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Diffusion partition consensus is a novel generative AI-based technique for time-series anomaly detection and data imputation in the presence of outliers. To illustrate the method, an implementation with design choices tailored for well-structured time series typical of single probe ultrasonic nondestructive evaluation (NDE) datasets is proposed. The technique relies on cross-talk between a conditional score-based diffusion model, and two well-chosen Savitzky-Golay filters. Testing and evaluation are performed on a series of progressively information rich synthetic datasets, and on real-world ultrasonic NDE datasets taken from a Canada Deuterium Uranium nuclear reactor pressure tube and calibration fixture. The iterative technique is a blend of stochastic and deterministic methods that uses confidence and consensus of target parameter estimates to update several data classifying partitions over the dataset, which in turn allows a new set of estimates and confidence measures to be established. Data classification induces a progressive bias in the training datasets allowing a diffusion model to identify the prevalent distribution. Methods for fault diagnosis support the efficacious inclusion of a human in the loop making the technique suitable for use in safety-critical applications. The main advantages of the technique are that it is unsupervised—in that it does not require labeled datasets or significant data preprocessing, does not rely on out-of-distribution generalization, provides means for fault diagnosis without recourse to ground truth, converges with stability, and naturally includes a human in the loop. The quality of results, the checks and balances provided by the fault diagnosis mechanism, and the opportunity to include a human in the loop, support the case for usage in safety-critical contexts such as NDE at a nuclear power facility.

Damage imaging algorithms are crucial for evaluating the condition of critical structures such as adhesively bonded joints. Particularly during service, baseline-free structural health monitoring (SHM) is essential for robust and real-time evaluation. This article proposes and investigates the impact of the shape of the damage intensity distribution and damage index on the damage imaging of composite lap joints using a baseline-free SHM system. This system comprises a parallel array of piezoelectric transducers attached to both sides of the lap joint for generating and receiving ultrasonic-guided waves. Various features are extracted from the received signals to serve as damage indices, representing the peak amplitude and energy of the signals as well as the time of flight (ToF). Different shapes of damage intensity distribution, including elliptical, diamond, rectangular, and quadrilateral, are considered between pairs of sensors to investigate their effects on the total damage intensity distribution. To evaluate the impact of these parameters, a 2-D correlation coefficient was employed to compare the results obtained from the baseline-free SHM system with the image containing actual defects. The results reveal that the ToF was ineffective in providing high correlation and considering the signal’s energy with quadrilateral shape achieved the highest correlation.

This paper proposes a nondestructive testing method for inspecting an unwanted foreign object underneath the wall of a buried polyethylene (PE) pipe. The measurement system comprised a vector network analyzer and two inspection antennas, enabling the acquisition of clear two-dimensional (2D) images of a foreign object under a buried PE pipe using microwave time-domain responses. In the measured 2D images, waves propagating through the air and along the pipe wall were observed, followed by a reflected wave caused by the foreign object. The size and shape of the foreign objects could be identified using the signal intensity and shape of the reflected waves, and their depth positions could be determined using the delay time of the waves. Furthermore, the use of high-gain antennas enabled the detection of small foreign objects. The substrate dipole and Yagi-Uda antennas fabricated in this study effectively detected mortar blocks with lengths of 30 and 17 mm, respectively. The study findings show that the proposed method can be employed for the inspection of plastic pipes with varying thicknesses and materials and the measurement system can achieve the miniaturization of equipment installed in pipes by incorporating optical fiber links.

In the field of nondestructive evaluation (NDE), we sometimes need an intricate system of multiple actuators and sensors to measure and assess the material condition or structural integrity of a specimen. Complicated systems are especially necessary for more advanced techniques that involve multiple phenomena or modeling in a geometric context. In the research laboratory, we rarely understand the intricacies of the measurement up front, and we need the agility to reconfigure our measurement system as needs evolve. Software is the glue that ties our measurement systems together. The traditional approach of ad hoc software quickly becomes unsustainable in the modern environment. We propose an alternative approach that addresses the need for agility in the modern NDE laboratory: a reconfigurable, modular software architecture that is built from the ground up to accommodate conflicting requirements in the areas of data management, automation, parallelism, geometry and robotics, and version control. We describe a new pair of open-source tools, Dataguzzler-Python and SpatialNDE2, that facilitate instrumentation control, data acquisition, and processing for the NDE laboratory. The tools make up a framework that provides the following: multiplexed automatic and manual control of instrumentation, a versioned database to store the acquired data, parallel acquisition and live high performance/GPU computation, the ability to acquire and store data in geometric context, and the ability to visualize and interact with the acquired data. This article discusses their design, implementation, and initial experiences in using them in the NDE laboratory.

This paper presents a novel nondestructive, contactless moisture content sensor based on the Wheeler Cap measurement technique. The sensor comprises an electrically small antenna enclosed within a compact metallic cap, along with the material under test. The operating frequency of the sensor without material under test is 268.25 megahertz. By measuring the reflection coefficient at the input port of the antenna enclosed in the metallic cap, the moisture content of the material under test can be determined. The proposed sensor is aimed for moisture content measurement of the municipal solid waste, the primary feedstock for refuse-derived fuel utilized in waste-to-energy power plants. The moisture content of material under test can be determined using magnitude or frequency of the minimum reflection coefficient from the measured frequency response. Linear regression models are developed to establish the relationship between moisture content and the measured magnitude or frequency yielding coefficient of determination $R^{2}$ of 0.934 and 0.936, respectively. The sensor exhibits a sensitivity of 0.034 dBm decrease in magnitude and 0.097 MHz decrease in frequency per 1% increase in moisture content. This performance demonstrates the sensor’s potential as a reliable and accurate tool for moisture content measurement in WtE applications.

A study utilizing convolutional neural networks (CNN) has been conducted to detect and classify invisible debonding-type defects in reinforced structures. Training data for these defects is collected from the finite element models of honeycomb sandwich panels and skin-stringer systems, commonly employed reinforcement structures in aerospace applications. The excitation frequency is determined based on the amplitude of the reflected wave from the defect, and the optimal sensor array is selected. The constructed two-dimensional training image, created by vertically stacking the measured responses in the time domain, exhibits high classification performance even with a shallow neural network. The neural network undergoes optimization through adjustment to the kernel parameters and initial learning rate. To assess the general performance of the training model, k-fold cross-validation is employed. The CNN-based non-destructive evaluation algorithm demonstrates high classification performance for debonding defects in honeycomb sandwich panels and skin-stringer structures. Moreover, the suggested algorithm is robust against noise, emphasizing its effectiveness in real-world applications.

The increasing use of glass fiber reinforced polymer (GFRP) laminates in aircraft creates an urgent demand for nondestructive testing to identify defects such as impact damage and delamination in the materials’ near-surface structure. The High-frequency ultrasonic technique is an effective nondestructive testing technique capable of detecting such defects. However, with the increased delamination depth, it is difficult to accurately characterize the delamination defects in GFRP laminates by traditional characterization methods. And the conventional defect depth detection method takes much time and requires professional discrimination, which is easily affected by the level of inspectors and subjective factors. To address the issue of inaccurate feature extraction and low detection efficiency, we propose a new method for delamination depth recognition in the near-surface of GFRP laminates based on the RQA-MKLSVM model by combining the recurrence quantitative analysis (RQA) feature extraction method with the multi-kernel learning SVM technique to improve the capability of detecting delamination defects on GFRP laminates. Additionally, an image-enhanced method based on the nonlinear transformation function and wavelet multiscale product is used to locate the delaminated areas. Results show that the proposed method achieves accurate recognition of different delamination defects within depths ranging from 0.8 mm to 4.0 mm, and the average recognition rate has reached 95.63%. Compared with the training models based on discrete wavelet transform(DWT) and empirical mode decomposition (EMD), the recognition rate of our method is improved by 6.25% and 4.38%, respectively. And Compared with the two single-core models, the recognition accuracy of our method is improved by 8.55% and 5.00%, respectively.

Much research has been conducted to improve the defect-detection rate and detection accuracy of the imaging technology used in terahertz nondestructive testing. Due to the power limit of light sources and noise interference in terahertz equipment, images have low resolution and fuzzy defect edges. Hence, improving the resolution is crucial for detecting defects. We designed an edge detection network structure based on a traditional deep neural network. Besides, we devised a node-fusing strategy to train the network. It demonstrates significant improvement of the resolution of the terahertz defect contour. A quartz fiber composites with embedded defects was tested with our network. The results showed that the proposed super-resolution reconstruction algorithm improves resolution, particularly on the edges of defect contours.

In this article, we present a smooth low-rank tensor completion (SLRTC) based reconstruction algorithm to recover raw thermal image sequences from highly randomly undersampled or small numbers of available thermographic data. The presented algorithm is also fused with a temporal interpolation algorithm (to produce the SLRTCTI algorithm) to complement high frame-rate thermal image sequences with notably enhanced thermal contrast. Pulsed and lock-in thermographic data are obtained for subsurface defects in carbon fiber reinforced polymer (CFRP) to demonstrate the performance of the algorithm, and it is shown that the algorithm is data-driven and is independent of the excitation form. The algorithm enables the maximum available frame rates of thermal infrared cameras to be increased by at least ten times. To further enhance the visibility of the CFRP defects in the results reconstructed using the SLRTC algorithm, fast randomized sparse principal component thermography (FRSPCT) and 2-D principal component thermography (TDPCT) are also proposed. Results show that TDPCT remarkably enhances the thermal contrast between the defective and intact regions under highly undersampled data conditions. In addition, FRSPCT provides more easily interpretable detection results and highlights the hidden details of irregularly-shaped abnormal defects. [ABSTRACT FROM AUTHOR]

To solve the problem of the low efficiency of traditional lettuce freshness classification methods and sample damage, we proposed an automatic lettuce freshness classification method based on improved deep residuals convolutional neural network (Im-ResNet). We built an image acquisition system to obtain the freshness classification dataset of lettuce leaves. For improving the classification accuracy, we developed an image acquisition system for curating the freshness of lettuce leaves. Then, we proposed a novel method that was derived from the existing ResNet-50 (which uses ReLU activation function) known as Improved Residual Networks (Im-ResNet): the new method factored extra convolutional layer, pooling layer, fully-connected layers, and a random ReLU (RReLU) activation function. We also performed the corresponding experiments using the Im-ResNet network compared with four network architectures (AlexNet, GoogleNet, VGG16 and ResNet50). The experimental results showed that the proposed network had more significant advantages in the recognition accuracy and loss value of lettuce freshness compared with the traditional deep networks. The recognition accuracy of the validation set of the proposed model can reach to 95.60%. Different from the physical and chemical methods, our scheme can automatically and non-destructively classify the freshness of lettuce.

This work presents in detail a new method for the optimization of rectangular spread spectrum excitation signals for its use in simple and low-cost ultrasonic pulsers, but that could be extended to other applications based on spread spectrum signals. Starting from rectangular linear frequency modulated (RLFM) chirps, it uses the transfer function of the transmitted signal and received echoes from reference specimens to iterate through a recursive algorithm to obtain arbitrary position and width pulse (APWP) signals with the desired bandwidth, maximizing the energy and the flatness of the spectrum, and enhancing the resolution and dynamic range of conventional chirp excitation signals. This optimization procedure can be repeated for any transducer and material, so that it achieves the best performance in each experimental environment. Such characteristics are ideal for Time-of-Flight estimation, imaging, and applications in which spectral regularity is needed, such as the Split Spectrum Processing (SSP) algorithm, which is used as example to test the performance of the proposed excitation signals. It also allows to specify and change the bandwidth reducing the need to change the transducers. The method is tested with different transducers (2 and 5 MHz focused transducers) and complex composite materials (aluminum-carbon aviation composite and high porosity GFRP composite) in immersion setup for imaging applications using SSP algorithm.

The traditional manual periodic screening method of grading electrode sediments is prone to cause the equipment failure of high voltage direct current converter valves. Therefore, we propose to use ultrasonic time-domain reflection method to detect the sediments. However, the ultrasonic echo signals are characterized by nonlinearity and nonsmoothness, which makes it very difficult to extract effective features for sediment detection. To address this issue, we propose an intelligent detection method based on multiscale hybrid entropy characteristics in the time-frequency domain. First, a multiscale decomposition of the signal is performed. Second, the weighted form factor index is proposed to select the noise modes. Moreover, we propose to calculate the hybrid entropy in the time-frequency domain of each mode as the characteristic input bidirectional long and short-term memory network model, and verified that feature enhancement can be achieved by noise modes noise reduction. Finally, the experimental validation shows that the proposed method can achieve nondestructive testing and intelligent identification of graded electrode sediment with a correct identification rate of 94.25%.

Due to its high resolution and deep penetration depth, microwave ultrawideband radar is a promising tool for the non-destructive testing (NDT) of transportation infrastructure. Microwave radiation can also be used to reconstruct the dielectric properties of objects and therefore can be used to detect an air cavity or metallic rust in concrete. We used contrast source inversion (CSI) as one of the most promising inverse scattering schemes. To resolve the observation domain limitations of NDT, radar imaging method, also known as range points migration (RPM) method, was first incorporated into the inverse scattering algorithm based on CSI as a region of interest (ROI) estimator, which substantially improves the accuracy of complex permittivity reconstruction. In addition, the ROI optimizing scheme based on the CSI cost function is used to enhance ROI accuracy. The effectiveness of the proposed methods is validated via finite-difference time-domain (FDTD)-based numerical simulation, which assumes typical NDT model. [ABSTRACT FROM AUTHOR]

This article presents numerical and experimental investigations of the structural control of multilayer high temperature superconducting (HTS) tapes, using eddy current thermography at room temperature. A rotating magnet wheel is used as inductor to avoid the thermal disturbance of current supplied inductors. A 3-D magnetothermal modeling approach based on an integro-differential formulation in terms of the electric vector potential is developed for eddy current computation in the HTS tapes; associated to a thermal modeling using the finite difference method. The source magnetic field is calculated using the magnetic surface charge model. The simulation results are confirmed by the measurements, both highlighting the potential of such control to detect structural defects, such as delamination. [ABSTRACT FROM AUTHOR]

Accurate acquisition of the complex refractive index of materials is of great significance to understand their physical properties. Based on a single time-domain transmission model in $Y$ -band, here, we develop a free-space nondestructive testing method combined with parameter extraction of flat plate materials. This method overcomes the limitation of the narrow frequency band of S-parameters of vector network analyzer (VNA), truncates multiple echo interferences in time-domain signals inside sample after the inverse fast Fourier transform (IFFT), and eliminates the crimping effect of the calculation results. In order to ensure the good signal integrity of the model, an empirical formula is proposed to calculate the minimum thickness of the sample corresponding to unit bandwidth. An $8f$ quasi-optical system is designed and optimized to provide a measuring condition of terahertz (THz) plane wave with high energy density. Compared with the values calculated by the conventional methods, the refractive index deviation is less than 0.004 (0.2%), which verifies the effectiveness and applicability of our proposed method. The adaptability of different processes to the corresponding physical transmission model is further analyzed, and the reasons for the calculation deviation are explained. This work reduces the complexity and difficulty of the material measurement in the $Y$ -band, as well as the requirements of system calibration. [ABSTRACT FROM AUTHOR]

The advanced non-destructive tests (NDT) have become increasingly popular in the structural performance evaluation of transportation infrastructures including pavements. Among them, Falling Weight Deflectometer (FWD) and Spectral Analysis of Surface Waves (SASW) are the two most widely used methods in modulus measurement. Although popularly used, FWD methods base the analysis purely on the deflection characteristics of the pavement and the back-calculated modulus values are usually not unique for different data processing methods. SASW is based on fundamental spectral analysis of the Rayleigh waves but is practically limited in field operation due to its efficiency concern. On the other hand, the impact loading applied by the FWD will generate the Rayleigh wave, and the geophones used in FWD can capture Rayleigh wave fluctuation and measure the particle motion for phase velocity calculations. It is theoretically possible to use the deflection-time history collected by FWD to perform dispersion analysis based on the Rayleigh wave propagation theory, as used in the SASW method. Inspired from that, the main objective of this paper is to propose a new approach of using the deflection-time history collected by FWD to develop the dispersion curves and thus obtain the in-situ modulus of the layered asphalt pavement. This approach is referred as the FWD dispersion curve method. Two in-service semirigid base asphalt pavement segments were used as case studies to explain the new methodology. The results of layered modulus from the FWD dispersion curve method were compared with the traditional FWD and SASW method, as well as the laboratory Indirect Tensile (IDT) testing results using field cores. Findings demonstrated the feasibility of the FWD dispersion curve method in accurately capturing the layered modulus profile of the asphalt pavements. [ABSTRACT FROM AUTHOR]

Dielectric loss factor is an important index to evaluate the quality of transformer oil, which is very sensitive to the response of polar impurities. As a nondestructive testing technology, ultrasonic can identify polar impurities through attenuation characteristics and then determine dielectric loss. In this article, a novel method for detecting dielectric loss factor of transformer oil based on multifrequency ultrasound and particle swarm optimization-Elman neural network (PSO-ENN) forecasting model was proposed. The analysis of the forecast results and precision shows that the dielectric loss factor can be effectively forecast via this method. The experiment verifies that the ultrasonic effect is nondestructive to transformer oil, which lays a foundation for the development of online monitoring system. [ABSTRACT FROM AUTHOR]

The transient response of an induction coil above an infinite conducting plate is calculated in the time domain (TD) by combining a semi-analytical modal expansion approach with the Euler integration scheme. The derived state equation at a given time step is formally equivalent to the Helmholtz equation with an imaginary frequency linked to the sampling rate, which provides a significant computational advantage with respect to spectral methods based on Fourier/Laplace domain discretization. In addition, time stepping schemes prove to be more robust than the spectral approaches, where the optimal choice of the sample frequencies is an issue of major concern. The developed solution is applied for the calculation of the magnetic field in a driver–pickup coil inspection configuration involving pulsed currents, and the coil response is obtained using the TD version of the reciprocity theorem. The theoretical results are compared against reference numerical solutions demonstrating an excellent agreement. [ABSTRACT FROM AUTHOR]

In this article, we address the problem of synthesized magnetic field focusing (SMF) of the pulsed eddy-current (PEC) technology for the non-destructive testing (NDT) of oil and gas well casing pipes. The electromagnetic response excited by multiple transmitters was derived using the signal model of downhole PEC system. The magnetic field was shown to be adjustable by modifying the currents of the multiple transmitting array. Based on this property, magnetic fields were synthesized with the mean squared error (MMSE) criterion to be focused on the center of the transmitting array, and a weighted MMSE (WMMSE)-based SMF was also proposed to achieve an optimal synthetic magnetic field. Furthermore, the primal-dual interior-point method was used to solve the WMMSE optimization problem with the total and single transmit power constraints for the practical application. Simulations and experimental results showed that the SMF with the PEC array effectively focuses the eddy-current field on the center of the transmitting array. [ABSTRACT FROM AUTHOR]

Modeling the electromagnetic response of carbon nanotube (CNT) reinforced composites is inherently a 3-D multiscale problem that is challenging to solve in real time for nondestructive evaluation (NDE) applications. This article presents a fast and accurate full-wave electromagnetic solver based on a multilayer dyadic Green’s function approach. In this approach, we account for the effects of the dielectric slab, where the CNTs are embedded, without explicitly discretizing its interfaces. Due to their large aspect ratios, the CNTs are modeled as arbitrary thin wires (ATWs), and the method-of-moment (MoM) formulation with distributed line impedance is used to solve for their coupled currents. The accuracy of the in-house solver is validated against the commercial MoM and the finite element method (FEM) solvers over a broad range of frequencies (from 1 GHz to 10 THz) and for a wide range of dielectric slab properties. Examples of 100 nm-long vertical and horizontal CNTs embedded in a 1 $\mu \text{m}$ -thick lossy dielectric substrate are presented. The in-house solver provides more than $50\times $ speed up while solving the vertical CNT and more than $570\times $ speed up while solving the horizontal CNT than a commercial MoM solver over the GHz-to-THz frequency range. [ABSTRACT FROM AUTHOR]

As a rapidly developing novel electromagnetic imaging technique, microwave-induced thermoacoustic tomography (MITAT) has found many applications and attracted tremendous research interest. Using sparse data to reconstruct images is very challenging for MITAT. This work proposes a novel deep-learning-enabled MITAT (DL-MITAT) modality to address the sparse data reconstruction problem and applies it in breast cancer detection. The applied network is a domain transform network called feature projection network (FPNet) + ResU-Net. Detailed structure and implementation method of the network is described. We conduct both simulation and ex vivo experiments with breast phantoms to test the validity of the DL-MITAT approach. The obtained images given by the trained network exhibit much better quality and have much less artifacts than those obtained by a traditional imaging algorithm. We show that only 15 measurements can still reliably recover an image of the breast tumor for both full-view and limited-view configurations in ex vivo experiments. We also provide detailed discussions on the capability and limitations of the proposed scheme. This work presents a new paradigm for MITAT based on sparse data and can be applied in all related applications of MITAT, including biomedical imaging, nondestructive testing, and therapy guidance. [ABSTRACT FROM AUTHOR]

This article explores the use of a 40-kHz air-coupled ultrasonic array in detecting and imaging blockages and defects in buried pipes with 17–26 wavelengths in diameter at short ranges (approximately 20–60 wavelengths). In particular, the imaging performance of arrays with different numbers of transducers is quantified and compared to establish how many are required for adequate performance. Even low numbers of transducers (<25) are capable of producing −6-dB contours of blockages that match reference images to within 95% by restricting the aperture to maintain element density. However, doing so also limits the resolving power, so arrays with more transducers ultimately image better by having great enough density even at larger apertures. Using <25 transducers also gives a poor contrast ratio of features above background noise (as low as 2), resulting in low tolerance for detection and producing unusable images in some cases. More robust performance is achieved with larger numbers of transducers, which achieves sufficient contrast. All images of planar objects feature a low-amplitude band due to interference between direct reflections and reflections via the pipe wall, which was verified by comparison to simulation. When tested in larger pipes in a deployment case, the low-amplitude band was notably larger but was found to decrease in size at longer ranges. [ABSTRACT FROM AUTHOR]

There is a rapid trend in various industries to replace the metallic pipes by nonmetallic ones. This is due to the certain properties, such as high strength, lightweight, resilience to corrosion, and low cost of maintenance for nonmetallic pipes. Despite the abovementioned advantages, nonmetallic pipes are still affected by issues, such as erosion, defects, damages, cracks, holes, delamination, and changes in the thickness. These issues are typically caused due to the manufacturing process, type of carried fluid composition, and flow rate. If not examined well, these issues could lead to disastrous failures caused by leakages and bursting of the pipes. To prevent such major failures, it is extremely important to test the pipes periodically for an accurate estimation of their thickness profile. In this article, we propose a nondestructive testing (NDT) technique, based on near-field microwave holography, for identifying the fluid carried by a nonmetallic pipe and estimating the pipe’s thickness profile. Identifying the carried fluid helps improve the thickness profile estimation. The performance of the proposed techniques will be demonstrated via simulations and experiments. [ABSTRACT FROM AUTHOR]

Progressive techniques that enable reagent-free detection and analysis of low-concentration and low-volume chemical samples are of great interest for scientific and technological investigations in biochemical and physiological areas. Highly sensitive microwave-based sensors are promising approaches among such techniques. In this work, we present a novel microwave-based noncontact sensing method to determine the concentration of standard bovine serum albumin (BSA) solution. It is demonstrated that the modal suppression technique can excite a split-ring resonator by two independent microstrip lines with reversed phase, resulting in high quality factor and resolution. The quality factor exceeding $5.5\times 10^{5}$ with lossy-liquid loaded can be achieved at $\sim 4.3$ GHz. The obtained high resolution in frequency is $\sim 20$ kHz for the concentration of BSA from 0 to 1 mg/mL. The measured concentration level ranges from 1 to 200 mg/mL in 0.9% sodium chloride, which covers most commercial standard BSA solutions. An equivalent circuit model is built to study and explain its working principle. Using the fit premeasurement data of standard solutions, the concentration and permittivity of an unknown BSA solution can be estimated. [ABSTRACT FROM AUTHOR]

Insulated aerial bundled cables (ABCs) are preferred over conventional bare conductor cables in electrical distribution system as ABCs are more safe, less prone to electricity pilferage and offers higher reliability. However, the degradation phenomenon of ABCs is sudden as compared to conventional cables especially in coastal areas. Sudden failures of ABCs in coastal areas make the maintenance planning challenging. Hence, accurate reliability estimation is required which can enable timely maintenance planning and in turn reducing the chances of failures. A novel reliability model is reported in this work which is derived from historical failure data of particular type of ABCs coupled with the degradation models. The models are based upon the actual environmental conditions experienced by the cables under study. The actual loading data as well as environmental data of two sites of varying distance from Seashore are used to develop the respective reliability models. The reliability prediction from proposed reliability model is then validated using time to failure computation through comparison of historical infrared thermography based Non-destructive testing (NDT) data, acquired at the sites under study, with reference to NDT measurements acquired from the ruptured/failed cable. The validation indicates the efficacy of the proposed reliability model. [ABSTRACT FROM AUTHOR]

Electromagnetic nondestructive testing methods to inspect metal rods and pipes are widely used in industrial production. When the scanning speed of the electromagnetic coil is close to the magnitude of eddy current diffusion speed in conductive specimens, the coil motion will significantly affect the distribution of the eddy current field and detection signal. In this article, based on the basic equations and boundary conditions of electromagnetic field problems for moving conductors, the eddy-current analytical model of coaxial relative motion between the coil and the cylindrical conductor rod is solved by using Fourier transform method, and the impedance change expression of the pick-up coil is obtained. The theoretical results are in excellent agreement with the experimental results, which verify the analytical expression of impedance change. Furthermore, the effects of a conductor moving velocity and excitation frequency on impedance-change modulus values for differential and absolute pick-up coils are analyzed, respectively. The theoretical methods proposed in this article quantitatively analyze the velocity effect on the eddy current field, which can be applied to fast real-time eddy current testing for conductive specimens. [ABSTRACT FROM AUTHOR]

Titanium alloys have become increasingly important for biomedical materials due to their high specific strength, good corrosion resistance, and excellent biocompatibility. In this research work, different heat treatments were performed for obtaining equiaxed and Widmanstätten microstructures of Ti-Al-4V extra low interstitials (ELI) alloy. To promote nucleation of $\alpha _{2}$ precipitates (Ti3Al), an aging process was carried out at 515 °C and 575 °C for 2, 288, and 576 h. The corrosion behavior was analyzed by double-cycle potentiodynamic corrosion tests. Hank’s balanced salt solution (HBSS) was used as the electrolyte, and the tests were maintained at 37 °C. This study uses the eddy current (EC) nondestructive evaluation technique, based on the principle of electromagnetic induction, in an effort to monitor corrosion damage in the medical Ti-6Al-4V alloy. The experimental results show significative electrical conductivity variations in EC data of different Ti-6Al-4V ELI alloy microstructures mainly on equiaxed and Widmanstätten conditions before and after corrosion performance. [ABSTRACT FROM AUTHOR]

Fatigue cracks occurred in the welds of steel structure are easily propagated. Therefore, it is important to detect the fatigue cracks at an early stage. However, most of the steel structures are protected by surface coating, which requires removing and restoring the coating for the inspection. This makes the inspection time-consuming. Magnetic methods such as eddy current testing (ECT) and magnetic flux leakage (MFL) can be used without removal of surface coating. However, these magnetic methods are difficult to apply to the shape of the welds and affected by nonuniform magnetic permeability. In this study, we developed a sensor probe adapted to the shape of the welded joint using the unsaturated ac MFL (USAC-MFL), which is less affected by the nonuniform magnetic permeability. To evaluate the performance of the developed sensor probe, the welded joint samples with the surface coating and shallow cracks of which depth ranges from 1 to 5 mm were measured. The measurement results showed a strong correlation between the intensity of the MFL and the crack depth. The detection performance was evaluated using the confusion matrix and the receiver-operating characteristic (ROC) curve. The area under curves (AUCs) of the ROC curves were around 0.9. In order to maximize the true positive rate (TPR) and minimize the false negative rate, the threshold of the intensity to identify a crack was obtained using the ROC curve. As the results of evaluating the detection performance using the obtained threshold, the four metrics (accuracy, precision, recall, and specification) were all above 0.8. [ABSTRACT FROM AUTHOR]

Defect classification and detection have been explored using convolutional neural networks (CNNs). Normally, a large set of training images containing defects and the associated annotation data are required by these approaches. However, such a large set of images is usually difficult to collect because defects are rare and annotation is time-consuming and expensive. To address these issues, we propose to use a multitask deep one-class CNN for defect classification. Compared with supervised classification methods, this CNN does not require abnormal images and annotated data for training. Specifically, we build a stacked encoder–decoder autoencoder for learning feature representation from normal images. The encoder is used as a feature extractor based on the hard sharing scheme of multitask learning. A one-class classification (OCC) objective learned as a hypersphere using minimum volume estimation is appended to it. Together the encoder and the OCC objective lead to a deep one-class classifier. To train both the autoencoder and one-class classifier end-to-end, a multitask loss function is built. Given an unknown sample, the distance between its feature representation and the center of the hypersphere is used as the anomaly score. Furthermore, defect detection is implemented using a moving-window scanning method on top of the deep one-class classifier. The proposed approach achieves better performance than its counterparts trained using a two-stage method. For defect detection, our approach achieves results almost as good as the supervised method even without using any annotated data. We attribute the promising results to the advantages of multitask learning. Note to Practitioners—Building and evaluating vision-based nondestructive testing (NDT) techniques require many examples of abnormal images, which may not be easy to acquire. This article describes a method that does not require abnormal images for training a convolutional neural network (CNN) in order to perform one-class defect classification (outlier detection). We also applied the method to defect detection with promising results. We include results of experiments demonstrating that better performance can be obtained using our method compared to a set of baselines. Although the proposed method does not use abnormal images for training, it still produces results that are almost as good as the supervised learning-based CNN approaches. This study provides a solution to the challenge encountered by the industrial inspection community when enough abnormal samples are hard to obtain. [ABSTRACT FROM AUTHOR]

Deep learning for nondestructive evaluation (NDE) has received a lot of attention in recent years for its potential ability to provide human level data analysis. However, little research into quantifying the uncertainty of its predictions has been done. Uncertainty quantification (UQ) is essential for qualifying NDE inspections and building trust in their predictions. Therefore, this article aims to demonstrate how UQ can best be achieved for deep learning in the context of crack sizing for inline pipe inspection. A convolutional neural network architecture is used to size surface breaking defects from plane wave imaging (PWI) images with two modern UQ methods: deep ensembles and Monte Carlo dropout. The network is trained using PWI images of surface breaking defects simulated with a hybrid finite element / ray-based model. Successful UQ is judged by calibration and anomaly detection, which refer to whether in-domain model error is proportional to uncertainty and if out of training domain data is assigned high uncertainty. Calibration is tested using simulated and experimental images of surface breaking cracks, while anomaly detection is tested using experimental side-drilled holes and simulated embedded cracks. Monte Carlo dropout demonstrates poor uncertainty quantification with little separation between in and out-of-distribution data and a weak linear fit ($R=0.84$) between experimental root-mean-square-error and uncertainty. Deep ensembles improve upon Monte Carlo dropout in both calibration ($R=0.95$) and anomaly detection. Adding spectral normalization and residual connections to deep ensembles slightly improves calibration ($R=0.98$) and significantly improves the reliability of assigning high uncertainty to out-of-distribution samples. [ABSTRACT FROM AUTHOR]

A pump–probe system with a subnanosecond pulsed laser is expected to be a compact and inexpensive transient spectroscopic system that enables nondestructive and noncontact evaluations of the physical properties. However, an improvement in the sensitivity and a theoretical model to complement the measurement signal are necessary to obtain the transient signal precisely because of the low sensitivity and large time resolution. We have developed a highly sensitive pump–probe system with a subnanosecond pulsed laser that combines signal amplification based on surface plasmon resonance (SPR) in this study. An integrated theoretical model of the transient response obtained by a subnanosecond pump–probe under the SPR condition was proposed. Our model consisted of the profile descriptions of the used pulse source, temperature change, generated thermoelastic stress, estimated permittivity change in the metal film, and estimated reflectivity change. The theoretical estimations in the time domain and the incident angle dependence were compared with those of the experimental results to verify our theory. As a result, the estimations were well in agreement with the experimental results. Moreover, the signal-amplification mechanism based on SPR was discussed using our theory. The amplification was caused by the broadening of the resonant curve of SPR and the shift of the resonant angle, which seemed to come from the increase in the electron–phonon scattering rate and the thermal expansion of the metal film, respectively. A clear mechanism of SPR-based signal amplification of the subnanosecond pump–probe was identified through experimental and theoretical approaches. [ABSTRACT FROM AUTHOR]

Indirect measurement principles have always been presented as a solution to save simulation and modeling efforts for complex permittivity extraction methods. However, detailed investigations on remaining systematic errors as well as recommendation guidelines for reference material selection are missing in the literature. In this article, typical systematic error sources of a planar coplanar waveguide over ground material sensor are investigated. An indirect error compensation algorithm is presented, and its ability to compensate the main error sources is simulatively analyzed. A measurement setup is created by considering the specified requirements. The measurement principle is validated by measuring three different plastics. Overall, the results are plausible and have uncertainties of the same or lower order of magnitude compared with the state of the art. However, the findings show that indirect planar methods suffer from fundamental problems, which limit the achievable measurement uncertainty. The findings of this work can be used as a basis for decision-making when considering an indirect planar measurement principle. [ABSTRACT FROM AUTHOR]

In the field of ultrasonic nondestructive testing (NDT), the total focusing method (TFM) and its derivatives are now commercially available on portable devices and are getting more popular within the NDT community. However, its implementation requires the collection of a very large amount of data with the full matrix capture (FMC) as the worst case scenario. Analyzing all the data also requires significant processing power, and consequently, there is an interest in: 1) reducing the required storage capacity used by imaging algorithms, such as delay-and-sum (DAS) imaging and 2) allowing the transmission and postprocessing of inspection data remotely. In this study, a different implementation of the TFM algorithm is used based on the vector coherence factor (VCF) that is used as an image itself. This method, also generally known as phase coherence imaging, presents certain advantages, such as a better sensitivity to diffracting geometries, consistency of defect restitution among different views, and an amplitude-free behavior as only the instantaneous phase of the signal is considered. Some drawbacks of this method must also be mentioned, including the fact that it poorly reproduces planar reflectors and presents a lower signal-to-noise ratio (SNR) than amplitude-based methods. However, previous studies showed that it can be used as a reliable tool for crack-like defect sizing. Thus, a lightweight acquisition process is proposed through single-bit digitization of the signal, followed by a phase retrieval method based on the rising and falling edge locations, allowing to feed the phase coherence imaging algorithm. Simulated and experimental tests were first performed in this study on several side-drilled holes (SDHs) in a stainless steel block and then extended to an experimental study on angled notches in a 19.05-mm (${{3}}/{{4}}''$)-thick steel sample plate through multiview imaging. Results obtained using the array performance indicator (API) and the contrast-to-noise ratio (CNR) as quantitative evaluation parameters showed that the proposed lightweight acquisition process, which relies on binary signals, allows a reduction of the data throughput of up to 47 times. This throughput reduction is achieved while still presenting very similar results to phase coherence imaging based on the instantaneous phase derived from the Hilbert transform of the full waveform. In an era of increasing wireless network speed and cloud computing, these results allow considering interesting perspectives for the reduction of inspection hardware costs and remote postprocessing. [ABSTRACT FROM AUTHOR]

Digital beamforming methods in plate-like structures are widely exploited for Lamb waves-based damage imaging. Among them, the delay and sum (DAS) imaging technique is the most popular thanks to its low-computational cost and ease of implementation. However, the imaging outputs are low quality due to the high levels of side lobes and limited off-axis signal rejection, which leads to limited image resolution and contrast. Recently, the delay multiply and sum (DMAS) beamforming has been applied to nondestructive testing (NDT) field as a promising DAS alternative able to enhance the imaging reconstruction in terms of contrast and damage detectability. However, DMAS is still affected by high levels of artifacts. To tackle this aspect, literature offers a beamforming algorithm called double-stage DMAS (DS-DMAS), first introduced in photoacoustic imaging and medical ultrasound imaging. In this article, the DS-DMAS performance is analyzed for Lamb waves inspection, to provide an exhaustive comparison between DAS, DMAS, and DS-DMAS. As a further step, a filtering process addressed as Fresnel zone filtering (FZF) is used to restrict the beamforming partial sums in a physical way to the area around the scattering point. The proposed approach is an adaptation of a well-established technique in seismic data processing called Fresnel migration, able to suppress artifacts and enhance the quality of the imaging. The algorithms have been compared and characterized by exploiting an online free dataset for guided waves inspection (ht.tp://openguidedwaves.de/) which collects piezo pitch-catch signals traveling through a quasi-isotropic carbon fiber-reinforced plate (CFRP) at different actuated frequencies and damage positions. [ABSTRACT FROM AUTHOR]

One of the most common methods for performing non-destructive testing (NDT) of the steel tank floors in aboveground storage tanks is dc magnetic flux leakage (MFL). This test method gives an estimate of the defect depth and width based on the MFL signal strength and its peak location, respectively. A key limitation is that the signal strength depends on not only the defect depth and width but also various other factors including a defect’s wall profile and sensor lift-off. Moreover, in a practical MFL test, the sensor lift-off changes due to surface roughness and uneven steel plate surface in tank floors. We present an ac MFL system to increase the accuracy of defect characterization by: 1) distinguishing between two common defect shapes located on the far side of the steel plate used in a typical above storage tank (AST) floor: lake-shaped and rectangular defects and 2) developing a sensor lift-off compensation scheme based on ac signal phase. Simulation results show that skewness of the ac MFL can be used to distinguish between far-side lake-shaped and rectangular defects. AC MFL signal phase is shown to be a suitable compensator for the dependence of signal strength on unintended variations in sensor lift-off. The simulation results have been validated through experiments. [ABSTRACT FROM AUTHOR]

Deep learning is an effective method for ultrasonic crack characterization due to its high level of automation and accuracy. Simulating the training set has been shown to be an effective method of circumventing the lack of experimental data common to nondestructive evaluation (NDE) applications. However, a simulation can neither be completely accurate nor capture all variability present in the real inspection. This means that the experimental and simulated data will be from different (but related) distributions, leading to inaccuracy when a deep learning algorithm trained on simulated data is applied to experimental measurements. This article aims to tackle this problem through the use of domain adaptation (DA). A convolutional neural network (CNN) is used to predict the depth of surface-breaking defects, with in-line pipe inspection as the targeted application. Three DA methods across varying sizes of experimental training data are compared to two non-DA methods as a baseline. The performance of the methods tested is evaluated by sizing 15 experimental notches of length (1–5 mm) and inclined at angles of up to 20° from the vertical. Experimental training sets are formed with between 1 and 15 notches. Of the DA methods investigated, an adversarial approach is found to be the most effective way to use the limited experimental training data. With this method, and only three notches, the resulting network gives a root-mean-square error (RMSE) in sizing of 0.5 ± 0.037 mm, whereas with only experimental data the RMSE is 1.5 ± 0.13 mm and with only simulated data it is 0.64 ± 0.044 mm. [ABSTRACT FROM AUTHOR]

Nondestructive thermography is a high-speed, low-cost, and safe solution for subsurface defects detection of carbon fiber reinforced polymer (CFRP) materials, providing essential quality control in aerospace, automobile, and sports industries. In this article, we build a reflective lock-in thermography system and construct a dataset that contains real-captured thermal image sequences of CFRP samples with various simulated internal defects under different excitation frequencies. Then, we present a novel 3-D convolutional neural network (CNN) model incorporating a combination of spatial and temporal convolutional filters and batch-size independent group normalization (GN) as a unified framework to process thermal image sequences captured by lock-in thermography for simultaneous subsurface defect detection and depth estimation. Finally, we define a multitask loss function to perform end-to-end training of both defect detection and depth estimation tasks based on the real-captured infrared sequences. Comparative experiments are carried out on CFRP specimens with artificial defects of various sizes/shapes and at different depths. Qualitative and quantitative results illustrate that our 3-D CNN model is capable of predicting accurate locations and depths of subsurface defects and performs favorably against the hand-crafted and CNN-based methods in lock-in thermography for individual defect detection and depth estimation tasks. The captured dataset and the source codes will be made publicly available. [ABSTRACT FROM AUTHOR]

The parts of working machinery have always suffered from various working conditions in the process of use (e.g., high temperature, high humidity, and high pressure) that may generate flaws, pitting, thermal fatigue crack and other types of defects on the surface or inside. Because these tiny defects carry big safety risks, regular detection is necessary to ensure the reliability of the production equipment. [ABSTRACT FROM AUTHOR]

Transient radar method (TRM) is introduced as a novel non-destructive testing (NDT) technique that is capable of analyzing fully blindly and simultaneously the electromagnetic (EM) properties as well as geometric parameters of deep sub-wavelength thin single layer slabs. This study focuses on the minimum detectable layer thickness by means of TRM, which mainly depends on the experimental setup configuration. In this article, the complex permittivity and thickness of three polyvinyl chloride (PVC) sheets are extracted by means of a differential TRM set-up. The thickness of these PVC sheets was 1 mm, $500 ~\mu \text{m}$ , and $300 ~\mu \text{m}$ that corresponded to $\lambda $ /30, $\lambda $ /60, and $\lambda $ /100, respectively ($\lambda $ is the wavelength in free space). The carrier frequency was 10 GHz and the experiments were done in a bistatic radar setup. A differential TRM structure was used in this experiment. Some error sources, such as switch leakage and non-perpendicular illumination as well as systematic errors are considered as well. The experimentally obtained results are as follows: the thickness of these samples were 1033 ± 12, 535 ± 11, and 302 ± $9 ~\mu \text{m}$ with 1.18%, 0.94%, and 2.89% relative error, respectively. Additionally, the complex permittivity of these samples was found as (2.73 ± 0.02)–(0.23 ± 0.01)j, (2.70 ± 0.02)–(0.20 ± 0.02)j, and (2.65 ± 0.02)–(0.37 ± 0.01)j, respectively. This novel technique has the potential for deployment in a wide range of applications ranging from the piping, wind energy industry, and automotive to biotechnology, food industry, clinical monitoring, and pharmacy. [ABSTRACT FROM AUTHOR]

Port structures are important parts of vacuum vessel (VV) in China Fusion Engineer Test Reactor (CFETR), which consists of port stubs and port extensions, mainly provides channels for the installation and maintenance of internal components, such as divertor and blanket, and provides interface for auxiliary heating system, diagnostic system, and vacuum pumping. A 1/8 VV sector mockup with port structures is used to explore the key manufacturing technology of CFETR VV. Electron beam welding (EBW) process development of port structures of 1/8 VV sector mockup is introduced in this article taking the inner shell of upper port stub as an example. Through EBW test and process optimization, the surface formation, microstructure, and mechanical properties of the weld are obtained. On this basis, the EBW of the inner shell of the upper port stub is carried out. After phased array ultrasonic nondestructive testing and laser tracker measurement, the weld quality is excellent, and contour accuracy has reached the technical requirements. This study provides important support for the engineering design and construction of the CFETR. [ABSTRACT FROM AUTHOR]

This article presents a cylinder-type magnetic camera (CMC) system in nondestructive testing (NDT) of a heat exchanger (HX) tube. The CMC system is developed for the inspection of corrosion on HX tubes of nuclear power plants. The CMC probe works based on the principle of eddy current testing, in which a single-large bobbin coil is used to supply eddy current on the tube, and a matrix array of InSb Hall sensor is used to measure the electromagnetic field around the corrosion. The CMC system can measure both the real and imaginary components of the magnetic field. The CMC system can operate in two modes: single-line scan mode by selecting a single-line Hall sensor or area scan mode by the electrical scan. The limitations and advantages of each operation mode will be presented. The CMC system has the potential for further applications such as evaluating corrosion size, shape, and locations. [ABSTRACT FROM AUTHOR]

An improved reversion method for ultrasonic surface wave characterization of coating layers is presented and validated by experiment. The experiment is implemented with seven specimens of thin Cu layers coated on Al substrates. 2-D Fourier transforms of the experimentally obtained surface wave pulse propagation signals were conducted to obtain frequency-wave number relations. The combined equations of two unknowns were derived using the relation. The two unknowns are the thickness h and the Young's modulus E of the coating layer of the specimen. The h and E are obtained by solution of the combined equations. Comparing with that used for many years which are based on 1-D Fourier transform and nonlinear regression, the new method is simpler, faster, and equally accurate and reliable. The standard error of 1.55μm is reached in the measurements of the Cu films thicknesses which vary in the range from 23.6μm to 224.8μm. The standard error of 2.97GPa is reached in measurements of Young's modulus of the Cu films and the exact value of Young's modulus of pure Cu is 110GPa.

The characterization of micro cracks on metal surface plays an important role in the process of manufacturing and using, which has attracted a lot of attention. This is mainly due to the fact that the size of defects is smaller and the depth of defects is difficult to predict compared with metal materials with larger plane, which is still challenging. In order to solve this problem, this paper proposes a characterization method of surface defects based on reflective laser thermography, and designs a laser heating nondestructive testing system based on reflection. The system includes a semiconductor laser to heat the surface of metal cracks, and an infrared imager to record changes in the temperature field of the metal surface. In the process of data analysis, an Otsu adaptive threshold segmentation method is selected to quantify the defect size, which can control the quantification accuracy of defect size within 25%. A derivative analysis method is proposed to quantify the depth of defects, which can control the depth quantification accuracy of tiny defects within 7%.

With the increased use of high-performance thin steels, detecting internal or external micro defects in a manufacturing process has become critical for the steel manufacturing process’s cost and quality management. For detecting defects, magnetic-based nondestructive testing (NDT) methods have been researched widely due to their simplicity, ease of use, and contactless nature. However, magnetic-based approaches suffer from low sensitivity, thus limiting their use. Here, we present a highly sensitive tunneling magnetoresistive (TMR) imaging system to detect sub-surface defects in thin steel samples. Artificially created holes in the steel plates were magnetically imaged by measuring magnetic flux leakage (MFL). The experimental results were compared against the simulated results to confirm the visibility of the holes. We also confirmed the successful visualization of the sub-surface defects in the steel plates formed during the real manufacturing processes. Thus, based on these results, we believe the TMR method holds great potential as an NDT technique for steel manufacture management.

This contribution presents a novel, computationally efficient approach to radar imaging of layered dielectrics with sparse MIMO arrays. Our concept does not impose any constraints on the array topology and at the same time promises to be more efficient than the state-of-the-art backprojection algorithm because it can make use of k-space reconstruction schemes. Experimental results with a sparse, non-equidistantly sampled array are provided. These demonstrate the feasibility of the approach and that the computational burden could be reduced by several orders of magnitude in a given practical example related to radar based non-destructive testing.

Terahertz tomography allows for non-contact tomographic inspection of dielectric materials without the need for radiation protection measures. Terahertz tomography offers the opportunity to inspect such objects from multiple angles not only by measuring the absorption but also by acquiring the time-of-flight of the radiation. Hence, this technique facilitates the reconstruction of the complete complex refractive index of a sample under test. Even complicated surface structures can be imaged, provided the feature size is above the diffraction limit roughly given by the wavelength of the terahertz radiation in use. For industrial applications, computational efficiency and imaging performance are crucial. Therefore, we apply the iterative conjugate gradient least square (CGLS) algorithm to reconstruct images from terahertz tomography data. To ensure reliable convergence of this semi-convergent CGLS algorithm a stopping mechanism based on the L-curve criterion is implemented. The result is a fast-converging, parallelizable method, which offers the flexibility to adapt to the specifics of terahertz tomography. As an example of this adaptability, we implement a non-negativity constraint, suppressing noise in the image and significantly enhancing reconstruction quality.

Fluxgate sensors with straight wire or rod cores are used in nondestructive testing (NDT), portable gradiometers, and sensor arrays and for the detection of small objects. We show that their sensitivity at the voltage output mode depends on the excitation parameters, properties of the core material and geometry, pick-up coil length, but only slightly on the pick-up coil diameter. This finding allows one to design multiwire cores with large wire pitch, which decreases their magnetic interactions and thus reduces demagnetization and correlation of their noise. As a result, using $N$ wires theoretically increases sensitivity $N$ -times, which is not achievable with dense cores. We have demonstrated this tendency for $N$ up to 8 and one type of permalloy wire. [ABSTRACT FROM AUTHOR]

In oil mining system, stress cracks on thread usually lead to drill pipe failure. To maintain normal operation of drill pipes, the thread needs to be nondestructively tested. However, due to complicated structure, it is challenging to identify and quantify the cracks the using existing nondestructive testing (NDT) methods. In this article, a scanning induction thermography (SIT) system for automatic detection of drill pipe thread (DPT) is presented. Specifically, a profiling heating method (PHM) is proposed to produce uniform heating on the root part of thread tooth where most of the stress cracks occur. A weighted least squares fitting (WLSF)-based difference algorithm is used to eliminate the background temperature of scanning data. The algorithm can extract crack thermal response with the influence of lift-off distance change and marginal effect of electric field in SIT. Experiments show that the proposed system can obtain 3k thermal contrast between crack and defective-free area when testing the crack with only 0.3 mm depth. Three SIT systems with other heating methods are also used for comparison. Better heating uniformity, signal intensity, robustness of structural change, linear property with depth information, and signal consistency when testing similar cracks in different areas of the root part can be demonstrated in the proposed system. [ABSTRACT FROM AUTHOR]

We put forward a focusing formula to describe the guided waves periodical focusing phenomenon, which, apart from being a fundamental problem in the guided waves’ propagation in the pipes, has essential applications in the field of nondestructive evaluation. Due to the partial circumferential loads or nonaxisymmetric defects, the guided waves are not only in the zeroth-order axisymmetric forms but also in the higher order nonaxisymmetric forms. When multiple orders of the same mode exist simultaneously, the angular profile is adopted to describe the circumferential energy distribution of the superposed wave field. However, the angular profile varies in the propagation process. In our finding, this variation is periodic, meaning that the circumferential energy will repeat the process of dispersing and focusing. Thus, we put forward a focusing formula to describe the phenomenon. The proposed formula indicates that the angular profile varies periodically with the ratio of propagation distance to wavenumber, and this period is only related to the pipe radius. Thus, three factors, including propagation distance, excitation frequency, and pipe radius, will affect the angular profile. Moreover, we established an experimental system to verify this phenomenon, based on which we have designed three groups of experiments to investigate these three factors. The experimental results are in good agreement with the theoretical predictions. [ABSTRACT FROM AUTHOR]

The article presents a monostatic $D$ -band frequency-modulated continuous-wave (FMCW) radar based on a fully integrated monostatic single-channel silicon-germanium (SiGe) transceiver (TRX) chip. The chip is fabricated in Infineon’s bipolar-complementary metal–oxide–semiconductor (BiCMOS) production technology B11HFC which offers heterojunction bipolar transistors (HBTs) with an $f_{\mathrm {T}}/f_{\mathrm {max}}$ of 250 GHz/370 GHz. The monolithic microwave integrated circuits (MMICs) output signal is coupled by a fully differential substrate integrated waveguide (SIW) based coupling network. The output power at the WR-6.5 antenna flange is more than −10 dBm over a bandwidth of 37.5 GHz. For a sweep within a single-loop phase-locked loop (PLL) circuit from 174.5 to 121.5 GHz, a spatial resolution of almost 3 mm with a metallic plate as the target is achieved. The radar provides a small form factor of $2 \times 4 \times 5$ cm3 and low power consumption of 2.2 W at 5 V. Finally, the capabilities of the sensor for non-destructive testing (NDT) are demonstrated using a millimeter scanner. With radar imaging, it was possible to measure the orientation of the fiber layers up to a depth of 7.03 mm. [ABSTRACT FROM AUTHOR]