BC method, a theoretical result can be easily obtained without tedious code writing. Then SFFEM helps to verify the theoretical result in an experimental way. Besides, the slight gap between the theoretical and experimental results works as a basis for calibrating the elastic constants of composite materials provided in the manual. Simulations in COMSOL Multiphysics FE software supported by both methods and experiments by the latter have been carried out in this work. The calculated phase velocity dispersion curves demonstrated the efficiency of those two methods. The developed method can be adapted to other complex pipeline structures to extract circumferential guided wave dispersion characteristics by both simulation and experimental measurements.

Acoustoelasticity or the change in elastic wave speeds with stress is promising for prestress measurements in waveguides. The theory of guided wave propagation in initially isotropic materials with arbitrary cross sections and under homogeneous biaxial stresses is developed using Semi-Analytical Finite Element (SAFE) modeling in this article. Based on the anisotropic effect induced by the applied biaxial load, an inversion method for biaxial force was developed. The acoustoelastic response for a particular mode and frequency is described by only two constants, which can be determined from known uniaxial loading experiments. The magnitude and direction of the biaxial force can be obtained by further coefficient fitting. Stress inversion can be obtained without considering the shape of the cross section and applies to multiple guided wave modes. The inversion has been verified by the results of SAFE and 3D Sweeping Frequency Finite Element Modeling (SFFEM) method, and the Mean Absolute Errors of stresses obtained by different methods are all within 1%. The 3D SFFEM was combined with the Matrix Pencil Method using the time domain information to extract the dispersion curve. Unlike previous finite element modeling, here the inheritance of the solution between the two solvers was set instead of approximating static load conditions by shortening the guided wave travel time. It guarantees the steady state of the force in the time-variant study, ensuring the high precision required for the study of the acoustoelastic effect.

The nondestructive evaluation of stress using guided waves through the acoustoelastic effect has significant importance for the safety of the structure. In this paper, a Semi-Analytical Finite Element (SAFE) method is used to develop the acoustoelastic theory of guided waves propagating in plate-like structures with arbitrarily shaped cross-sections. Based on the anisotropy of the material induced by the axial force, a method for in-situ detection of biaxial stress through multi-angle dispersion change was developed. The inversion algorithm was validated by data of the SAFE method and the Sweeping Frequency Finite Element Modeling (SFFEM) method, respectively. The inversion results of S0 mode under this method are mainly studied, which can achieve accurate stress in-situ detection of the plate-like structure, and the Root Mean Square Error (RMS) can reach below 1%.

As an established biometric authentication approach, fingerprint scanning has received considerable attention due to its high accuracy and reliability. In this article, the fingerprint reconstruction at any position is achieved in large physical domains, which monitors wavefield variations of plate-like structures within arrays through the ultrasonic guided wave. Accurate reconstruction and quantitative characterization of fingerprints are obtained using fast inversion tomography (FIT) based on the deep learning convolutional neural network (DLCNN). Parametric optimization is conducted to reveal submillimeter fingerprint minutiae, and a specific DLCNN model is proposed for the artifact removal in FIT reconstructions. The results prove that the FIT based on DLCNN restoration can significantly improve the imaging quality in terms of increased resolution, reduced reconstruction errors, and higher fingerprint matching confidence. The reconstruction also allows an exponential improvement in computational efficiency as a result of much-reduced sensor numbers. Several factors affecting the performance of the proposed reconstruction method are discussed at the end.

Shear horizontal (SH) guided waves are being widely considered as a promising tool for locating wall thinning corrosion in pipelike structures. One established approach to excite such waves in pipes is through the magnetostrictive transducers (MsT), which is an electromagnetic-based guided wave transducer that offers unique advantages over other transducer types. A common practice for fast screening of defects is using an automated probe positioning system. In this paper, we report the usage of a newly designed linear scanning MsT, where an iron cobalt (FeCo) strip of a predefined length wound with radio frequency (RF) coils is attached to the testing structure using shear wave couplants and a moving permanent magnet driven by a stepper motor is used to excite SH guided waves at predefined positions. In this fashion, manual manipulation of probe is minimized which significantly increases testing speed. The performance of the linear scanning MsT at corrosion inspection is evaluated experimentally by introducing “V” shaped gradual wall thinning patches of different depths and locations on a 406 mm outer diameter (OD) steel pipe with 10 mm wall thickness. The reflection and transmission amplitudes of SH modes, as well as indications from B-scan and synthetic aperture focusing technique (SAFT) images, are extracted for corrosion detection and quantification. Numerical modeling is also conducted to facilitate the understanding of SH waves interaction with defects.

针对分数阶傅里叶变换无法对线性调频(L F M)脉冲信号实现脉冲宽度和带宽估计问题,提出了基于迭代搜索的L FM脉冲信号参数估计方法.该方法首先依据分数阶傅里叶变换求取的调频斜率,按chirp相乘思想对原始数据进行处理,得到一组新数据;然后对新数据从起始点到终止点迭代处理,得到一组能量与频率和时间有关的二维数据;最后对二维数据进行差分处理,得到一组能量变化关系图,实现对L FM脉冲信号脉冲宽度估计,并依据事先估计出的调频斜率,实现对L FM脉冲信号带宽估计.数值仿真验证结果表明,在分数阶傅里叶变换无法获取处理数据中L FM脉冲信号脉冲宽度和带宽时,该方法可对L FM脉冲信号宽度和带宽等参数实现有效估计,并对起始频率实现了校正.

Based on the linear three-dimensional elastic theory and the mechanics theory of incremental deformation, using the Legendre orthogonal polynomial expansion (LOPE) method, the wave equations of Lamb wave propagation in the non-principal symmetry axes direction of anisotropic composite lamina was firstly derived when the initial stresses were applied in the horizontal and vertical directions. Subsequently, the coupled equations of Lamb wave propagation were numerically solved. In order to verify the accuracy of the process, the dispersion curves of phase velocity in an isotropic aluminum plate and anisotropic composite lamina without loaded initial stresses were beforehand calculated by LOPE method to compare with those obtained from the commercial software of DISPERSE® and available data. Then, a series of cases referring to unidirectional fiber reinforced composite lamina were further implemented to study the effects of horizontal initial stress and vertical initial stress on the fundamental Lamb wave modes. The results show that the phase velocity of the A0 mode is more sensitive to the implementation of initial stresses compared with the S0 mode and SH0 mode. Finally, the effect of fiber orientation was illustrated in details.

为提高桥梁缆索损伤声发射监测的精度和可靠度,将小波变换的用于导波模态分析,提出了基于导波模态特征的高强钢丝腐蚀评估方法.通过有限元方法模拟多模态导波在索内单根高强钢丝中的传播过程,采用小波变换对节点振动加速度信号进行模态分析,并与柱波导理论时频曲线对比.通过在不同腐蚀程度高强钢丝上进行导波传播实验,分析了腐蚀损伤对导波模态特征的影响,并根据导波模态的频率分布特征构建评估指标,进行腐蚀评估.结果表明:有限元节点不同方向振动所包含的模态存在差异,数值计算结果与理论值吻合较好.高阶纵向导波频率更高,能量衰减更快,波形在反射过程中迅速消失.随腐蚀程度增加,高频高阶导波衰减严重,导波模态逐渐向低频低阶转移.基于导波模态特征的评估指标随腐蚀程度的增加单调上升,腐蚀率在6.0％以内的轻微腐蚀损伤均可被有效识别.可见小波变换能够有效提取导波模态特征,基于导波模态特征构建评估指标可有效识别高强钢丝腐蚀损伤.

潜艇装备使用、发展周期较长,在此期间潜艇装备技术及作战使用需求可能发生变化.美国是核潜艇装备技术强国,曾多次对现役核潜艇改换装,及时有效的满足了潜艇新装备技术验证、潜艇使用功能变化等需求.本文对美国潜艇改换装发展情况及相关考虑因素进行研究,为相关单位提供参考.

为了研究外加磁场对PPT性能的影响,建立了PPT带外加磁场的机电模型,并用三种不同放电能量水平的PPT验证了该模型的可靠性.利用该模型研究了外加磁场强度、模式、位置以及长度对PPT性能的影响.结果表明,对于尾部馈送型PPT,当外加磁场增加时,PPT性能先增加后减小,存在最优的外加磁场强度.对于LES-6 PPT和LES-8/9 PPT,外加磁场从极板的最左端开始施加效果最好;当外加磁场强度分别为0.25T,0.50T,0.75T,1.00T时,这两种PPT对应的最适合施加磁场长度分别是1.3mm,1.8mm,2.1mm,2.3mm和1.6mm,2.8mm,3.4mm,3.7mm.对于TMU PPT,外加磁场从极板的最右端开始施加效果最好,但是施加磁场长度应该根据具体实验结果并结合仿真计算来决定.

通过在不同腐蚀程度钢绞线上进行超声导波试验,构建以导波的小波包能量谱作为特征向量的腐蚀指标.结果表明:随腐蚀程度增加,实测导波的小波包能量谱出现明显变化,基于小波包能量谱的腐蚀指标值随腐蚀程度增加线性增长;采用钢绞线侧面激励导波进行腐蚀状态识别,其敏感性优于钢绞线端面激励,端面激励敏感性系数K值仅为侧面激励的51.35％,确定系数降低了6.4％;腐蚀指标抗噪性能良好,当信噪比达到10 dB时,P1测点敏感性系数K值仅下降了5.97％,信噪比达到0 dB的强噪声环境下,K值仍达到0.51,较未加入噪声时相比,降幅为23.88％;腐蚀指标受导波传播距离影响,当距离增加时,腐蚀指标的敏感性略有降低,且在低腐蚀状态下波动较大.