Search Results (504)

Regular inspection of ignition systems in internal combustion engine (ICE) vehicles is essential as these checks influence both engine performance and emission levels. While emission testing is mandatory for road vehicles, many industrial combustion devices remain outside routine emission control. During standard service procedures such as oil changes, the ignition system can be evaluated using electronic diagnostic tools, which are commonly available in licensed service stations. These measurements provide valuable insight into the spark plug condition—a critical factor affecting ignition quality and emission formation. This article presents the design of a diagnostic system based on an oscilloscope equipped with voltage and current probes. Experimental data were obtained directly from test vehicles and include waveform records of electrical quantities, revealing clearly distinguishable differences in component behavior. The proposed system enables rapid and accurate spark plug condition assessment under various operating states. Results confirm that the selected diagnostic approach can identify characteristic variations in ignition components, thereby improving fault detection accuracy. This study introduces an innovative, non-intrusive diagnostic method applicable to the development of modern automotive tools. Overall, this work contributes to enhancing the reliability, efficiency, and emission performance of internal combustion engines. Full article

Ensuring the reliability of solder joints is essential for stable operation in electric vehicle chargers, particularly for components assembled using surface-mount technology. Therefore, we developed a defect inspection system for welding joint defects using a Faster Region-based Convolutional Neural Network model to classify results as insufficient defect, shifting defect, and normal (pin-qualified) on voltage control IC pins. The model was trained on 72,000 pin samples and achieved a training accuracy of 99.93%. Evaluation of 65,700 pin samples resulted in an accuracy of 98.89%. The experimental results demonstrate that the system provides stable recognition of reflective solder joints, reliably identifies critical pin-level defects, and is suitable for deployment in practical inspection environments. Full article

In automated production lines for automotive chargers, solder joint inspection is critical due to the widespread adoption of automotive electronics and electric vehicles. This study establishes a You Only Look Once Version 8 (YOLOv8)-based single-pin solder joint classification model for an 8-pin automotive voltage regulator IC. Solder joints were categorized into four types: normal, misalignment, insufficient fillet, and cold joint. The model achieved a single-pin training accuracy of 0.987 (4000 samples) and a test accuracy of 0.973 (4800 samples), while overall IC-level evaluation exceeded 0.90. Normal and cold joint categories were detected with the highest reliability, whereas occasional misclassifications occurred in the insufficient fillet and misalignment categories. These results demonstrate that the proposed method is feasible for efficient and accurate detection of solder joint defects, providing a practical approach to support automated inspection and ensure consistent production quality. Full article

Unmanned aerial vehicles (UAVs) are increasingly being used as diagnostic platforms in electricity transmission and distribution, enabling safer and faster inspections compared to manual climbing operations or manned aerial support. This article presents an implementation-oriented inspection process that integrates RGB imaging, infrared (IR) thermography and (optionally) LiDAR documentation for critical energy infrastructure and photovoltaic (PV) installations. The survey consists of two stages: a preliminary stage under controlled conditions and an operational stage in a real-world environment, limited only by UAV flight restrictions. Thermal measurements are recorded in radiometric formats and analyzed using polygon- and profile-based tools to identify temperature anomalies (hot spots) and support maintenance escalation decisions. This manuscript presents standardized sample templates for mission logs, QA/QC activities, and anomaly lists, intended to support reproducible data collection in future studies. The proposed process supports predictive maintenance by enabling repeatable inspections, archive-based trend analysis, and integration with asset management processes, while minimizing operational risk and avoiding power outages when technically feasible. Full article

Smart energy networks, including electricity distribution and district heating, are increasingly operated as sensor-enabled infrastructures where maintenance decisions must be made under heterogeneous and time-varying operating conditions. In these settings, time-to-event data are rarely complete; preventive actions and limited observation horizons routinely introduce censoring and truncation, so models and validation procedures must account for partially observed lifetimes to avoid biased inference and misleading performance estimates. This review surveys survival models for predictive maintenance (PdM) and remaining useful life (RUL) estimation, spanning non-parametric, semi-parametric, parametric, and learning-based approaches, with emphasis on censoring-aware formulations and the use of static and time-varying covariates derived from sensor, inspection, and contextual information. A structured taxonomy and a systematic mapping of model families to data types, core assumptions (proportional hazards versus parametric distributional structure), and decision-oriented outputs such as risk ranking, horizon failure probabilities, and RUL distributions are presented. Evaluation practice is also synthesized by covering discrimination metrics, censoring-aware RUL accuracy measures, and probabilistic assessment via proper scoring rules, including the time-dependent Brier score and Integrated Brier Score (IBS). The review provides researchers and practitioners with a practical guide to selecting, fitting, and evaluating survival models for risk-informed maintenance planning in smart energy networks. Full article

Metallic contaminants in powdered foods represent a serious safety concern. Therefore, effective detection is crucial for food safety. This study aimed to develop an electric-field-based detection system and quantitatively evaluate its performance. An alternating (+/−) electrode array (gap 1–2 mm) was designed, and resonance analysis identified 15 kHz with a 2 mm gap as the optimal operating condition. Using an IGBT-based high-voltage source, 1.35 kV was selected to ensure stable operation without partial discharge. A real-time algorithm based on a minimum current-change threshold was implemented, and detection responses to stainless steel (SUS), aluminum (Al), and copper (Cu) particles in three size classes (<0.5, 0.5–1.0, and 1.0–2.0 mm) were evaluated using hit/miss modeling and logistic regression to obtain probability-of-detection (POD) curves and limits of detection (LOD). The system achieved POD ≥ 0.9 for 1.0–2.0 mm particles; in the 0.5–1.0 mm range, observed POD values were 84%, 90%, and 68% for SUS, Al, and Cu, respectively. Safety was assessed by COMSOL-based localized heating simulation validated by infrared thermography and by ozone monitoring for real-time operation. Compared with conventional inspection approaches, the proposed system provides a compact, cost-effective architecture while reporting inspection-oriented reliability metrics (POD/LOD) for process-line deployment. Full article

The rapid increase in the production of Waste Electrical and Electronic Equipment (WEEE) and batteries requires advanced automated disassembly solutions. While disassembly automation has progressed, the non-destructive removal of electrical cable connectors (ECCs) remains a critical unresolved challenge, particularly for battery packs where safety is paramount. This paper presents a critical review of the state-of-the-art in robotic ECC disassembly. To systematically assess the technological maturity of the field, the authors introduce a functional decomposition of the process into six fundamental tasks: detection, pose estimation, accessibility, motion planning, manipulation, and extraction. While detection, pose estimation, and manipulation are more advanced due to contributions from adjacent fields like assembly and inspection, accessibility, motion planning, and extraction are still at an early stage. Based on the identified gaps, the authors suggest that future developments could follow two main directions: leveraging comprehensive databases for applications with limited variability, or shifting the disassembly approach from the connector housing to the locking mechanism to achieve broader applicability. Full article

Reinforcing steel bars in coastal regions are frequently exposed to chloride-rich environments before the concrete placement, yet the mechanical consequences of this pre-embedding exposure are rarely quantified. This study experimentally investigates the corrosion progression and mechanical degradation of ASTM A615 grade 60 reinforcing steel bars subjected to natural marine exposure and accelerated simulated chloride conditions using real Arabian seawater. Bare bars of 10 mm diameter were exposed to outdoor coastal conditions in Karachi and to an electrically accelerated seawater environment. A periodic evaluation was carried out up to 270 days, including visual inspection, mass loss, diameter reduction, tensile testing, and microstructural characterisation using scanning electron microscopy (SEM). Natural exposure produced gradual general corrosion, corresponding to ~0.5% annual cross-sectional loss and minor reductions in tensile strength within experimental variability. In contrast, simulated chloride exposure markedly accelerated deterioration, causing diameter losses approaching 1 mm and reductions in yield and ultimate strength of up to 20–25% within 60 days. Strength degradation trends closely followed section loss, indicating cross-sectional reduction as the dominant observed factor. SEM observations showed porous and cracked corrosion products with limited protective capacity. A performance-based time equivalence between natural and simulated exposure was derived from degradation trends while acknowledging possible mechanistic differences. Regression models relating exposure parameters to residual strength showed strong agreement with experimental data. The findings demonstrate that pre-placement marine exposure can introduce measurable steel degradation, underscoring the need to account for construction-stage corrosion in durability management of reinforced concrete in coastal regions. The findings highlight the critical impact of pre-embedding chloride exposure on reinforcing steel performance and emphasise the need to incorporate construction-stage corrosion effects into durability-based design and marine construction practices. Full article

The drive of the bucket-wheel on SRs1200 excavators is realized by a 400 kW electric motor and a multi-stage gearbox through which power and torque are transmitted from the drive motor to the bucket-wheel. The gearboxes used on these excavators are of a conventional extended design with parallel shafts and pairs of helical cylindrical gears, equipped with a main and an auxiliary drive. The main drive is used during bucket wheel operation, while the auxiliary drive is applied during overhaul activities and inspection. From the input shaft of the main drive to the output shaft, a four-stage gear transmission is formed. In previous designs, the gear on the output shaft was manufactured by casting, while the gearbox output shaft is hollow, allowing the bucket wheel shaft to be mounted through it. The objective of the research is the implementation of two different methods, one theoretical and one practical, for diagnosing the behavior and vibrations occurring in the drive group, with the aim of determining the most optimal approach to operation, maintenance, and necessary reconstruction of the gearbox. The basic diagnostic parameters are vibration values measured at characteristic locations throughout the drive group and its supporting structure. These measurements show good agreement with a mathematical 3D model developed using the Inventor software package, based on the finite element method, the theory of elasticity, and machine dynamics. Testing was performed prior to installation, followed by inspection after a certain number of operating hours, reconstruction of the gear teeth, and testing after reconstruction. A reduction in drive group vibrations of approximately 30% was achieved. The scientific contribution lies in the potential for future development of gearbox condition analysis models based on measured vibration parameters. Full article

Accurate identification of user–transformer relationships is fundamental to refined management, load forecasting, and fault diagnosis in low-voltage distribution networks. Traditional approaches often rely on costly manual inspection or complex physical modeling, which limits their scalability. This paper proposes a frequency-domain representation learning and weakly supervised optimization method for automatic transformer-area identification from large-scale user electricity data with incomplete labels. Specifically, the proposed method first applies the Fast Fourier Transform (FFT) to convert users’ voltage and current time series into robust frequency-domain feature vectors, effectively revealing intrinsic periodic structures while reducing noise interference. Then, under limited supervision, a deep metric learning framework is employed to optimize the embedding space such that users belonging to the same transformer area are clustered more compactly, while those from different areas are separated farther apart. Finally, a high-density clustering algorithm is applied in the optimized embedding space to complete the transformer-area partition for all users. Experimental results demonstrate that the proposed approach can effectively leverage limited label information and significantly improve transformer-area identification accuracy, providing an efficient and low-cost solution for digitalized operation and maintenance of low-voltage distribution networks. Full article

The primary focus of enhancing the efficiency of operations in the Industry 4.0 setting is Predictive and Preventive Maintenance (PPM). The paper introduces a predictive-maintenance system based on the Unified Namespace (UNS), which involves real-time sensor measurements, photogrammetry, and modelling of a digital twin to improve fault prediction and responsiveness to maintenance. This experiment was conducted over six months in a medium-sized discrete electromechanical production plant equipped with motors, Variable Speed Drives (VSDs), robot/cobots, precision grip systems, pipework systems, Magnemotion/linear motor drives, and a CNC machine. The continuous data, such as high-frequency vibration, temperature, current, and pressure, were monitored and analysed with machine-learning models, including support-vector machines, Gradient Boosting, long-short-term memory, and Random Forest, through which temporal degradation can be predicted. UNS architecture integrated all sensor and imaging data into a vendor-neutral data model through OPC UA to help ensure that all experiments could be integrated consistently and be updated in real time to real digital twins. The suggested system correctly identified mechanical and electrical failures and predicted failures before they really took place. Consequently, machine downtime was reduced by 42.25%, and Mean Time to Repair (MTTR) by 36%, compared to the prior six-month baseline period. These improvements were associated with earlier anomaly detection and digital-twin-supported pre-inspection. Overall, the findings indicate that the integration of UNS with multi-modal sensing and digital-twin technologies may enhance predictive maintenance performance in comparable industrial settings. The framework provides a data-driven, scalable solution to organisations that aim to modernise their maintenance processes, attain greater reliability and better equipment utilisation, as well as enhanced Industry 4.0 preparedness. Full article

With the advent of ubiquitous healthcare and advancements in textile industry, non-invasive wearable biomedical solutions are becoming an increasingly attractive alternative to in-hospital monitoring, allowing for timely diagnostics and prediction of severe medical conditions. Non-contact biopotential monitoring is particularly promising because non-contact biopotential electrodes can be applied over clothing or embedded in the material without almost any preparation. However, due to the intricacies of capacitive coupling they rely on, the design of such electrodes and their interface with the body plays a key role in achieving measurement repeatability and their widespread utilization in clinical-grade diagnostics. Based on exhaustive investigation of several decades of the literature on non-contact and capacitive biopotential electrodes and electric potential sensors, this study is intended to serve as a state-of-the-art overview of their historical development and design challenges, a collecting point for important research theories and development milestones, a starting point for anyone seeking for a soft head start into this research area, and a remedy for occasional misnomers and conceptual errors identified in the existing papers. The ultimate goal of this comprehensive analysis is to demystify phenomena of non-contact biopotential monitoring and capacitive coupling, systematically reconciliate terminological inconsistencies, and enhance accessibility to the most important findings for future research. To accomplish this, fundamental concepts are thoroughly revisited—from fundamentals of electrochemistry and working principles of capacitors and operational amplifiers to system stability and frequency-domain analysis. With the use of various mathematical tools (Laplace transform, phasors and Fourier analysis, and time-domain differential calculus), discussions on non-contact and capacitive biopotential electrodes, collected from the 1960s onward, are for the first time compiled into a unified, abstracted, bottom-up analysis. The laid-out inspection provides analytical explanation for various aspects of measurement results available in the referenced literature, but also serves an educative purpose by devising a methodological framework that can be easily applied to other similar research fields. Firstly, the differences and similarities between wet, dry, surface-contact, non-contact, capacitive, insulated, on-body, and off-body biopotential electrodes are clarified. For this purpose, equivalent electrical models of various non-invasive biopotential electrodes are analyzed and compared. As a result, a proposal for a revised classification of biopotential electrodes is given. Secondly, instead of using the concept of a purely capacitive biopotential electrode, a test is proposed for assessing the predominant coupling mechanism achieved with an electrode over an insulating layer. Thirdly, a fundamental model of a buffer active non-contact biopotential electrode and its interface with the body is built and generalized, and the proposed test is applied for analyzing the influence of voltage attenuation and phase shifts on signal morphology. Lastly, guidelines for designing the described electrode–body interfaces are proposed, along with a discussion on practical aspects of their implementation. Full article

Wolves (Canis lupus) have recolonized Belgium after more than a century of absence, raising concerns about interactions with livestock in densely populated regions such as Flanders. Empirical field-based documentation of wolf behavior near protected livestock in such landscapes remains limited. This study presents a short-term, descriptive camera-trap case study documenting wolf presence near a temporary sheep pasture protected by electric fencing and livestock guardian dogs (LGDs). Nineteen camera traps monitored the pasture perimeter within a military training area in northeastern Flanders over a 16-day period in September 2023. Sheep were present for 11 days and accompanied by six LGDs. Twenty-three wolf images were recorded, corresponding to eight distinct detection events. Wolves were detected shortly after fence installation and following sheep removal. Occasional close approaches and fence inspection behavior were observed, but no fence crossings or predation events occurred. Most wolf detections occurred when sheep and LGDs were absent, although wolves were also recorded near periods of human activity. Given the observational design, causal inference is not possible. The study provides baseline documentation of wolf–livestock–LGD interactions in a densely populated European landscape. Full article

With the continuous advancement of the “dual-carbon” strategy, the penetration of renewable energy sources such as wind and photovoltaic (PV) power has steadily increased, imposing more stringent requirements on the safe and stable operation of modern power systems. As the core components of these systems, critical electrical devices operate under harsh conditions characterized by high voltage, strong electromagnetic interference (EMI), and confined high-temperature environments. Their operating status directly affects the reliability of the power supply, and any fault may trigger cascading failures, resulting in significant economic losses. To address the issues of low inspection efficiency, limited fault-identification accuracy, and unstable data transmission in strong-EMI environments, this study proposes an intelligent inspection system for power equipment based on multi-technology integration. The system incorporates a redundant dual-mode wireless transmission architecture combining Wireless Fidelity (Wi-Fi) and Fourth Generation (4G) cellular communication, ensuring reliable data transfer through adaptive link switching and anti-interference optimization. A You Only Look Once version 8 (YOLOv8) object-detection algorithm integrated with Open Source Computer Vision (OpenCV) techniques enables precise visual fault identification. Furthermore, a multi-source data-fusion strategy enhances diagnostic accuracy, while a dedicated monitoring scheme is developed for the water-cooling subsystem to simultaneously assess cooling performance and fault conditions. Experimental validation demonstrates that the proposed system achieves a fault-diagnosis accuracy exceeding 95.5%, effectively meeting the requirements of intelligent inspection in modern power systems and providing robust technical support for the operation and maintenance of critical electrical equipment. Full article

Automated inspection of power infrastructure with drones requires processing video streams in real time and performing object recognition from image data with constrained resources. Server-based object recognition algorithms depend on transmitting data over a network and require considerable computational resources. In this study, we present an automated system designed to inspect power infrastructure using drones in real time. The proposed system is implemented on the Rockchip RK3588 platform and uses a lightweight YOLOv8 architecture incorporating a Slim-Neck model with a VanillaBlock module integrated into the backbone. To support real-time operation, we developed a digital video stream processing system (DVSPS) to coordinate multimedia processor (MPP)-based hardware video decoding, with inference performed on a multicore neural processing unit (NPU) using thread pooling. The system can navigate autonomously using a closed-loop machine vision system that computes the latitude and longitude of electrical towers to perform multilevel inspections. The proposed model attained an 84.2% mAP50 and 52.5% mAP50:95 with 3.7 GFLOPs and an average throughput of 111.3 FPS with 34% fewer parameters. These results demonstrate that the proposed method is an efficient and scalable solution for autonomous inspection across diverse operational conditions. Full article