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Pitting corrosion, a localized form of corrosion leading to cavities and structural failure in metallic materials, requires early detection for effective mitigation. While ultrasonic inspection techniques can readily detect uniform wall thinning, they often struggle to identify pitting corrosion. This study proposes a time-lapse ultrasound inspection method to detect early-stage pitting using pulse-echo sensors. By recording multiple ultrasonic traces over time, 2-D timelapse images of ultrasonic reflectivity can be generated and fed into a trained neural network for pitting diagnostics. In general, training a machine-learning model requires a large training dataset. This work used data from a drilling experiment to generate a suitable dataset. Dataset construction by random time-ordered combinations of ultrasonic measurements was conducted to create a diverse set of time-lapse image samples to generalize the resulting machine-learning model adequately. A classification neural network was trained to detect the presence of drilled holes, and a separate regression network was trained to estimate the hole depth. Based on drilling data from an independently acquired test dataset, results demonstrate a mean absolute error of 0.163 mm for hole depth estimations. All holes are successfully detected when 0.1 mm deeper than the defined pitting threshold of 0.5 mm. This suggests that the proposed method generalizes well and can be deployed to any similar acquisition system.

Acoustic emission (AE) is one of the most effective nondestructive testing (NDT) techniques for the identification and characterization of stress waves originated at the uprising of acoustic-related defects (e.g., cracks). To this end, the estimation of the time of arrival (ToA) is crucial. In this work, a novel processing flow which shifts the identification process from the time to the time-frequency domain via wavelet transform (WT) is proposed, allowing to better capture transient behaviors typical of the originated AE signals. More specifically, both the continuous and the discrete WT alternatives have been explored to find the best compromise between time-frequency resolution and computational complexity in view of extreme edge deployments. Furthermore, the event-driven capabilities of neuromorphic architectures (and spiking neural networks (SNNs) in particular) in processing spiky and sparse temporal information are exploited to retrieve ToA in a beyond state-of-the-art power-efficient manner and negligible loss of performance with respect to standard models. Therefore, we aim at combining the superior performances in ToA identification enabled by the WT operator with the unique energy saving disclosed by spiking hardware and software. Experimental tests executed on a metallic plate structure demonstrated that WT combined with SNN can achieve high precision (median values less than 5 cm) in ToA estimation and AE source localization even in the presence of relevant noise (signal-to-noise ratio down to 2 dB), while its deployment on dedicated neuromorphic architectures can reduce by six orders of magnitude the power expenditure per inference when compared to standard convolutional architectures.

Wireless monitoring of displacement between two points is demonstrated with an interrogation range (${D}_{\boldsymbol {m}}$) which is dramatically larger compared with previous configurations based on near-field coupling. The sensing system comprises a transmitting antenna, a nested split-ring resonator (NSRR) sensor functioning as a tag, and a probe, called a “sniffer coil.” In classical configuration, the NSRR and the antenna constitute an electromagnetically coupled system which exhibits high sensitivity and resolution but is only functional within the near-field of the antenna, which in practice is much smaller than one wavelength, which limits the capabilities of the sensor. In this work, the system is separated into the TX and RX channels, which enables the transmitter antenna to be placed at a location which is several wavelengths away from the sensor. To remove the effect of clutter that disrupted the sensing signal at higher ${D}_{\boldsymbol {m}}$ in previous works, the variation with frequency of the power level coupled to the sniffer coil is tracked instead of the variation in the scattering parameters with frequency. While extending ${D}_{\boldsymbol {m}}$ , the high sensitivity and resolution are retained by the coupling between the NSRR and the sniffer coil. Wireless sensing of displacement is essential for several application areas, most specifically, structural health monitoring (SHM). The fact that the sensing system can work in far-field and that the NSRR is passive allows for remote and nondestructive testing (NDT) of damaged structures and buildings. [ABSTRACT FROM AUTHOR]

This article proposes a novel framework to characterize the morphological pattern of barely visible impact damage using machine learning. Initially, a sequence of image processing methods is introduced to extract the damage contour, which is then described by a Fourier descriptor-based filter. The uncertainty associated with the damage contour under the same impact energy level is then investigated. A variety of geometric features of the contour are extracted to develop an artificial intelligence model, which effectively groups the tested 100 samples impacted by 5 different impact energy levels with an accuracy of 96%. Predictive polynomial models are finally established to link the impact energy to the three selected features. It is found that the major axis length of the damage has the best prediction performance, with an R2 value up to 0.97. Additionally, impact damage caused by low energy exhibits higher uncertainty than that of high energy, indicating lower predictability. [ABSTRACT FROM AUTHOR]

U-shaped excitation structures are widely used to magnetize steel plates in magnetic flux leakage (MFL) testing. However, the edge effect leads to the difficulty of further increasing the magnetization near the defect as well as the leakage magnetic field (LMF). Therefore, this article proposes an MFL testing device with a pot-shaped excitation structure to concentrate on the main magnetic flux in the steel plate between the two magnetic poles for improving the LMF. First, the simulated comparison of the conventional U-shaped and the proposed pot-shaped structures is presented in this article. Then, a sensor array is applied to investigate the measured MFL signal components and the C-scan images of the pot-shaped structure. Finally, the experiments are conducted to verify the effectiveness of the simulations and investigate the effect of the relative position of the sensor. The results show that the peak-to-peak value of the MFL signal can increase to 1.93 times after using the pot-shaped structure. Besides, its signal is not affected by the steel plate width and remains the ability to characterize defect sizes and profiles. [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]

In recent years, trees in European countries have been increasingly endangered by emerging infectious diseases (EIDs). In the United Kingdom (U.K.), this has been observed to affect whole woodlands and forests, threatening the existence of some types of trees. Although quarantine measures have been taken to limit the spreading of such diseases, this has not yet been effectively controlled leading to millions of trees affected by EIDs. Ground-penetrating radar (GPR) has proven effective in identifying critical features on diseased trees for detection of EIDs spread. However, the irregular shape of tree trunks and their complex internal structure represent real challenges for conventional GPR measurements and data processing methodologies. In this research, a dual-polarized GPR system is used to detect internal decay in tree trunks using novel signal processing methodologies. A polarimetric correlation filter based on Bragg scattering on a 3-D Pauli feature vector and an arc-shaped Kirchhoff migration are discussed in detail. The proposed polarimetric correlation filter is utilized to enhance the signal-to-noise ratio (SNR) of B-scans due to bark and tree trunk high-loss properties of tree trunks. Meanwhile, an arc-shaped Kirchhoff migration algorithm is performed to counteract the influence of the bark irregularity. The proposed data processing framework is successfully validated with measurements on a real tree trunk, where cross sections were subsequently cut for comparison purposes. Outcomes from the proposed methodology demonstrate a high consistency with the features observed on the tree trunk cross sections, indicating the reliability of the proposed detection scheme for assessing tree-decay associated with EIDs. [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]

This article presents a new method called phase of transient response using the local reference pixel vector (PTR-LRPV) to process pulsed thermography data for defect detection and depth estimation in carbon fiber reinforced polymer specimens. Due to the use of flash as excitation source in pulsed thermography and subsequently its adaptation with the conditions of suddenly applied input, the received signal from the infrared camera can be separated into two transient and steady-state responses in the frequency domain. Defects cause local variations in the thermal spatio-temporal patterns that the transient response can fairly reveal such highly informative variations. On the other hand, the steady-state response mainly includes the intrinsic characteristics of the specimen (sound areas). In fact, by properly separating these responses and employing the phase of the transient term, a suitable distinction of defect characteristics from sound areas has been reached. The results show that the proposed PTR-LRPV is effective in both defect detection and depth estimation tasks, and also, can fairly compete with several well-known algorithms in terms of both quantitative and qualitative criteria. [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]

Nondestructive testing (NDT) methods are widely used in the rail industry to detect and characterize rolling contact fatigue (RCF) defects in railroads, which is very important for railway inspection and maintenance to prevent catastrophic accidents. Existing NDT methods, e.g., ultrasonic testing (UT), magnetic flux leakage (MFL), and eddy current testing (ECT) have been successfully applied in the rail industry, and the state-of-the-art UT method reported recently achieved a high speed of 40 mi/h with a probability of detection (POD) over 80% under 30% false alarms. However, NDT methods still suffer from a bottleneck in that a higher inspection speed causes lower detection sensitivity due to their physical limits, such as negative velocity effect and long sensing time. One of the leading challenges to the rail NDT community is to develop a high-speed high-sensitivity (HSHS) capability that can provide an improved POD of rail defects in high-speed inspection scenarios over 60 mi/h. This article proposes a horizontal U-shaped magnets-based motion-induced eddy current array (MIECA) method to detect rail surface defects with the HSHS capability. The MIECA method deploys a three-axis magnetic sensor array along the rail transverse direction at the middle of the magnets to measure the MIECA signals, which utilizes the wake effect of the diffused motion-induced eddy current (MIEC) caused by the relative high-speed motion between the magnets and the rail track. Finite-element method (FEM) simulations with a wide speed range from 0 to 62.5 mi/h are carried out to investigate the relationships between the MIEC generation, diffusion and magnitude, and the three-axis MIECA signals. The simulation results show that the higher the speed, the greater the magnitude of diffused MIEC, and the greater the peak-to-peak values of three-axis MIECA signals caused by rail surface defects, which shows a great promise for detecting rail surface defects in high-speed scenarios and is superior inspection relative to existing NDT methods in terms of inspection speed, detection sensitivity, and defect characterization capability. [ABSTRACT FROM AUTHOR]

X-ray imaging has been broadly adopted as a nondestructive testing method for product quality inspection. Deep learning has demonstrated powerful image scene understanding capabilities. In this article, U-Net with resnet101 is taken as the baseline for defect segmentation. First, there exist gray inhomogeneous and low-contrast regions in X-ray images, which can hardly be segmented. Contrast-limited adaptive histogram equalization (CLAHE) could be used to improve the contrast and consistency of the X-ray image. A two-stream convolutional neural network (CNN) is proposed that takes the original image and CLAHE processed image as inputs to address this issue. And then in CNN, low-level feature maps are lacking semantic information, which may lead to worse results. A gated multilayer fusion module is proposed to adaptively fuse the high-level features into low-level features. Furthermore, loss functions (such as cross entropy) in semantic segmentation are usually pixel level, ignoring the regional information. A weighted intersection over union (IOU) loss function is proposed to introduce IOU information to guide the model to focus on the objects that are easy to mine. The experimental results prove that the three proposed methods have better performance than the baseline for our dataset, achieving 42.2 in mIoU, 59.2 in Dice, and 54.5%, 74.9%, and 86.3% in small, middle, and large object recall rate, respectively. [ABSTRACT FROM AUTHOR]

Regular inspections of steam generator tubes (SGTs) to evaluate structure degradation and sludge deposition are essential for maintaining safety and efficiency of power plants. However, with the interference of complex structures such as tube support plates (TSPs), simultaneously detecting multiple objects is still a challenging problem. This article proposes a novel integrated versatile probe for multiobject inspection. The probe consists of a low-frequency operating region (LFOR) with tunnel magnetoresistance (TMR) array sensors and a high-frequency operating region (HFOR) that uses array coils as the pickup sensors. After frequency optimization and image calibration, the signals of three types of objects are concentrated in three different modalities of the probe’s output. Then, features for position of TSP, profile, and location of sludges are extracted from the image of LFOR. A feature extraction algorithm based on convolution and pattern search is developed to extract defects’ features from the quadrature component image of HFOR. Finally, a hierarchical independent decision-level data fusion scheme is adopted to integrate and visualize the recognition and discrimination of multiple objects in an RGB image. Combinations of machined defects, sludges, and TSP mockup on an SGT are experimentally tested by the probe. Experimental results show that the TSP and sludges are detected accurately. Most of the defects, including the defect embedded under the TSP, can be identified and localized with a high intersection of union (IoU) of 0.92. The TSP, sludges, and defects are clearly visualized in the fused image. For complex scenarios with multiple objects superimposed, the fused image shows each type of objects clearly. [ABSTRACT FROM AUTHOR]

Eddy current testing (ECT) is an effective technique for evaluating depth of metal surface defects. However, in practice, evaluation primarily relies on the experience of an operator and is often carried out by manual inspection. In this article, we address the challenges of automatic depth evaluation of metal surface defects by virtual of state-of-the-art deep learning (DL) techniques. The main contributions are threefold. First, a highly integrated portable ECT device is developed, taking the advantage of an advanced field-programmable gate array (Zynq-7020 system on chip) and provides fast data acquisition and in-phase/quadrature demodulation. Second, a dataset, termed metal defects of different depths by ECT (MDDECT), is constructed using the ECT device by human operators and made openly available. It contains 48000 scans from 18 defects of different depths and liftoffs. Third, the depth evaluation problem is formulated as a time series classification problem, and various state-of-the-art 1-D residual convolutional neural networks are trained and evaluated on the MDDECT dataset. A 38-layer 1-D ResNeXt achieves an accuracy of 93.58% in discriminating the surface defects in a stainless steel sheet with depths from 0.3 to 2.0 mm in the resolution of 0.1 mm. In addition, the results show that the trained ResNeXt1D-38 model is immune to liftoff signals. [ABSTRACT FROM AUTHOR]

Detecting covered cracks in metals is critical in many applications. In this article, a single dual-polarized circular aperture antenna and synthetic aperture imaging are devised to detect arbitrarily oriented cracks covered under thick dielectric layer. The devised monostatic quad-polarized system facilitates measurements of the reflection (copolarized) and transmission (cross-polarized) parameters which form the basis for reliable detection. Through analysis and measurements involving dielectric covers with thicknesses ranging from 4.25 to 12.7 mm, it will be demonstrated that the background clutter and the effect of the cover are largely suppressed in the images formed using the cross-polarized parameter. On the other hand, it will be highlighted that the crack could be actually masked by the clutter in the image formed using the reflection measurements commonly used for crack detection. [ABSTRACT FROM AUTHOR]

This article presents a new methodology for structural health monitoring (SHM) applications using a passive microwave sensor. This sensor provides sensitivities on metallic structures for nondestructive testing (NDT) and detecting fatigue cracks or damages. In this method, two different microstrip-based designs are mounted on metal, spiral, and comb sensing structures. Both sensing structures are interrogated by a 2.45 GHz signal in CST Microwave Studio, and their sensitivities for crack detection are compared through the $S_{11}$ scattering parameter. We demonstrate that measuring the reflected signal’s phase parameter from a sensor on a damaged metal provides information from the surface crack by comparing it to the same metal without any crack. The vision is to provide a new chipless, low-cost sensor with increased detection reliability and durability in harsh environments. Simulation results of the comb sensing (CS) structure show that the signal phase shift caused by a crack envisions the possibility of submillimeter-width crack detection through smart structures. [ABSTRACT FROM AUTHOR]

In this article, the application of active microwave thermography (AMT) is investigated as an efficient nondestructive technique (NDT) for health monitoring of structures coated with radio frequency absorbing materials (RAMs). The uniqueness of AMT for this particular application is the use of microwave excitation, which acts as a highly efficient thermal source due to the inherent absorptive electromagnetic properties of RAM-coated structures. In addition, the microwave-induced heating takes place subsurface within the absorbing material of an RAM-coated structure, as opposed to the sole surface heating of conventional thermography. Specifically, this work focuses on detection of delamination, a common defect in carbon fiber reinforced polymer (CFRP) structures, via AMT. To this end, a formulation and analysis method based on electromagnetic/thermal modeling is presented, which enables estimation of the output of an AMT inspection of an RAM-coated CFRP structure suffering from delamination. The effect of the structure’s constitutive properties, the microwave excitation parameters, and the delamination size and depth on its detectability is investigated. In addition, several simulated and experimental results are provided, which verifies the accuracy of the proposed approach and shows the high efficiency of AMT for detection of delamination in such structures. [ABSTRACT FROM AUTHOR]

Local shallow defects on the surface of ferromagnetic materials are difficult to detect and quantify. Imaging of this kind of defects benefits to evaluate the deterioration rate. However, traditional nondestructive testing (NDT) methods have some limitations in detection sensitivity and spatial sizing resolution. To resolve this problem, a 3-D imaging method based on the law of magnetic circuit is proposed, which has a high signal-to-noise ratio (SNR) and high spatial resolution in quantization. Based on this method, a novel sensor consisting of a ring magnet, a T-shaped yoke, and a sensor array is designed, which achieves imaging defects in all directions with the same sensitivity. Three typical defects, including pit, cylindrical, and conical defects, are simulated to explore the relationship between the difference value of the magnetic flux density and the sizes (depth and radius) of these typical defects. Then, the defect reconstruction algorithm is proposed. Furthermore, we analyze the factors that affect the sensor including the relative position of the sensor and the defect, the sensor’s lift-off, and tilt. In addition, the sensor’s imaging ability to more complex defects is explored. Finally, the experimental study carried out verifies our designed sensor. [ABSTRACT FROM AUTHOR]

For the electromagnetic eddy current (EC) testing, various methods have been proposed for reducing the lift-off error on the measurement of samples. In this article, instead of eliminating the measurement error caused by the lift-off effect, an algorithm has been proposed to directly measure the lift-off distance between the sensor and nonmagnetic conductive plates. The algorithm is based on a sample-independent inductance (SII) feature. That is, under high working frequencies, the inductance is found to be sensitive to the lift-off distance and independent of the test piece under an optimal single high working frequency (43.87 kHz). Furthermore, the predicted lift-off distance is used for the thickness prediction of the nonmagnetic conductive samples using an iterative method. Considering the EC skin depth, the thickness prediction is operated under a single lower frequency (0.20 kHz). As the inductance has different sensitivities to the lift-off and thickness, the prediction error of the sample thickness is different from that of the lift-off distance. From the experiments on three different nonmagnetic samples—aluminum, copper, and brass, the maximum prediction error of the lift-off distance and sample thickness is 1.1 mm and 5.42%, respectively, at the lift-off of 12.0 mm. [ABSTRACT FROM AUTHOR]

Eddy current sensors can be used to test the characteristics and measure the parameters of the conductive samples. As the main obstacle of the multifrequency eddy current (MEC) sensor, the liftoff distance affects the effectiveness and accuracy of the measurement. In this article, a material-independent algorithm has been proposed for the restoration of the liftoff distance when using the MEC sensor, which is based on the approximation under the thin-skin effect. Experiment testing on the performance of the proposed method is presented. The results show that from the dual-frequency inductance, the liftoff distance could be restored with a maximum error of 0.24 mm for the distance up to 12 mm. Besides, the derived liftoff distance is used for the inversion of the magnetic permeability. Based on a liftoff insensitive inductance (LII) feature, the magnetic permeability of steels can be inversed in an iterative manner, with an error of less than 0.6 % for the liftoff distance up to 12 mm. [ABSTRACT FROM AUTHOR]

A new technique to calculate an equivalent indirect hysteresis loop, $B_{\mathrm{ ec}}$ – $H$ loop, from the distortion of the voltage measured in the magnetic excitation coil is proposed. The aim is to facilitate the industrial implementation of magnetic characterization reducing hardware and error sources of the measuring system, as pick-up coils and their conditioning stages are not necessary. With the $B_{\mathrm{ ec}}$ – $H$ technique, the characterization of two sets of materials with very different properties using a simplified electromagnetic system has been achieved. Specifically, the change in the microstructure of annealed low-carbon steel samples was monitored and the hardness of ultrahigh steel sheets was characterized. Moreover, the conventional hysteresis cycles and $B_{\mathrm{ ec}}$ – $H$ loops are compared, and the obtained lineal relationships between the parameters derived from conventional hysteresis and $B_{\mathrm{ ec}}$ – $H$ loops verify that the $B_{\mathrm{ ec}}$ – $H$ loops can provide equivalent parameters for magnetic characterization of steel samples as conventional hysteresis loops. [ABSTRACT FROM AUTHOR]

Detecting defects in metal structures quickly and robustly is critical to maintaining safe operation in industry. This work discloses an eddy current (EC) testing probe with coils carrying three-phase currents as excitation and integrated array tunnel magnetoresistance (TMR) sensors measuring the magnetic field as receiver. The three-phase currents in the planar layout coils induce EC that migrates electrically in the material under test. This excitation method does not require multiplexing of the coils and is sensitive to defects of any orientations. The TMR array contains 64 sensors, which are microfabricated and packaged in a line. Within single probe pass, the sensors generate magnetic field image with high sensitivity ($\text{1.99}\;\text{nT}/\sqrt {\text{Hz}} @\text{30}\;\text{kHz}$) and fine spatial resolution (0.5 mm pitch). By analyzing the output image, defects are identified and localized. The operating principle of the probe was investigated based on a finite-element method model. A prototype probe was developed and tested, with which machined defects in aluminum samples were inspected. Experimental results show that the probe has comparable sensitivity for horizontal and vertical defects, and it can recognize a small defect with length × width × depth = 1 × 0.2 × 1 mm3. The defects are located within 1 mm with an artificial neural network. [ABSTRACT FROM AUTHOR]

With the rapid increase of the integration and complexity of industrial components, the inaccessibility and inapplicability of existing nondestructive testing devices have become a bottleneck for in situ inspection of these objects. This article introduces a miniaturized active thermography system featured with a small-size, low-resolution, and low-cost thermal sensor, where two optional excitation sources including flash and laser are integrated. Dedicated data analysis approaches to evaluate defects are proposed considering the degraded signal quality. Three carbon fiber-reinforced polymer laminates with a variety of defects are evaluated quantitatively and qualitatively using the proposed system by comparing with two existing nonminiaturized inspection systems. The results show that the proposed system can work effectively for the degradation assessment of composite laminates. Even with the technical limitations that affect the detectability, for instance, the low pixel resolution, this technique will play an important role to inspect components featured with geometrically intricate space. [ABSTRACT FROM AUTHOR]

This work demonstrates the ultrasonic imaging ability of piezoelectric micromachined ultrasonic transducer (PMUT) for the detection of defects deep in solids by total-focus imaging algorithm. The 3-MHz PMUT array uses thin-film PZT as the material for energy transformation because of its high piezoelectric coefficient. Six columns with 12 PMUT units each exhibit an acoustic pressure of 137 kPa measured in water at 1 cm away when driven by a 27-Vpp input. Butter is chosen experimentally as the coupling agent to solids because of its low noise level and short cycling down. Through multilevel processing of echoes and total-focus algorithm, the 2-D image of a graphite plant was obtained with four holes identified based on our customized impedance-matched imaging system. The 16 columns of PMUT array with an area of 5.8 mm × 4.2 mm exhibit the imaging ability of an area of 40 mm × 40 mm in the graphite plant with identified defects deep as 3 cm. These results indicate that PMUT has the detection and imaging ability for defects deep in solids, not merely surface within hundreds of micrometers, and it has the potential for 3-D imaging, especially those occasions in limited space. [ABSTRACT FROM AUTHOR]

Eddy current testing (ET) and eddy current thermography (ECT) are both important nondestructive testing methods that have been widely used in the field of conductive materials evaluation. Conventional ECT systems have often employed to test static specimens even though they are inefficient when the specimen is large. In addition, the requirement of high-power excitation sources tends to result in bulky detection systems. To mitigate these problems, in this article, a moving detection mode of multiphysics structured ET and ECT is proposed in which a novel L-shape ferrite magnetic yoke circumambulated with array coils is designed. The theoretical derivation model of the proposed method is developed which is shown to improve the detection efficiency without compromising the excitation current by ECT. The specimens can be speedily evaluated by scanning at a speed of 50–250 mm/s while reducing the power of the excitation current due to the supplement of ET. The unique design of the excitation-receiving structure has also enhanced the detectability of omnidirectional cracks. Moreover, it does not block the normal direction visual capture of the specimens. Both numerical simulations and experimental studies on different defects have been carried out and the obtained results have shown the reliability and detection efficiency of the proposed system. [ABSTRACT FROM AUTHOR]

Because of its flexibility, high strength, and durability, steel wire rope (SWR) is widely used in irrigation works, bridges, harbors, tourism, and many industrial fields as a vital component. Thus, it can cause accidents and economic losses if local flaws (LFs) of the SWR in service are not detected in time. This article points out two major problems in magnetic flux leakage (MFL) imaging-based nondestructive testing for fault diagnosis of SWR and proposes an integrated signal-processing method specifically designed for addressing the two problems. In this article, the MFL signals are collected by a detector that is formed by a set of permanent magnets and a Hall sensor array. Based on these multichannel MFL signals obtained from the Hall sensor array, we use the principle of multichannel signal fusion to determine rich information from all MFL signals. We solve the strand noise problem by an oblique-directional resampling and filtering method, which avoids severe attenuation in the LF signal. Moreover, the shaking noise is effectively removed by the proposed antishaking filtering based on the median filter. According to our simulation and experiment, the proposed fault diagnosis method for SWR significantly improves the performance of LF detection and localization under strong shaking and strand noises. [ABSTRACT FROM AUTHOR]

Nondestructive testing of substation grounding grids is an issue that has increasing importance. The traditional EMI method transforms the condition of the undergrounding conductors to the surficial induced electric signal in the sensing coil. However, The EMI signals excited by multiple coexisted faults combining with other unknown noises surrounding the substation often cause the failure of detection. Therefore, the observed EMI signals rather complex and cannot be used directly. To address this problem, the separation of individual signatures from the mixture is posed as an SCBSS problem. To extract the induced signal, an EEMD-based EMI method is proposed. The desired signal is then reconstructed to visualize the structure of the grounding grids by a virtual instrument that consists of DAQ and digital signal processing modules. The numerical simulation and practical experiments are employed. The results show the proposed method can be used to effectively detect the topological structure of grounding grid in real substations' electromagnetic environment.

Machine learning methods play an important role in the nondestructive testing field for quality assessment of polymer composites. As a popular deep learning branch, a generative adversarial network is introduced to the thermography field as an image augmentation approach to improve its defect detection performance. Specifically, a generative principal component thermography (GPCT) method for defect detection in polymer composites is proposed. By employing the data augmentation strategy, more informative images are generated to enlarge the diversity of the original set of images. The defect detection results can be visualized using a number of interpretable features. Consequently, the defect detection performance of thermographic data analysis can be enhanced to some extent. The experimental results on a carbon fiber reinforced polymer specimen demonstrate the feasibility and advantages of the GPCT method. [ABSTRACT FROM AUTHOR]

Recently, nonmetallic materials, which are resilient to corrosion, low cost, and lightweight, have been exploited in many industrial sectors such as oil and gas field. A common application of them is in the form of pipes. Despite the advantages, these pipes can be damaged by delamination, defects, holes, and erosion, which may lead to major production failures or even environmental catastrophes. To prevent these issues, nondestructive testing (NDT) of these pipes is required to replace these components in a timely manner. Due to the fact that the traditional NDT methods are mostly effective for metallic pipes, microwave holographic imaging is proposed for inspection of multiple concentric nonmetallic pipes. Besides, standardized minimum norm (SMN) is used to mitigate the depth biasing problem in the proposed imaging technique. To reduce the complexity of the imaging system, we aim at using the narrowest possible frequency band using an array of receiver antennas. The performance of the proposed NDT method is validated through simulation and experimental data in two scenarios including the antennas placed outside the pipes and inside the pipes. [ABSTRACT FROM AUTHOR]

Nondestructive testing (NDT) for ferromagnetic material under motion condition is a challenging topic, because the distribution of the magnetic fields inside the ferromagnetic material is more complicated due to motion-induced eddy current (MIEC) and ferromagnetic material magnetization. Therefore, a fast, accurate, and automatic NDT technique for quantitative detection of crack in the moving ferromagnetic material is an urgent issue. Currently, the pulsed eddy current (PEC) technique is widely applied in quantitative crack characterization in metals; however, the literature focus on the investigation of the velocity influence on PEC is rare. This article performs a deep investigation on the velocity effect of PEC technique by numerical simulation and experiments, and the relationships of speed and PEC signal are discovered. The results show that the velocity effect can change the baseline value of the detection signal in the PEC detection system. Moreover, the crack location, width, and depth can be characterized by PEC with high speed when crack appears in the high-level stage of the excitation signal, and in contrast to static PEC testing, motion detection has a stronger capability to characterize the crack depths. Based on the results, it is beneficial to propose a new high-speed inspection technique for crack characterization in ferromagnetic material components, such as rotating metal components, pipelines, and rail tracks. [ABSTRACT FROM AUTHOR]

The detection of the strain-induced martensitic transformation with the magnetic Barkhausen noise (MBN) was investigated in the high-cycle fatigue (HCF) of the AISI 304L stainless steel at different points of the surface of an hourglass-type specimen. Load-controlled fatigue tests with loads below and above the fatigue limit were performed. Each fatigue test was interrupted at certain numbers of cycles, prior to fracture, and MBN measurements were performed without applied loading. Comparing with the initial condition of the steel under study, no large changes in the MBN measurements were detected in the specimens subjected to loads near the fatigue limit, i.e., no $\alpha '$ -martensitic transformation was detected. In the specimen subjected to the greatest load, it was possible to detect an uncommon distribution of the MBN signals, which are correlated with the strain-induced $\alpha '$ -martensite and were verified with optical microscopy and X-ray diffraction (XRD). The results show that the MBN is a sensitive technique to detect the strain-induced $\alpha '$ -martensite in HCF tests, which can be used as an indirect parameter to show the fatigue damage of materials. [ABSTRACT FROM AUTHOR]

A microwave crack detection method using circularly polarized synthetic aperture radar (SAR) imaging is described. The advantages of circular over linear polarization, mainly that the crack detection is not dependent on the relative orientation of the crack, is discussed. A technique to extract the orientation of a detected crack using SAR images is presented, where the phase difference between the left- and right-hand circular polarizations (LHCP and RHCP) images is used for this purpose. The orientation estimation error introduced by a nonzero crack width is discussed and demonstrated through simulation, and a technique to overcome this error is proposed and demonstrated. The effect of the axial ratio on orientation error is also studied. Subsequently, a dual-circularly polarized antenna was designed, fabricated, and used to image several metal samples containing narrow electrical discharge machining (EDM) notches and real cracks, by which the detection and orientation estimation techniques are successfully demonstrated. [ABSTRACT FROM AUTHOR]

In the context of nondestructive testing (NDT), this article proposes an inverse problem approach for the reconstruction of high-resolution ultrasonic images from full matrix capture (FMC) data sets. We build a linear model that links the FMC data, i.e., the signals collected from all transmitter–receiver pairs of an ultrasonic array, to the discretized reflectivity map of the inspected object. In particular, this model includes the ultrasonic waveform corresponding to the response of transducers. Despite a large amount of data, the inversion problem is ill-posed. Therefore, a regularization strategy is proposed, where the reconstructed image is defined as the minimizer of a penalized least-squares cost function. A mixed penalization function is considered, which simultaneously enhances the sparsity of the image (in NDT, the reflectivity map is mostly zero except at the flaw locations) and its spatial smoothness (flaws may have some spatial extension). The proposed method is shown to outperform two well-known imaging methods: the total focusing method (TFM) and Excitelet. Numerical simulations with two close reflectors show that the proposed method improves the resolution limit defined by the Rayleigh criterion by a factor of four. Such high-resolution imaging capability is confirmed by experimental results obtained with side-drilled holes in an aluminum sample. [ABSTRACT FROM AUTHOR]

Nondestructive testing (NDT) is an economical way of detecting subsurface defects in composite products. Infrared thermography serves as a popular NDT method due to its high efficiency and low cost. However, defect identification by directly visualizing thermal images is difficult owing to the nonuniform background and noise. Recently, data analysis methods have been introduced to thermal image processing, including principal component analysis, which is known for its good performance in dimensionality reduction, feature extraction, and noise reduction. However, most of these methods can only extract linear features. In this article, a multiview learning-based autoencoder, which can process not only nonlinear features but also sequential attributes, is utilized in thermographic data analysis. After extracting the low-dimensional features by multiview learning, a background elimination step is conducted to highlight the locations and shapes of the defects. The experimental results demonstrate the feasibility of the proposed method. [ABSTRACT FROM AUTHOR]

Wire rope is a chain structure, and the strength of the weakest link is the strength of the entire wire rope. The axial resolution of loss of metallic cross-sectional area (LMA) detection using the induction coil is related to the length of the detection probe, which is usually greater than or equal to the length of the probe. Improving the axial resolution is an important technical indicator of the LMA. In this article, through theoretical analysis based on the principle of magnetic aggregation and 3-D transient magnetic field simulation, a particular kind of magnetic aggregation structure used for winding coil is proposed and optimized. Simulation and experimental results show that for wire rope LMA detection adding the proposed magnetic aggregation structure, the defect signals’ signal-to-noise ratio (SNR) is improved and the axial resolution of the LMA is increased to 20 mm. At the same time, the cross-sectional area loss can be quantitatively evaluated by the magnetic flux peak-to-peak value. The difference between the maximum and minimum values of the derivative of the magnetic flux signal can be used to evaluate the defect length. [ABSTRACT FROM AUTHOR]

The subspace learning (dimensionality reduction) algorithms have played an important role in the analysis of thermographic data: a key step in infrared thermography-based nondestructive testing of subsurface defects in composite materials. However, one of its branches, manifold learning, with excellent ability to preserve local data structure, is rarely applied. In this article, a spatial-neighborhood manifold learning (SNML) framework is proposed for thermographic data analysis. Different from traditional manifold learning methods, SNML uses the spatial-neighborhood information instead of the traditional k-nearest neighbors, or $\varepsilon$ -neighborhood, to construct the adjacency graph. This overcomes the difficulty of parameter selection and extracts local features in images in a more reasonable way. Additionally, the data preprocessing step and the means of thermographic data normalization in the proposed framework are discussed. For performance comparison, three traditional manifold learning methods are also implemented. The experiments on carbon fiber-reinforced polymer specimens demonstrate the validity and feasibility of SNML. [ABSTRACT FROM AUTHOR]

A systematic computational method to simulate and detect subsurface flaws, through nondestructive transient thermography, in aluminum (AL) sheets and friction stir (FS) welded sheets is proposed in this article. The proposed method relies on feature extraction methods and a data-driven machine learning modeling structure. Here, we propose the use of a multilayer perceptron feed-forward neural network with feature extraction methods to improve the flaw-probing depth of transient thermography inspection. Furthermore, for the first time, we propose thermographic signal linear modelling (TSLM), a hyper-parameter-free feature extraction technique for transient thermography. The new feature extraction and modeling framework was tested with out-of-sample experimental transient thermography data, and results show effectiveness in subsurface flaw detection of up to 2.3 mm deep in AL sheets [99.8% true positive rate (TPR) and 92.1% true negative rate (TNR)] and up to 2.2  mm deep in FS welds (97.2% TPR and 87.8% TNR). [ABSTRACT FROM AUTHOR]

Pillars represent some of the commonest supporting elements of modern and historical buildings. Nondestructive testing methods can be applied to gain information about the status of these structural elements. Among them, ground penetrating radar (GPR) is a popular diagnostic tool for the assessment of concrete structures. Despite several theoretical and experimental studies on concrete structural evaluation by GPR have been reported, little work has been done so far with respect to pillars. Owing to their circular geometry, pillars are complex multiscattering environments, which render the interpretation of the radar images very challenging. This article deals with the application of radio frequency tomography as a nondestructive technique for imaging the inner structure of pillars. The main goal of the study is the assessment of the imaging performance that can be obtained in comparison to conventional GPR exploiting a multimonostatic configuration. Accordingly, potentialities and performance of multimonostatic and multiview/multistatic measurement configurations are herein investigated in the inverse scattering framework. For each measurement configuration, the regularized reconstruction of a point-like target and the spectral content are evaluated. The data inversion is carried out by means of the truncated singular value decomposition scheme. Tomographic reconstructions based on full-wave synthetic data are shown to support the comparative analysis. [ABSTRACT FROM AUTHOR]

This article presents a bistatic reflection-based imaging technique for electromagnetic testing of conductor-backed composite panels, which is based on an inverse formulation of the numerical problem at hand, and technically extends a previously demonstrated transmission-based system. The technique exploits the a priori assumption of pixel-based sparsity and retrieves the final image using data from only a single measurement, thereby removing the necessity of a reference measurement to be conducted. To demonstrate the capability of the technique, retrieved images are presented for a synthetic proof-of-concept device under test and an industrially manufactured composite panel. The presented technique can be considered as the initial model in the development of more complex bistatic imaging systems and has been developed for the purpose of extending the opportunities to conduct reflection-based electromagnetic nondestructive testing on aircraft structural components. [ABSTRACT FROM AUTHOR]

Conventional nondestructive testing techniques do not suffice to efficiently inspect the defects in multiple nonmetallic pipes. Thus, in this article, we propose a novel method for inspection of these components based on near-field microwave holographic imaging augmented with beamspace transformation. This approach harnesses the ability of microwaves for 3-D imaging by measuring the backscattered fields in the near-field region. In the proposed technique, we augment the solution of near-field holographic imaging through the use of beamspace transformation which allows for focusing on each pipe during the solution process. This improves the quality of the reconstructed images, in particular, for the pipes which are further away from the measuring antennas. First, we demonstrate the performance of the proposed imaging technique using simulations. Then, we present the experimental results for imaging of concentric cylindrical polyvinyl chloride (PVC) pipes using wideband microwave data. [ABSTRACT FROM AUTHOR]

This article presents an eddy current probe with array tunnel magnetoresistance (TMR) sensors for inspection of the steam generator tube of the nuclear power plant. The TMR sensors are integrated on a circular printed circuit board (PCB) with the sensitive axis along the radius directions. The dimensions of each TMR sensor in the array are length (0.45 mm) $\times $ width (0.45 mm). Fine spatial resolution magnetic field imaging can be generated as the size of the sensor is small. The operating principle of the probe is based on Faraday’s law, where eddy current is induced in the tube wall by an excitation coil carrying ac current, and then, the disturbance of the eddy current is monitored in terms of the magnetic field measured by the TMR sensors. Thanks to the high sensitivity and fine spatial resolution of the TMR sensors, the probe is capable of detecting outer diameter (OD) defect with an effective size 0.127 mm $\times \,\, 0.436$ mm. Moreover, when the probe scans in a defect-free area, the variation of lift-off of the probe to the inner surface of the tube wall does not bring in noises, which is important for maintaining a high signal-to-noise ratio in the practical inspection. The operation principle of the probe was studied based on a finite-element method (FEM) model, and then, a prototype unit was developed and tested. The experimental results demonstrated the feasibility of the concept and validated that the probe has excellent ability to detect various defects. [ABSTRACT FROM AUTHOR]

Nondestructive testing for identifying defects on the surface of metal materials is important for industries and infrastructures. The Rayleigh wave is widely used for detecting these surface defects. For replacing piezoelectric transducers with electromagnetic acoustic transducers (EMATs) for the surface inspection of metal materials, this article proposes a new magnet and coil combination consisting of a periodic-permanent-magnet (PPM) and a returned dislocation meander line coil. The returned dislocation meander line coil was developed using a traditional meander line coil, whose wires return from one side to another and shift for a certain distance. A 2-D finite-element simulation was conducted to analyze the performance of the proposed Rayleigh wave EMAT. The simulation results revealed that, compared with a large conventional magnet, the PPM increased the maximum magnetic flux density, and made the magnetic flux density distribution more concentrated on the specimen’s surface, particularly below the coil. In the middle part of the coil, the PPM greatly increased the intensity of the horizontal magnetic field. Additionally, the returned dislocation meander line coil made full use of the strong magnetic field below the center of each small magnet and at the adjacent magnets. The designed Rayleigh wave EMAT was fabricated, and the experimental results revealed that the new design of the Rayleigh wave EMAT increased the received signal by 57.9% compared with the conventional Rayleigh wave EMAT. [ABSTRACT FROM AUTHOR]

This article presents improvements to ultrasonic imaging of solid platelike structures using the minimum-variance distortionless response (MVDR) beamforming processor. The primary application of this work is in the nondestructive testing (NDT) of platelike components that are widely used in aerospace, marine, and civil structures. The study proposes a new set of weights, or MVDR replica vectors, that are based on the physics of the propagating Lamb modes, including the symmetric mode S0, the antisymmetric mode A0, and the shear horizontal mode SH0. Numerical results show that these wave-mode weights, combined with geometrical spreading, improve the focus of the array by increasing the dynamic range and the spatial resolution of the image. In addition, quite dramatic improvements in image quality are achieved by combining, or compounding, the multiple Lamb modes naturally present in the plate in both transmission and reflection. As shown in recent work applied to bulk waves in 3-D solids, the compounding of Lamb modes in plates increases the array gain without increasing its physical aperture. [ABSTRACT FROM AUTHOR]

In this paper, we introduce a synthetic aperture radar (SAR)-based millimeter-wave imaging system for high-resolution imaging of human skin, in addition to traditional nondestructive testing applications. The imaging system, which operates in the 58–64-GHz frequency range, forms the basic building block of a future millimeter-wave camera suitable for imaging burned skin and skin cancers. The system is composed of a coherent transceiver and two antennas, principally enabling a quasi-monostatic measurement configuration. The designed antenna is an antipodal Vivaldi antenna with 5.0–8.0-dB gain and half-power beamwidths of ~79.0° and ~76.0°in its $E$ – and $H$ -planes, respectively. The transceiver is designed using a complete system-on-chip transmitter and receiver capable of $I/Q$ detection. The fully designed imaging system has 47 dB of dynamic range and is phase-calibrated using a robust wideband calibration procedure based on single-frequency SAR imaging approach. Examples of images produced with this system include burned pigskin skin and a small balsa wood panel with four small embedded rubber pieces. [ABSTRACT FROM AUTHOR]

A microwave crack detection method using circularly polarized synthetic aperture radar (SAR) imaging is described. The advantages of circular over linear polarization, mainly that the crack detection is not dependent on the relative orientation of the crack, is discussed. A technique to extract the orientation of a detected crack using SAR images is presented, where the phase difference between the left- and right-hand circular polarizations (LHCP and RHCP) images is used for this purpose. The orientation estimation error introduced by a nonzero crack width is discussed and demonstrated through simulation, and a technique to overcome this error is proposed and demonstrated. The effect of the axial ratio on orientation error is also studied. Subsequently, a dual-circularly polarized antenna was designed, fabricated, and used to image several metal samples containing narrow electrical discharge machining (EDM) notches and real cracks, by which the detection and orientation estimation techniques are successfully demonstrated. [ABSTRACT FROM AUTHOR]

The development of eddy current nondestructive techniques and instruments is moving toward new challenges in detecting buried and smaller defects and minimizing the amplitude of excitation currents. The smaller the size of the detected defect, the greater is the diagnostic capability of these techniques. Moreover, the lower the excitation current required, the longer the battery discharge time of the portable instruments, the greater the possibility of implementing broadband excitation strategies with reliable results. This feature is also important when portable instruments with array of probes have to operate for a large amount of time. Following the actual trends based on the use of double-coil excitations, this paper proposes the investigation of the performance of these kinds of probes, when small and buried defects are involved, and low amplitudes of the excitation current are adopted. To allow these activities a suitable double-coil-based excitation probe has been assembled. The obtained performance has been also compared with a traditional single-coil excitation probe based on the same sensing element. The experimental analysis demonstrated the appreciable performance of the proposed solution in a number of test cases. [ABSTRACT FROM AUTHOR]

Time-domain plane-wave imaging (PWI) has recently emerged in medical imaging and is now taking to nondestructive testing (NDT) due to its ability to provide images of good resolution and contrast with only a few steered plane waves. Insonifying a medium with plane waves is a particularly interesting approach in 3-D imaging with matrix arrays because it allows to tremendously reduce the volume of data to be stored and processed as well as the acquisition time. However, even if the data volume is reduced with plane wave emissions, the image reconstruction in the time domain with a delay-and-sum algorithm is not sufficient to achieve low computation times in 3-D due to the number of voxels. Other reconstruction algorithms take place in the wavenumber–frequency (f-k) domain and have been shown to accelerate computation times in seismic imaging and in synthetic aperture radar. In this paper, we start from time-domain PWI in 2-D and compare it to two algorithms in the f-k domain, coming from the Stolt migration in seismic imaging and the Lu theory of limited diffraction beams in medical imaging. We then extend them to immersion testing configurations where a linear array is facing a plane water–steel interface. Finally, the reconstruction algorithms are generalized to 3-D imaging with matrix arrays. A comparison dwelling on image quality and algorithmic complexities is provided, as well as a theoretical analysis of the image amplitudes and the limits of each method. We show that the reconstruction schemes in the f-k domain improve the lateral resolution and offer a theoretical and numerical computation gain of up to 36 in 3-D imaging in a realistic NDT configuration. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the effect of frequency uncertainty in signals generated or measured by a microwave instrument on the resulting synthetic aperture radar (SAR) images, particularly for nondestructive testing (NDT) applications. Wideband SAR imaging systems measure reflections from a target by irradiating it with locally generated signals that can potentially have some level of frequency uncertainty. Quantifying this frequency uncertainty provides the user with a realistic and expected level of image distortion which may manifest itself as blurring, noise artifacts, etc. In this paper, we show that as uncertainty in the actual frequency value increases, the level of image distortions increases predominantly for distant targets. This is an important fact for NDT applications since the imaged object is commonly close to the imaging system. In addition, these imaging system usually have a limited “aperture” size, which makes target distance an important consideration. For complex targets, we show how frequency uncertainty-based image distortions can dominate features in an image depending on the reflected signal amplitude from the target. We also show that in real imaging systems, the statistical distribution of frequency uncertainty combined with practical, near-target ranges (distances) produce nondiscernible image distortions. [ABSTRACT FROM AUTHOR]

Nondestructive testing is of great significance for preventing fracture accidents of porcelain postinsulators, especially coated with room temperature vulcanized (RTV) silicone rubber. In this paper, the feasibility of pulsed thermography for defects detection of porcelain postinsulators was investigated. 110-kV porcelain postinsulators with crack defects were tested by utilizing the investigated approach. The results show that crack defect as small as 0.4 mm under the RTV silicone rubber coating with the thickness of 1 mm can be accurately identified. Homomorphic filtering method with Fourier transform was applied to correct the nonuniform heating and improve the defect contrast in the thermal images. With the advantages of simple operation, contactless measurement, full-field imaging, and quantitative defect estimation capability, the pulsed thermography would have promising applications in live detection of porcelain postinsulators. [ABSTRACT FROM AUTHOR]

Barely visible impact damage (BVID) is inevitable during either fabrication or lifetime of a carbon fiber reinforced plastic (CFRP) component. These flaws are usually difficult to be detected from the surface by visual inspection or machine vision based on a charge-coupled device or CMOS. In order to solve the problems of low efficiency, low sensitivity, and small detection area of the existing nondestructive testing (NDT) for BVID in CFRP, this paper proposes for the first time the integrated nonlinear ultrasound (NU) and vibrothermography (VT) NDT based on the shared excitation sources. The experimental system was built after introducing the principle of shared excitation based NU&VT NDT. The CFRP plates with 5, 15, and 25 J visible impact damage (VID) as well as 12 and 16 J BVID were tested using the integrated NU&VT. Experimental studies after signal processing have shown that all VID and BVID could be detected by the integrated NU&VT NDT, and the defection capability has a significant improvement after fast Fourier transform. The proposed method could provide a visualized and effective means for quality control and inspection of large-scaled and complex shape key components in manufacturing process and in service. [ABSTRACT FROM AUTHOR]