Search Results (2,238)

Compared with mass spectrometry or high-performance liquid chromatography (HPLC), surface-enhanced Raman scattering (SERS) is a promising alternative technique for inspection of preservatives in food safety. However, conventional SERS substrates based on metallic nanoparticles commonly suffer from complicated fabrication processes, long processing times, and high costs. Therefore, we propose a high-density porous anodic aluminum oxide (AAO) substrate prepared by one-step anodization process combined with pore widening to increase number of SERS hotspots on template. Through a rapid one-step anodization process conducted at 25 °C, the processing time and efficiency are greatly improved compared to conventional low temperature of 0–10 °C and two-step anodization method. By lowering the anodization voltage to 20 V, a high-density porous substrate is achieved, effectively enhancing the SERS signal intensity. Furthermore, we demonstrated that SERS signal intensities are affected by multiple correlated structural factors and significantly improved by lower anodization voltage with pore widening. The analytical enhancement factor is calculated as 1.18 × 10⁵ to 1.44 × 10⁷ on an AAO substrate prepared at 20 V with pore-widening process for 1000 and 0.1 ppm p-hydroxybenzoic acid, respectively. For the preservative detection of p-hydroxybenzoic acid, a detection limit of 100 ppb is achieved by a high-density AAO substrate prepared at 20 V, which is far below the regulatory limit of 600 ppm. Full article

Wind turbines are increasingly deployed at larger scales and in harsher operating environments, leading to greater structural complexity, stronger load variability, and higher maintenance demands across both drivetrain and structural components. Reported field data indicate that gearboxes and bearings account for approximately 30–40% of total turbine downtime, while blade-related failures contribute roughly 20–25% of reported failure events, primarily through fatigue, delamination, leading-edge erosion, and lightning-induced defects. In parallel, large-scale and offshore turbines show increasing susceptibility to tower fatigue cracking, corrosion-assisted degradation, and flange joint bolt-preload loss under cyclic and environmental loading. This review provides a comprehensive applied-engineering synthesis of failure mechanisms, reliability challenges, and monitoring strategies for major wind turbine components, including gearboxes, bearings, blades, towers, and flange joints. A wide range of condition monitoring, structural health monitoring (SHM), and prognostics and health management (PHM) approaches is critically examined, including vibration analysis, acoustic emission, ultrasonic inspection, infrared thermography, impedance-based sensing, electromagnetic methods, machine vision, SCADA-based diagnostics, and artificial-intelligence-assisted fault classification. The review compares these techniques in terms of detectable damage types, spatial coverage, sensitivity, deployment practicality, and limitations under real operating conditions. In addition, statistical reliability methods and data-driven models are discussed to interpret failure trends and uncertainty. Recent AI-based studies have reported fault classification accuracies exceeding 90% under controlled or semi-controlled conditions; however, their field reliability remains constrained by data imbalance, domain shift, limited labeled failure datasets, model interpretability, and insufficient validation under realistic turbine operating environments. The main contribution of this review is an integrated applied synthesis that connects drivetrain and structural failure mechanisms with measurable monitoring indicators, diagnostic technologies, AI-enabled PHM limitations, and predictive-maintenance decision needs. The paper provides practical guidance for monitoring design, early fault detection, predictive maintenance, and long-term reliability improvement in next-generation wind turbine systems. Full article

Power line component inspection is essential for maintaining the reliability of the electrical power infrastructure. Recently, some researchers have studied automatic power line inspection using drones and deep learning. However, fully supervised deep learning approaches require large amounts of labeled data that are difficult and expensive to obtain in real-world environments. To address these challenges, this paper proposes an edge-optimized semi-supervised deep learning framework for power line component inspection. The proposed approach combines a semi-supervised learning (SSL) strategy to leverage both limited labeled images and abundant unlabeled field data with hardware–software (HW-SW) co-optimization techniques for efficient deployment on resource-constrained edge devices. In the learning stage, the framework improves detection performance by leveraging unlabeled inspection data via pseudo-labeling and confidence-based sample selection, thereby reducing annotation effort while maintaining robust recognition performance. In the deployment stage, the quantization technique was applied to enable real-time operation on embedded platforms with limited computational resources and power budgets. In this paper, an improved version of the edge-AI deployment score, the generalized edge-AI deployment score (GEADS), is proposed. In SSL evaluation, debiased semi-supervised learning (DeSSL) achieves a higher observed mAP@0.5 and F1-score than the standard SSL method in the single-run simulations using dataset 1 and dataset 2. In hardware evaluation, the YOLOv7-Tiny (INT8) configuration implemented on a Raspberry Pi 5 achieves the highest GEADS of 0.657, confirming it offers the most balanced performance among the required parameters. From the simulation, it is also confirmed that the proposed GEADS provides a more interpretable and statistically stable metric than the existing metric to evaluate the edge deployment. Full article

Smart energy meters are widely deployed in modern distribution networks, extending their role beyond revenue billing to real-time monitoring and data-driven smart city applications. However, conventional legal metrology frameworks rely on periodic recalibration and are not intended for the detection of accuracy drift or unexpected malfunctions between scheduled inspections. In scientific publications, various techniques for remote smart meters’ error surveillance are presented, but experimental verification on real distribution network data remains limited. The objective of this study is to experimentally verify two previously proposed power event-driven methods for remote estimation of active power measurement error in individual consumer meters, using a feeder-level sum meter as a reference instrument. One-second resolution electrical readings were collected from a real low-voltage distribution branch using ESP32-based local adapters communicating via MQTT over Wi-Fi, with SNTP-based clock synchronization for power event correlation. Under optimized detection parameters, the linear regression method achieved 0.20% RMSE and 0.75% maximum absolute error, and the neural network method 0.09% RMSE and 0.31%, confirming suitability for Class 1 m accuracy surveillance. Feasibility analysis of three MQTT-based deployment scenarios demonstrates that binary encoding limits local adapter buffers to 2.8 kB and worst-case daily channel demand to 2000 kB, confirming the practical viability of the proposed architecture. Full article

Industrial quality control is a critical component of modern manufacturing, as defects can lead to significant economic losses and safety risks. Traditional inspection methods, largely reliant on human operators or rule-based systems, often suffer from inconsistency, limited scalability, and reduced accuracy in complex environments. Recent advances in artificial intelligence (AI), particularly in deep learning and computer vision, have enabled automated defect detection and classification with unprecedented performance. This paper provides a comprehensive review of AI-based image processing techniques for industrial quality control, covering classification, detection, and segmentation approaches. Key applications across manufacturing sectors are discussed, alongside current challenges such as data scarcity, real-time implementation, and model generalisation. Furthermore, this paper explores emerging trends toward autonomous inspection systems, integrating real-time analytics, edge computing, and intelligent decision making. The insights presented aim to guide future research toward robust, scalable, and fully automated quality control solutions in smart manufacturing environments. Full article

Fouling on ship hulls increases hydrodynamic drag, fuel consumption, and emissions. This, in turn, necessitates the development of efficient methods for side cleaning and inspection. This work focuses on the application of image-based classification to assess the cleanliness of the surface of the hull in robotic cleaning systems, with respect to the ISO 8501-4 standard. Due to limited data availability, transfer learning techniques using pre-trained convolutional neural networks (ResNet50, EfficientNetB0 and MobileNetV2) were used. Both end-to-end models and hybrid approaches that combine deep feature extraction with XGBoost (version 3.2.0) classification were evaluated. Experiments were carried out on binary classification (cleaned vs. uncleaned surfaces) and multi-class classification of cleanliness levels (WA1, WA2, WA2.5). The results show that transfer learning enables effective recognition of cleaning status, achieving high performance for binary classification despite a small dataset. However, multi-class classification remains challenging due to subtle differences between classes and data limitations. The proposed approach supports automated visual inspection of underwater robotic platforms and represents a step toward objective standards-based assessment of hull cleaning processes. Full article

Due to climate change, corrosive conditions, and hydrogen-rich environments, steel energy pipelines inevitably develop a variety of defects. These deficiencies compromise pipeline safety and reliability, and neglecting them may result in pipeline leaks, fractures, and even potentially catastrophic explosions. Although a considerable body of literature reviews the effects of metal-loss defects like corrosion and cracks on pipeline safety and reliability, the impact of geometric deformation, like dents, lacks a comprehensive review. This work employs a hybrid systematic literature review (SLR) and bibliometric analysis (BA) to investigate the current research status of pipeline dent assessment. Four questions are answered: (1) What are the publication distribution characteristics, active journals, production organizations, and production authors related to research on pipeline dents? (2) What criteria have been employed for evaluating the pipeline dent? (3) From what perspective has the integrity of dented pipelines been assessed, and what research approaches have been used? (4) What are the future challenges and prospects of pipeline dent studies? The findings demonstrate that depth-, strain-, and damage-based evaluation criteria are widely employed to assess pipeline dents, each with merits and limitations. Despite the simplicity and ease of use of depth- and strain-based criteria, they are prone to underestimation flaws. In contrast, damage-based criteria, which consider multiple factors, are limited by their complexity and high computational resource requirements. The reliability of dented pipelines is predicted with remaining strength, fatigue life, and failure pressure using theoretical modeling, experimental testing, numerical simulation, or a combination of these methods. Future dent studies should involve refining numerical models, full-scale testing under varied loading conditions, and integrating advanced sensing techniques for real-time inspection. Full article

Early identification of thermal and electrical anomalies in grid-connected photovoltaic (PV) systems is becoming increasingly important to reduce energy losses, limit power quality (PQ) degradation, and avoid excessive operating stress on power electronic converters. Conventional electrical monitoring methods can provide overall performance information, but they are generally unable to detect and localize early-stage defects occurring at module or cell level. In this context, the present study proposes an integrated diagnostic framework that combines non-destructive infrared thermography (IRT) with advanced electrical signal processing techniques for PV condition monitoring. The proposed approach correlates thermographic information, capable of revealing defects such as hotspots, cell cracks, and bypass diode failures, with high-frequency electrical signal analysis based on frequency-domain and time–frequency methods, together with deep learning-driven thermographic segmentation. By associating thermal acquisitions with electrical PQ indicators, the framework enables the early detection of physical defects linked to inefficient Maximum Power Point Tracking (MPPT) operation and progressive degradation of PV system performance. The methodology was experimentally validated on a grid-connected photovoltaic installation under different fault conditions, including hotspots, bypass diode anomalies, and localized overheating effects, demonstrating the potential of the proposed approach for predictive maintenance and intelligent PV monitoring applications. The obtained results indicate that the proposed framework improves the reliability of photovoltaic fault detection by combining thermographic inspection with advanced electrical signal analysis and AI-based defect interpretation, thus supporting predictive maintenance strategies in smart PV infrastructures. The proposed approach demonstrates image segmentation capabilities, as evidenced by a precision (PA) of 96.88%, a mean IoU (mIoU) of 77.83% and a macro F1-score of 87.47%. The proposed framework maintained reduced computational requirements compatible with real-time monitoring applications. Full article

This study proposes a combination of optical and thermal methods to investigate the structural integrity of two 16th–17th centuries wooden panel paintings at the early stages of restoration. Well-established techniques, such as 3D scanning, technical photography, and active thermography, are combined with the less conventional shearography, which has recently gained increasing relevance in the diagnostics of cultural heritage materials. The proposed methodology enables the identification and spatial localization of different forms of degradation within the multilayered structure of the artworks, including physical-structural alterations, insect damage, localized hygroscopic degradation, nails, interlayer deterioration, and craquelure. This approach provides a comprehensive insight into the state of the panel painting structure and highlights potentially critical areas which were undetectable by visual inspection alone, demonstrating the ability to guide restoration interventions. Full article

The deployment of wall-climbing robotic systems plays an important role for executing inspection and maintenance tasks in high-risk environments and minimizing the risk to operators tasked with the inspection. Conventional adhesion techniques, such as magnetic, suction, and dry adhesives, encounter significant challenges when applied to diverse surface types. This study presents a four-wheeled robotic platform utilizing dual electric ducted fans (EDFs) to produce adjustable adhesion forces, facilitating uninterrupted movement from horizontal to vertical planes. A comprehensive multibody dynamics model constructed using MSC Adams analyzed wheel–surface interaction, thrust forces, and system stability during transitional phases, revealing essential force parameters for stable vertical operation and determining minimum thrust levels required to sustain four-point contact during orthogonal transitions. These findings informed thrust distribution optimization between the two EDF units to reduce rotational effects while ensuring sufficient safety margins during the ground to vertical wall transition. The findings also allowed for appropriate thrust application ensuring the generation of the required normal force distribution at wheel contact interfaces during vertical movement. A physical prototype was developed and experimentally validated, demonstrating dependable adhesion and maneuverability across a spectrum of orientations and highlighting the efficacy of simulation-driven design for thrust-based adhesion systems. Full article

Drought stress significantly impacts barley (Hordeum vulgare L.) production, necessitating early and accurate detection systems for precision agriculture. Traditional monitoring approaches rely on manual inspection or single-modality sensing, which often fail to capture the complex physiological responses to water deficit. This study presents a novel multimodal deep learning framework that integrates RGB imaging with environmental sensor data (temperature and humidity) for real-time drought stress classification in barley plants. The proposed architecture employs EfficientNetV2-S for visual feature extraction, coupled with a dedicated sensor encoding branch, unified through a cross-modal attention mechanism and gated multimodal fusion strategy. To address the computational constraints of agricultural IoT systems, we implemented comprehensive CPU optimization techniques and model compression via TensorFlow Lite INT8 quantization, achieving a 68.5% reduction in training time and 90% reduction in model size. The system was validated on a custom greenhouse dataset (379 samples, 80/20 split) and the PlantVillage dataset (26,000 images, binary reformulation). A 10-seed evaluation protocol demonstrated that the full multimodal model achieves 98.3 ± 1.5% accuracy, outperforming both an image-only baseline (97.4 ± 1.8%) and a sensor-only MLP (73.8 ± 3.5%). Across seeds, the model also achieved an F1-score of 98.34 ± 1.48% and ROC-AUC of 99.93 ± 0.13%. Ablation analysis with ANOVA (F(4,36) = 4.44, p = 0.005) confirmed that multimodal fusion improves accuracy by 0.92% over image-only models, with the full gated cross-modal attention mechanism outperforming all simplified baselines, including AgriFusionNet (75.22%), Shallow CNN (92.54%), Logistic Regression multimodal (92.11%), and Random Forest multimodal (89.91%). These results further show that relying on environmental data alone is insufficient, reinforcing the benefit of multimodal fusion. External validation on PlantVillage achieved 99.97% accuracy, demonstrating strong generalization capabilities. The optimized model operates efficiently on CPU-only hardware (training time: 9.1 min/epoch), making it suitable for edge deployment in resource-constrained agricultural environments. This work demonstrates that a low-cost, CPU-compatible multimodal deep learning system can reliably detect drought stress in barley under real greenhouse conditions and provides a practical and scalable solution for early stress monitoring in precision agriculture. Full article

To address the inherent limitations of qualitative hygiene inspections, this study establishes a structured MCDM framework to evaluate kitchen hygiene management in Taiwan’s catering industry by integrating the Analytic Hierarchy Process (AHP) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). The model integrates expert-weighted criteria to facilitate a structured risk-oriented assessment and support sustainable hygiene management through prioritized resource allocation and more systematic hygiene management. The AHP results determined hygiene behavior, cooking and processing, and storage operation management as the most influential criteria, underscoring the critical role of direct food handling practices. The framework was empirically applied to five large-scale catering enterprises and international tourist hotels with multinational operational backgrounds. TOPSIS analysis revealed significant performance variability, with establishment D achieving the highest relative closeness coefficient (0.6125) and establishment E the lowest (0.2358). These findings indicate that operational control measures play a more critical role in food safety and sustainable hygiene governance than supporting infrastructure alone. The proposed model serves as a quantitative decision-support tool for both industry self-assessment and regulatory inspections, facilitating prioritized resource allocation, continuous hygiene improvement, improved food safety governance, and more consistent long-term hygiene management practices. Sensitivity analysis further demonstrated that the overall comparative ranking structure remained generally consistent under alternative normalization conditions, although minor variation was observed between the two highest-performing alternatives. Full article

Civil infrastructure plays a critical role in ensuring societal safety and economic development. However, structural damages such as cracks inevitably occur during long-term service. Traditional manual inspection methods are insufficient to meet the demands of large-scale and routine monitoring. Unmanned Aerial Vehicles (UAV) remote sensing has become an important approach for Structural Health Monitoring (SHM), owing to its high spatial resolution imaging capability and superior operational flexibility. Nevertheless, existing studies focus on optimizing individual algorithms, lacking a systematic analysis oriented toward multi-scenario engineering applications. Therefore, we present a comprehensive review of UAV-based crack detection techniques for infrastructure using remote sensing imagery. First, publicly available datasets, UAV platforms, and evaluation metrics are systematically summarized. Then a multi-level visual analysis framework for UAV inspection is established. The framework categorizes existing methodologies into five levels: image-level classification, object-level detection, pixel-level segmentation, geometric quantification, and three-dimensional (3D) reconstruction, followed by a systematic evaluation of representative methods. Furthermore, the applicability of different methods across diverse scenarios, including bridges, pavements, dams, building facades and wind turbine blades, is systematically explored. Finally, the key challenges and future research directions are discussed. This review aims to provide a systematic theoretical foundation and methodological reference for advancing UAV-based infrastructure crack inspection from algorithm development toward practical multi-scenario engineering applications. Full article

Automated power line inspection plays a crucial role in maintaining grid reliability within smart cities by identifying potential defects in towers, conductors, insulators, and fittings. While modern anomaly detection relies heavily on deep neural networks (DNNs), training these models requires massive amounts of high-quality image data. However, a significant scarcity of publicly available datasets persists because data acquisition not only demands highly specialized professional skills but also faces strict data protection regulations enforced by grid companies. To bridge this gap, this paper presents a comprehensive review of open-access image datasets dedicated to power line inspection. Based on strict inclusion criteria—specifically, unrestricted public availability and a direct focus on core power line components—19 datasets are systematically selected and analyzed. We provide a detailed taxonomy and comparative analysis of these datasets in terms of inspection targets, acquisition platforms, annotation toolkits, and labeling schemes. Furthermore, our investigation highlights current research trends and identifies critical gaps, such as the disproportionate focus on insulators and the notable scarcity of multimodal data. To address the limitations of small-scale datasets, we also discuss existing data augmentation strategies and synthetic data generation techniques. Ultimately, this review serves as a unified navigational guide, aiming to foster the development of more robust visual inspection algorithms and to inspire future high-quality dataset construction in the power domain. Full article

Recent advances in deep convolutional neural networks (D-CNNs) have improved automated welding defect inspection. This study presents an explainable comparative framework for multi-class classification of defects in Aluminium 5083 TIG weld joints using High Dynamic Range (HDR) image data, integrating a transfer-learning model, stratified five-fold cross-validation, computational-time analysis, and Grad-CAM-based visual interpretation. Five transfer-learning-based D-CNN architectures such as VGG16, VGG19, Inception V3, MobileNet, and DenseNet were trained, validated, and tested under a common evaluation protocol to assess their suitability for welding defect classification. The dataset was organised into classes such as good weld, contamination, lack of fusion, lack of penetration, and misalignment. Model performance was compared using multiple evaluation metrics. Stratified five-fold cross-validation was also performed to assess model stability. Alongside the cross-validation, training/inference times were also recorded to evaluate computational feasibility. Grad-CAM was used as an explainable artificial intelligence (XAI) technique in order to provide visual interpretation of weld regions. Among evaluated models, DenseNet achieved the best overall performance, with a classification accuracy of 98%, and showed the least confusion across defect classes. The Grad-CAM visualisations showed that the model focused on defect-relevant weld regions, demonstrating that transfer-learning D-CNNs with XAI can support TIG welding defect classification and effective visual quality assessment. Full article