Journal of Nondestructive Evaluation最新文献

Existing infrared thermography detection of cavitation defects in external prestressed pipelines is characterised by a variety of test conditions, making it difficult to explore the applicable conditions thoroughly by experiment. To address this issue, key parameters for the numerical model of hydration heat transfer in grouting material for prestressed pipes were established through the fitting of simulation experiments and field experiments. Subsequently, simulation models were constructed under various conditions to investigate the factors affecting the detection of void defects using infrared thermal imaging, including the presence or absence of steel strands, the size of void defects, the material of the pipeline, and its wall thickness. Our results demonstrate that the presence of steel strands reduces the defect identification capability, with the maximum contrast (MaxΔT) decreasing by 1.117℃ in high polyethylene (HDPE) pipes with a 100% void area. Galvanized steel (GSP) pipes are more difficult to detect than HDPE pipes due to their lower emissivity, particularly in the case of GSP pipes with a 60% void area, where MaxΔT is reduced by 18.96% compared to HDPE pipes. As the size of the void increases, the defect identification capability gradually enhances, and void defects larger than 26% can be detected. For both types of pipes, as the wall thickness increases, the infrared detection time window gradually narrows, with the most significant reduction observed for 30% void defects. This study serves as a reference and provides a theoretical basis for the infrared thermal imaging detection of cavity defects in externally prestressed pipes.

Deep learning-based super-resolution has shown significant potential for enhancing the resolution of low-resolution X-ray computed tomography (CT), a 3D nondestructive imaging technique. It could accelerate CT scanning by several orders of magnitude, opening new possibilities for high-throughput acquisition, in situ, and low-dose experiments. However, current assessments of the resulting image quality often rely on 2D image quality metrics such as PSNR and SSIM, which may not correlate directly with scientific measurements. In the present study, the relationship between training dynamics and image quality in deep learning super-resolution is investigated. A super-resolution method was applied to a stainless steel lattice structure featuring numerous quantifiable defects, imaged with a laboratory CT. The core contribution lies in employing a 2.5D Mixed-Scale Dense neural Network (MSDnet) on experimental data and evaluating its performance using scientific and task-based metrics–specifically related to porosity and surface roughness–while monitoring training dynamics. The results demonstrate that even a standard loss function can effectively reflect network performance dynamic for such material science applications. The best super-resolution accuracy with a magnification factor of 3 was achieved after 100 epochs, generating less than 2 % of missing pores and only around 15 % average error in pore volume. Additionally, practical considerations are proposed to assist in the design of tailored training strategies.

A new steel pipe detection system with a high lift-off detachable enhanced magnetic moment with targeted magnetic core extension and magnetic field focusing probe and its electronic control centre has been proposed to detect in-service steel pipe damage. The system adopts a parabolic arc-arm coil structure to increase the magnetic moment and achieve the target magnetic core extension, supplemented by a targeted compensation coil for targeted magnetic field focusing. We have also developed a supporting circuit control centre to further expand the detection magnitude of data collection. Experimental verification was conducted on 20# steel pipes under various conditions, including different defect scales, defect circumferential positions, pipe wall thicknesses, and operating environments such as thick cladding under extreme conditions. The results showed that the system achieved a probe lift-off height of up to 4.1 times the pipe diameter, detected defects throughout the entire wall thickness, and could discriminate defect severity, increasing the maximum effective detection distance by 28.23% compared to the previous generation system, it can assist in detection of pipelines with ultra-thick cladding or deeper burial dimensions under extreme operating conditions such as high temperature and deep cold. This study discovered the magnetic moment enhancement effect of targeted magnetic core extension and, based on it, optimised the design of the detection end structure. Combined with its corresponding signal characterisation form and circuit control instrument, it contributes to a new way of in-service pipe detection.

The understanding of processes in heat storage materials and reactors can be greatly improved by the use of non-destructive methods that allows the view inside the objects. The advantage of non-destructive methods is that the sample of interest remains intact, experimental changes can be monitored in-situ, and the experiments are less labor intensive. Alongside others, three of the most utilized non-destructive techniques for heat storage systems are discussed: NMR, X-ray imaging, and neutron imaging. The working mechanism and (dis)advantages of these techniques are discussed alongside various applications and examples. This work aims to provide a handle to researchers working in the field of thermal energy storage on how to investigate heat storage materials and reactors in a non-destructive manner.

The need to optimize processes in terms of quality and productivity has led to an increased demand for inspection tools that make the production process faster and more reliable. In the field of welding, one of the biggest challenges is the identification of internal defects. In this context, advanced non-destructive testing techniques have gained prominence in the inspection of joints due to the geometric or metallurgical characteristics of the inspected material. This study evaluated the use of the magnetic eddy current (MEC) technique as an alternative for the inspection of welded joints in thin sheets of high-performance steels by the resistance seam welding (RSEW) process. MEC tests were performed on welded specimens, taken from the production process of high-performance steel coil rolling, using welding parameters both compliant and non-compliant with the production process. In order to evaluate the effectiveness of MEC technique, hot tensile tests were performed to simulate the thermal cycle of the rolling process in the Gleeble thermomechanical system. The results showed a high correlation between the mechanical performance of the joints and the signals obtained through the MEC technique, enabling a future application in industrial environments. In comparison to conventional NDT techniques, MEC proved to be a fast and non-contact metohod with potential for in-line application.

Robotic X-ray imaging systems enable autonomous inspection of the internal integrity of critical infrastructure. However, these systems often suffer from vibrations and unwanted movements that cause motion blur in the resulting radiographs. The impact of this motion blur is often unknown until the first prototype is available and even then requires extensive experimental testing to assess. In addition, tests involving radiation are time-consuming, demand specialized equipment, and pose inherent safety risks. In this work, we propose using X-ray simulation as a tool to complement and replace real images during the development of robotic inspection systems. Our method extends an existing X-ray simulation framework (gVirtualXray) to generate motion-blurred images from any type of motion, which are then validated against experimental data. The approach is applicable to various robotic systems and we demonstrate its use for a decoupled two-drone inspection system for wind turbine blades. This is one of the most demanding applications due to the high degree of freedom of the system components and relatively long exposure times. The simulator provides insights into the motion blur sensitivity of the design, helping among others, to pinpoint the most significant degrees of freedom that affect image quality. Finally, we highlight the potential of the simulator for early estimation of performance limits, generation of training datasets for machine learning algorithms, and optimization of system design without the need for physical prototypes. Both the datasets and the software implementation are publicly available.

This study presents an novel structural health monitoring (SHM) approach by integrating Digital Image Correlation (DIC) with the YOLOv8 instance segmentation model to quantify crack damage evolution in concrete beams subjected to different preloading conditions. Four-point bending tests were conducted on plain concrete, BFRP-reinforced concrete, and preloaded BFRP-reinforced concrete beams. Our method leverages the model’s pixel-level segmentation capabilities to provide a more granular and continuous tracking of damage progression. A novel Weighted Damage Index (WDI) was developed to quantify the extent and progression of cracking based on the spatial and probabilistic features extracted by the model. The WDI demonstrated a clear correlation with mechanical degradation and effectively characterized three distinct stages of damage: elastic, stable, and unstable. As an interpretable and scalable visual damage metric, WDI shows strong potential for computer-assisted or semi-automated SHM applications, offering a cost-efficient tool to support early warning, maintenance prioritization, and reinforcement strategy optimization. These findings provide a new perspective on integrating vision-based techniques into intelligent infrastructure monitoring.

The train axle has complex structures and works under various non-stationary operating conditions. The acoustic emission (AE) signals of a train axle are complicated and usually polluted by noise and interference. It is difficult to extract effective features of fatigue cracks. In addition, there are some unintelligent fatigue crack identifications for traditional AE-based methods. Aiming at these problems, an intelligent method based on acoustic emission-stacked denoising autoencoder (AE-SDAE) is proposed to identify fatigue cracks. The proposed method leverages deep learning to autonomously extract discriminative features from raw AE data, overcoming the subjectivity and inefficiency of manual feature selection commonly criticized in conventional non-destructive evaluation techniques. The proposed method eliminates the need for manual feature extraction by directly processing raw AE signals through a deep learning network, enabling automated and intelligent crack classification. Experimental validation was conducted using an acoustic emission test bench, where AE signals were collected from train axles under simulated loading conditions. The SDAE network was trained on preprocessed data, and its performance was compared with other models. Results demonstrate that the proposed method achieves a crack identification accuracy of over 98%, significantly outperforming traditional approaches. Using kurtosis-guided segmentation, the framework identifies four crack stages via AE kurtosis jumps, achieving 99.67% accuracy. These experimental results validate the effectiveness of the AE-SDAE method for fatigue crack detection and stage identification in railway axles.

Statistical methods within the Bayesian framework have been widely used to address inverse imaging problems, such as computed tomography (CT) image reconstruction. These methods offer a probabilistic approach that is able to enhance the reconstruction quality by employing regularization methods while enabling uncertainty quantification of the result, providing valuable insights into the reliability of the reconstructed images. However, despite the flexibility and range of techniques within this framework, the computational intensity of this class of approaches is still impractical for large-scale datasets like those in CT. In this manuscript, we introduce a concept for determining the uncertainty caused by the noise in the observed data in CT-based dimensional measurement using a rapid, regularized, Markov Chain Monte Carlo reconstruction technique. This method provides a volumetric model where each voxel is represented by a distribution, which is then transformed into a triplet of gray value models: one for the central value and one each for the upper and lower bounds of the confidence interval. Bi-directional and uni-directional length measurements on results derived from each single-gray-value model, for real CT data, provide a task-specific measurement uncertainty. This method requires significantly less computation and storage capacity compared to classic Monte Carlo simulations by reducing the number of needed simulations for approximating a distribution while incorporating regularization techniques. The results are compared to conventional non-regularized and regularized reconstruction methods, such as Feldkamp–David–Kress (FDK), and state-of-the-art statistical methods, followed by validation of the determined uncertainty in real CT data.

To address the challenge that continuous small leakage signals are easily disrupted by noise, resulting in a low recognition rate for urban pipeline leakage, we propose an improved multivariate variational mode decomposition (IMVMD) fusion machine learning method specifically for the recognition of continuous small leakages in urban pipelines. Building upon the preliminary time–frequency assessment of the original leakage signal, we enhance the MVMD by incorporating the correlation coefficient and normalized Shannon entropy, enabling adaptive decomposition and reconstruction of the leakage signals. We establish a BP neural network based on the IMVMD and a SVM leakage recognition model also based on IMVMD. Random forest (RF) evaluation is employed to identify the signal feature inputs. The results indicate that the signal-to-noise ratio of the reconstructed signal using IMVMD is 55.42% higher than that of the original signal, demonstrating a superior decomposition effect compared to traditional MVMD 、EMD and VMD. RF is utilized to reduce the dimensionality of signal characteristics under various leakage conditions, resulting in the selection of four representative features: root mean square, short-term energy, Margin factor, and waveform factor, which serve as inputs for the BP neural network and SVM leakage recognition model based on IMVMD. The accuracy of signal recognition reaches 98.22% and 97.22%, respectively. Compared to the traditional MVMD decomposition recognition model, this method improves accuracy by 10.72% and 10.22%, respectively, thereby providing reliable support for the detection and precise localization of continuous small leakages in urban pipelines.