Search Results (31)

Facade thermal defect diagnosis is a critical prerequisite for energy-efficiency retrofitting of old residential buildings. However, conventional infrared thermography is easily affected by environmental conditions and occupant behavior, making it difficult to distinguish persistent thermal defects from transient anomalies. To address this challenge, this study proposes an integrated diagnostic framework for old residential buildings in Wuhan, China, combining unmanned aerial vehicle (UAV) infrared thermography, multi-temporal data acquisition, 3D flight-path planning, thermal anomaly recognition, facade spatial mapping, and temporal screening. Field experiments were conducted to determine key acquisition parameters, including sensor preheating time, imaging distance, and acquisition timing. Thermal anomalies were identified through image-processing techniques and mapped onto facade representations derived from 3D models. Repeated observations across different times and days were then used to evaluate anomaly recurrence and spatial stability. The results show that preheating the sensor for at least 10 min, maintaining a UAV-to-facade distance of 8–10 m, and acquiring data around 17:00 provide more reliable thermal images. Multi-temporal screening effectively reduces false positives caused by temporary disturbances, while persistent anomalies associated with window–wall joints, floor slabs, wall surfaces, and moisture-related areas can be identified more robustly. The proposed framework provides a practical workflow for facade thermal defect diagnosis and retrofit-oriented decision support. Full article

Flow lines in aluminum alloy forgings are not merely post-deformation metallographic features; they are integrated indicators of material transport, microstructural evolution, defect susceptibility, and service performance. This review critically examines the mechanisms controlling flow-line evolution, with emphasis on constitutive flow behavior, dynamic recovery and recrystallization, second-phase redistribution, friction, thermal gradients, and die/preform design. It then evaluates how abnormal flow paths promote key defects, including folding/laps, flow-through discontinuities, vortex-like instability, and exposed flow lines, and distinguishes well-established mechanisms from topics that still rely on indirect evidence. Particular attention is given to the effects of flow-line morphology on anisotropy, notch sensitivity, corrosion-assisted damage, and fatigue life in forged aluminum alloys. Current control strategies, including preform optimization, FE-based backward tracing, multiphysics defect indices, frictional heat management, and isothermal forging, are also assessed. The available literature shows that stable contour-following flow lines are essential for the simultaneous control of defect formation, microstructural homogeneity, and durability, while major research needs remain in in situ validation, quantitative defect criteria, and digitally closed-loop process control. This review is therefore framed as a critical narrative synthesis rather than a formal systematic review; emphasis is placed on forging-centered studies that directly relate flow-path evolution to defect formation, anisotropy, fatigue, and process optimization, while evidence transferred from adjacent processes is treated as mechanistic support rather than equivalent proof. Full article

A coupled non-destructive testing (NDT) workflow is proposed that integrates active infrared thermography (IRT) with laser-scanning-based reverse engineering (RE) to increase the reliability of detecting and interpreting damage in composite wind turbine blades across laboratory specimens and real components. IRT provides rapid, image-based qualitative localisation of potential anomalies, while 3D scan analysis supplies quantitative, geometry-aware verification and measurement of defect magnitude, reducing both false positives (design-related thermal signatures) and false negatives (weak thermal contrast). On polystyrene-filled profiles, IRT alone produced thermal anomalies unrelated to delamination; co-registered scan maps identified or ruled out local indentation, correctly attributing heat-flow patterns to internal design rather than damage. Outcome: the fused method disambiguates thermal indications and quantifies defect magnitude. On a vertical-axis wind turbine (VAWT) blade, the integration distinguished genuine geometric change from architectural effects under unknown internal structure and without CAD/reference scans, preventing false calls. For three horizontal-axis wind turbine (HAWT) blades, fleet-level scan comparison detected a significant tip deviation despite no clear local IRT anomalies, demonstrating complementary roles: scan = global quantitative homogeneity; and IRT = local qualitative verification. These findings operationalise thermal–geometric cross-validation and outline a path toward UAV-enabled inspections combining passive IRT and laser scanning for hard-to-access structures under real environmental conditions. Full article

The advent of bilayer silicon carbide as a critical two-dimensional material has opened up a range of potential applications in various fields. The field of nanoelectronics and nanomechanical systems is distinguished by its exceptional mechanical robustness, yet the combined effects of environmental and structural factors on its mechanical integrity remain poorly understood. Molecular dynamics simulations are used in this study to systematically examine the tensile response of bilayer SiC across a range of strain rates, temperatures, vacancy concentrations, and pre-existing crack lengths. Results indicate that mechanical properties converge at a system size of 18,144 atoms, ensuring computational efficiency. Increasing strain rate enhances strength and toughness by suppressing atomic relaxation, while elevated temperature induces thermal softening, reducing failure strain and strength by up to 50% at 900 K. Vacancy defects drastically degrade performance, with 3% concentration causing over 70% toughness loss, and crack propagation follows Griffith-type brittle fracture, where the zigzag direction exhibits superior resistance compared to the armchair orientation. These findings highlight the sensitivity of bilayer SiC to defects and environmental conditions, providing critical insights for designing reliable SiC-based nanodevices. Full article

This state-of-the-art review provides a comprehensive, critical synthesis of the rapidly expanding field of HECCs, emphasizing the unique scientific challenges that distinguish these materials from conventional ceramics and high-entropy alloys. Key challenges of HECCs include accurately predicting stable phases and quantifying resultant material properties, optimizing complex fabrication and processing techniques, and establishing a robust correlation between the intricate microstructural characteristics and macroscopic performance. Unlike previous reviews that focus on individual ceramic families, this article integrates the novel features, diverse applications, and recent developmental breakthroughs across carbides, nitrides, borides, and oxides to reveal the unifying principles governing configurational disorder, phase stability, and microstructure property relationships in HECCs. A key novelty of this review work is the systematic mapping of fabrication pathways, including CTR, PAS, SPS, and reactive sintering, against the underlying thermodynamic and kinetic constraints specific to multicomponent ceramic systems. The review introduces emerging ideas such as HEDFT, machine-learning-assisted phase prediction, and entropy–enthalpy competition as foundational tools for next-generation HECC design and performance analysis. Additionally, it uniquely presents densification behavior, diffusion barriers, defect chemistry, and residual stress evolution with mechanical, thermal, and tribological performance across the coating classes. By consolidating theoretical intuitions with experimental developments, this article provides a novel roadmap for predictive compositional design, development, microstructural engineering, and targeted application of HECCs in extreme environments. This work aims to support researchers and coating industries toward the rational development of high-performance HECCs and establish a unified framework for future research in high-entropy ceramic technologies. Full article

Lithium-ion batteries are extensively employed in electric vehicles (EVs) and energy storage systems (ESSs) owing to their high energy density, long cyclability, and cost-effectiveness. However, the use of flammable electrolytes makes them inherently susceptible to thermal runaway (TR), which can lead to ignition, explosion, and large-scale fires. Accordingly, early detection of defect internal conditions that precede thermal events is essential for ensuring battery safety. This study proposes a time-resolved partial discharge (TRPD)-based diagnostic method for identifying early electrical precursors of fire hazards in lithium-ion batteries. Both destructive (ex situ) and non-destructive (in situ) experiments were performed to collect defect signal data under physical deformation and accelerated degradation conditions. Through fast fourier transform (FFT) analysis of the acquired signals, specific frequency-domain characteristics associated with micro internal short circuits (MISC) were identified, particularly within the 3.9 MHz, 11.9 MHz, and 19 MHz bands. Defect signals were clearly distinguishable from background common-mode voltage (CMV) noise, confirming the diagnostic sensitivity of the proposed approach. The results demonstrate that the TRPD-based technique enables early recognition of latent insulation degradation and internal short-circuit phenomena before thermal runaway occurs. This work bridges the gap between conventional insulation monitoring and battery safety diagnostics, providing a scalable framework for integrating high-frequency signal analysis into EV and ESS battery management systems for fire prevention. Full article

The fusion of infrared and visible images provides critical value in computer vision by integrating their complementary information, especially in the field of industrial detection, which provides a more reliable data basis for subsequent defect recognition. This paper presents STGAN, a novel Generative Adversarial Network framework based on a Swin Transformer for high-quality infrared and visible image fusion. Firstly, the generator employs a Swin Transformer as its backbone for feature extraction, which adopts a U-Net architecture, and the improved W-MSA is introduced into the bottleneck layer to enhance local attention and improve the expression ability of cross-modal features. Secondly, the discriminator uses a Markov discriminator to distinguish the difference. Then, the core GAN framework is leveraged to guarantee the retention of both infrared thermal radiation and visible-light texture details in the generated image so as to improve the clarity and contrast of the fused image. Finally, simulation verification showed that six out of seven indicators ranked in the top two, especially in key indicators such as PSNR, VIF, MI, and EN, which achieved optimal or suboptimal values. The experimental results on the general dataset show that this method is superior to the advanced method in terms of subjective vision and objective indicators, and it can effectively enhance the fine structure and thermal anomaly information in the image, which gives it great potential in the application of industrial surface defect detection. Full article

Photovoltaic (PV) fault detection faces significant challenges in distinguishing subtle defects from complex backgrounds while maintaining reliability across diverse environmental conditions. Traditional approaches struggle with scalability and accuracy limitations, particularly when detecting electrical damage, physical defects, and environmental soiling in thermal imagery. This paper presents SolarFaultAttentionNet, a novel dual-attention deep learning framework that integrates channel-wise and spatial attention mechanisms within a multi-path CNN architecture for enhanced PV fault classification. The approach combines comprehensive data augmentation strategies with targeted attention modules to improve feature discrimination across six fault categories: Electrical-Damage, Physical-Damage, Snow-Covered, Dusty, Bird-Drop, and Clean. Experimental validation on a dataset of 885 images demonstrates that SolarFaultAttentionNet achieves 99.14% classification accuracy, outperforming state-of-the-art models by 5.14%. The framework exhibits perfect detection for dust accumulation (100% across all metrics) and robust electrical damage detection (99.12% F1 score) while maintaining an optimal sensitivity (98.24%) and specificity (99.91%) balance. The computational efficiency (0.0160 s inference time) and systematic performance improvements establish SolarFaultAttentionNet as a practical solution for automated PV monitoring systems, enabling reliable fault detection critical for maximizing energy production and minimizing maintenance costs in large-scale solar installations. Full article

The Republic of Korea, with 64% forest coverage, is increasingly vulnerable to large-scale wildfires. This study employed electrical resistance tomography (ERT) to diagnose internal damage in Pinus densiflora trees following a surface fire in spring 2023. Of the 30 monitored trees, 5 died in 2023 and 6 more had died by 2024. Dead trees showed a 41% higher Bark Scorch Index (BSI) and a 10%–15% lower DBH and circumference than survivors. From July, ERT detected significant increases in high- (ERT_R) and medium-resistance (ERT_Y) areas, while low-resistance (ERT_B) regions declined. By September, ERT_R and ERT_Y were 2.2 and 1.9 times higher in dead trees. Maximum resistivity (Rs_max) rose 6.1-fold to 3724 Ωm. One year post-fire, healthy areas in dead trees dropped below 18%. These findings indicate that internal defects develop gradually and accelerate in summer and winter, correlating with thermal and freeze–thaw stress. Early diagnosis within two months post-fire was unreliable, while post-summer assessments better distinguished trees at mortality risk. This study demonstrates ERT’s utility as a non-destructive tool for tracking post-fire damage and guiding forest restoration under increasing wildfire threats. Full article

The rapid growth of wind energy infrastructure over the past two to three decades has led to an urgent need for advanced non-destructive testing (NDT) methods—both for newly installed wind turbine blades (WTBs) and for ageing components nearing the end of their service life. Among emerging techniques, passive infrared thermography (IRT) offers a promising solution by enabling contactless, time-efficient inspection based on naturally occurring thermal variations. The effectiveness of passive IRT depends on the presence of sufficient thermal contrast to distinguish surface features, subsurface structures, and defects. To better understand the possibility of obtaining such contrast in composite structures such as WTBs, a controlled study was carried out on a blade section exposed to programmed temperature transients in a climate chamber. Infrared measurements were recorded, and the thermal behaviour of the specimen was simulated using finite element models (FEM) in COMSOL Multiphysics 6.3. Although direct validation is limited by measurement uncertainties and transient effects, the comparison provides insight into the capabilities and limitations of FEM in replicating real-world thermal behaviour. This paper focuses specifically on the challenges related to the modelling approach. Full article

Rolling contact fatigue (RCF)-induced cracks in steel rails exhibit a fish-scale-shaped cluster distribution, and generally form in a layered, overlapping manner. Eddy-current-pulsed thermography (ECPT) has been applied in RCF detection by taking advantage of electromagnetic–thermal execution; however, one still faces challenges in identifying and quantifying such layered, overlapping defects. This paper proposes a swept-frequency eddy-current-pulsed thermal tomography (ECPTT) detection method to quantitatively characterize multilayer crack depth and inclination angle in an artificial rail sample. In particular, stimulating frequency modulation is used to guide the induced eddy current and heat to varying depths, and this is combined with principal component analysis (PCA) to identify multilayer defects. Moreover, a thermal signal reconstruction (TSR) algorithm is introduced. TSR features are extracted for analyzing the burial depth and inclination angle of multilayer defects. The results demonstrate that the third principal component (PC3), extracted via PCA, enables layer-count discrimination in multilayer defects. Integrated with gradient magnitude analysis of the second principal component (PC2) under swept-frequency excitation, defect contour localization error can be controlled within 0.5 mm. Building on layer discrimination, multi-frequency thermal response analysis further reveals variations in PC1’s variance contribution, differentiating inclination angles of 10° and 20°, whereas comparative heating- and cooling-rate magnitudes distinguish burial depths of 0.5 mm and 1.0 mm. The research verifies that the ECPTT system can accurately detect the layer number, inclination angle, and depth of buried RCF defects, substantially enhancing the accuracy of defect contour reconstruction. Full article

Thermal analysis (TA) has been a valuable tool for controlling the carbon equivalent (CE) of cast irons. Additionally, this technique can provide enhanced control over melt quality, allowing for the avoidance of defects such as undesirable graphite morphology and the formation of carbides. To obtain the most valuable information from the TA, it is necessary to minimize the variations in the filling operation of the TA cups. However, the mass and pouring temperature of TA cups can vary in TA’s typical foundry operations. A design of experiments was performed to determine whether specific parameters of cooling curves used for quality control can distinguish the inoculation effect in the melt when the mass and the pouring temperature of TA cups are varied. The minimum temperature of the eutectic arrest proved to be a robust inoculation potential control parameter when variations in the cup’s mass were within a range of 268–390 g and were filled at any pouring temperature between 1235 and 1369 °C. Lighter cups under 268 g and poured at a low temperature are not suitable for controlling inoculation potential by TA; however, they remain helpful in controlling CE. These later cups are related to cooling times of less than 180 s, which can serve as a criterion for discarding unsuitable samples. A bimodal population of cell surfaces was revealed in the samples, with the population of small cells being proportionally more numerous in samples with lower TE_min values. Full article

In recent years, the building insulation layer peeling caused by quality problems has brought about safety hazards to human life. Existing means of non-destructive testing of building insulation layers, including laser scanning, infrared thermal imaging, ultrasonic testing, acoustic emission, ground-penetrating radar, etc., are unable to simultaneously guarantee the detection depth and resolution of the insulation layer defects, not to mention high-precision imaging of the insulation layer structure. A new type of high-precision imaging radar is specifically designed for the quantitative quality inspection of external building insulation layers in this paper. The center frequency of the radar is 8800 MHz and the −10 dB bandwidth is 3100 MHz, which means it can penetrate the insulated panel not less than 48.4 mm thick and catch the reflected wave from the upper surface of the bonding mortar. When the bonding mortar is 120 mm away from the radar, the radar can achieve a lateral resolution of about 45 mm (capable of distinguishing two parties of bonding mortar with a 45 mm gap). Furthermore, an ultra-wideband high-bunching antenna is designed in this paper combining the lens and the sinusoidal antenna, taking into account the advantages of high directivity and ultra-wideband. Finally, the high-precision imaging of data collected from multiple survey lines can visually reveal the distribution of bonded mortar and the bonding area. This helps determine whether the bonding area meets construction standards and provides data support for evaluating the quality of the insulation layer. Full article

Pantograph-based electrical current transmission systems are used in electric traction vehicles. The contact surface between the pantograph and the catenary wire experiences mechanical and thermal effects during the train’s movement. Typically, this contact surface on the pantograph is covered by a segmented carbon or copper rod, attached to an aluminum base. Railways implement organizational measures for pantograph condition monitoring, based on scheduled inspections. Constitutionally, the option to replace contact elements or individual segments of the pantograph exists if wear is detected. Many scientific publications describe ideas for pantograph visualization and automated condition monitoring. These ideas are based on analyzing mechanical vibrations generated by the pantograph, acoustic vibration signal analysis, 3D geometric data of the pantograph surface captured by laser scanning, and combinations of several methods. However, in these publications, mechanical vibration analysis is limited to signal shape and spectral analysis. The possibility of treating the vibration signal as a random process using statistical methods has not been utilized. This study describes the possibility of evaluating classified mechanical pantograph vibrations using the signal’s autocorrelation transformation. A laboratory experiment confirmed the proposed method for evaluating informative signal classification features. The proposed method can distinguish between signals generated by a defective pantograph surface and identify different types of defects. Full article

Railways play a pivotal role in national economic development, freight transportation, national defense, and regional connectivity. The detection of rolling contact fatigue (RCF) defects in rail tracks is essential for railway safety and maintenance. Due to its efficiency and non-contact capability in detecting surface and near-surface defects, Eddy Current Pulsed Thermography (ECPT) has garnered significant attention from researchers. However, detecting multi-layer RCF defects remains a challenge. This paper introduces a sweeping Eddy Current Pulsed Thermal-Tomography system (ECPTT) to detect multi-layer RCF defects effectively. This system utilizes varying excitation frequencies to heat defects, altering skin depth and facilitating feature extraction to distinguish multi-layer RCF defects. Skewness and thermographic signal reconstruction (TSR) values are employed as features in the experiments. These features are qualitatively analyzed to differentiate the layers and depths of multi-layer RCF defects. Additionally, five different coils were compared and analyzed quantitatively. The results indicate that the ECPTT system can effectively detect and distinguish multi-layer RCF defects, thereby providing more detailed defect information and enhancing railway safety and maintenance efficiency. Full article