Journal of Nondestructive Evaluation最新文献

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.

Rapid and reliable automated identification of wood species can be a boon for applications across wood scientific context including forestry and biodiversity conservation, as well as in an industrial context via requirements for timber trade regulations. However, robust machine learning classifiers must be properly analyzed and immunized against domain shift effects. These can degrade the automated system performance for input data variations occurring in many real-world scenarios. This work methodologically analyses the domain shift generated by using two differing sub-micro-scale and micro-scale computed tomography setups in the focused context of deep learning based binary wood classification from volumetric image data. To counteract this, we examine several mitigation strategies and propose primary data-level and narrow model-level strategies to effectively and successfully minimize the performance domain gap. Core elements of the data-wise strategy include the combined usage of phase-correction methods, low-pass pyramid representation of the data and adjustments of model normalization and regularization. Vanishing domain performance differences led to the conclusion that the combined strategy ultimately prompted the model to learn robust features. These features are discriminative for the utilized wood species data from both sub-micro-system and micro-system domains, despite the substantial differences in data acquisition setup that propagate into fundamental image quality metrics like resolution, contrast and signal-to-noise ratio.

The insulation of aviation cables is critical to aircraft safety but is vulnerable to defects such as cracks, ruptures, slices, and swelling. Reliable nondestructive testing (NDT) of these defects is challenging due to environmental interference, noise, and the limitations of existing inspection techniques. This work presents a novel NDT approach integrating reflective digital in-line holography with a Combined Anisotropic Total Variation (CATV) reconstruction algorithm and an Xception-based deep transfer learning model. The CATV reconstruction suppresses twin-image artifacts and preserves structural detail, enabling the generation of a phase-map dataset of multiple defect types. Using this dataset, the Xception-based classifier achieved 98% accuracy, surpassing state-of-the-art approaches. The contributions of this work are: (i) using CATV-based reconstruction for reflective holography of aviation cables, (ii) creating a phase-map dataset of insulation defects, and (iii) demonstrating the feasibility of a high-precision, non-contact inspection method for aviation safety applications.

A clinch point’s quality is usually assessed using ex situ destructive testing methods. These, however, are unable to detect phenomena immediately during the joining process. For instance, elastic deformations reverse and cracks close after unloading. In situ methods such as the force-displacement evaluation are used to investigate a clinching process, though deviations in the clinch point geometry cannot be derived with this method. To overcome these limitations, the clinching process can be investigated using in situ computed tomography (in situ CT). When investigating the clinching of aluminum parts in in situ CT, the sheet-sheet interface is hardly visible. Earlier investigations showed that radiopaque materials can be applied between the joining parts to enhance the detectability of the sheet-sheet interface. However, the layers cause strong artefacts, break during the clinching process or change the clinch joint’s properties significantly. In this paper, a minimally invasive method to enhance the interface detectability is presented. First, the aluminum oxide layer is removed by etching. Second, the specimen is electroplated with copper or gold, respectively. In some cases, a mask is applied to create a cross-shaped plating pattern. Then, the plated specimen is clinched with a non-plated counterpart and the interface detectability of the clinch points is assessed in CT scans. It is shown that a copper plating of 2.6–4 μm can visualize some parts of the interface, while 7–9 μm is suitable to enhance the detectability of the sheet-sheet interface almost continuously.

This study presents a novel vision-based methodology for damage detection in CFRP composite beams, combining optical flow analysis, statistical anomaly scoring, and deep learning (DL) models. Composite materials such as CFRP are widely used in structural applications due to their high strength-to-weight ratio, yet detecting internal damage remains a significant challenge. To address the limitations of traditional non-destructive evaluation methods, this study integrates non-contact optical flow techniques with a hybrid anomaly detection pipeline. The Lucas-Kanade optical flow method is used to extract displacement time series from video recordings of vibrating structures. These displacement signals are transformed into spectrograms using Short-Time Fourier Transform (STFT), and frequency-domain features are enhanced with added Gaussian noise to improve model robustness. Principal Component Analysis (PCA) is applied to reduce the dimensionality of the spectrogram features, and Mahalanobis Distance is computed to quantify deviations from the healthy state. The resulting Mahalanobis Distance time series is then used as input for three DL architectures—Autoencoder, Convolutional Neural Network (CNN), and Long Short-Term Memory (LSTM)—which are trained to detect structural anomalies based on reconstruction error or pattern recognition. The proposed approach is experimentally validated on CFRP composite beams under multiple damage scenarios. Results show that leveraging Mahalanobis-based statistical features within DL models significantly improves anomaly detection accuracy, offering a robust and scalable framework for real-time structural health monitoring in civil, aerospace, and automotive domains.

Accurately picking acoustic emission (AE) arrival times remains a significant challenge, particularly for low signal-to-noise ratio (SNR) signals where manual picking is subjective and unreliable. This article introduces an improved manual picking method for AE arrival times, developed by integrating sensor acquisition principles with wave velocity attenuation laws. This method provides a derivation formula that enables the determination of “ground truth” arrival times for low SNR signals by leveraging characteristics from high SNR signals. These derived values serve as labels to train a two-dimensional convolutional neural network (2D CNN) for automated arrival time picking. A key innovation is converting the one-dimensional AE signal directly into a two-dimensional matrix using a transformation matrix as the CNN’s input, thereby significantly streamlining preprocessing by eliminating the need for additional feature extraction. The labeled 2D matrices are then fed into the 2D CNN for training to enhance its ability to recognize crucial temporal patterns. Finally, the AIC algorithm picks the arrival times picked from the CNN-processed signals. A major advantage of CNNs in this context is that it does not require additional feature extraction and can extract features from the original elements. In addition, it can identify high-order statistics and nonlinear correlations of images. The third convolutional neuron can process data in its receptive domain or restricted subregion, reducing the need for a large number of neurons with large input sizes and enabling the network to be trained more deeply with fewer parameters. Results demonstrate that the proposed method significantly outperforms mainstream detection methods, including AIC and Floating Threshold (FT), achieving high accuracy and stability, particularly in scenarios with limited data and low SNR.

X-Ray Computed tomography (XCT) has become an important non-destructive quality assurance technique in industry. Consequently, standards for quality insurance of XCT and on its performance are required to support industrial XCT users for reliable production. This performance is determined by analysis of the quality of the images produced and by the dimensional measurement accuracy achieved for a given XCT parameter setting. Until recently, standards assessed image quality solely in terms of contrast sensitivity and spatial resolution. Detection limits could not be predicted until now. A new term is introduced: The Detail Detection Sensitivity (DDS). It depends on the contrast sensitivity as a function of contrast and noise, and on the spatial resolution. The spatial frequency needs to be implemented into the analysis to consider sensitivity as a function of the size of an indication. The contrast sensitivity is quantified by the Contrast Discrimination Function (CDF) and the spatial resolution by the Modulation Transfer Function (MTF). The numerical DDS is determined for air flaws from the Contrast Detection Diagram (CDD) at 100% contrast. However, some XCT operators prefer visual determinations rather than numerical ones. To face this need, the SensMonCTII project proposes a new Image Quality Indicator (IQI), consisting of a disk with holes of different sizes for visual DDS determination. The project aims to produce a new ISO standard draft providing a practice to evaluate numerically the XCT image quality via MTF, CDF, CDD and DDS, as well as to evaluate visually the DDS from the hole visibility of a disk IQI. The paper does not address the performance of XCT in terms of dimensional measurement accuracy, but focuses on the performance of XCT in terms of image quality. It describes the methodology to evaluate the image quality, including DDS for the first time.