Search Results (1,848)

The intensification of ground pressure and instability of surrounding rock in deep mining are the core safety challenges of metal mines. This article takes the first mining area of the Sishanling Iron Mine from 960 m to 1020 m as the object, and based on the data obtained from the multi-channel microseismic monitoring system for the whole year of 2025, combined with monthly mining and filling parameters, conducts a rock mass risk assessment that couples microseismic activity with the mining and filling process. The results show that microseismic activity and blasting operations exhibit a significant “resonance of the same frequency” response. The event-intensive areas are distributed along the fault zone and the edge of the goaf, and migrate in a directional manner from shallow to deep with the advancement of mining. By comparing the photos of the tunnel damage on site on a monthly basis, it was found that the incident gathering area was highly consistent with the locations of roof collapse and debris support, which verified the accuracy of microseismic positioning. Based on this, a risk discrimination index based on event frequency, energy release rate, and spatial concentration was established to dynamically evaluate the −960 m and −1020 m sections on a monthly basis. The local risk intensity in the −1020 m section was higher, and the risk increased compensatorily when the filling was delayed 2–4 weeks after mining, revealing the key control role of the mining filling coordination rhythm on the stability of the surrounding rock. The coupled evaluation system of mining filling microseismic risk constructed in this study can provide technical reference for active early warning and differentiated prevention and control of ground pressure in deep mines. Full article

Low-voltage networks are designed as solidly grounded neutral networks (TN, TT) or isolated neutral networks (IT). The latter type is used when continuity of supply and effective protection against electric shock are required despite a single ground fault. A characteristic feature of the IT network is the application of insulation monitoring systems, currently officially named Insulation Monitoring Device (IMD). The aim of this device is to signal the first ground fault, and the network can still be powered. This article shows a comprehensive overview of IMD solutions, from historical to contemporary. IMD structures and characteristic features, as well as critical evaluation, are presented, highlighting their advantages and disadvantages. The desired directions for the development of IMDs are indicated to ensure their proper functioning in modern power networks. Full article

The growing scale of photovoltaic (PV) installations creates an urgent need for automated fault detection systems that operate in real time on resource-constrained monitoring hardware. Although deep learning methods achieve high classification accuracy on PV fault benchmarks, their computational requirements make them impractical for deployment on embedded devices such as smart inverters and industrial IoT controllers. This paper proposes a lightweight classification approach based on the fuzzy k-Nearest Neighbor (Fuzzy kNN) algorithm, in which every electrical measurement is represented as an Ordered Fuzzy Number (OFN) whose spread is automatically calibrated from the local standard deviation of the measurement window. This adaptive fuzzification encodes the inherent sensor noise and environmental variability of SCADA measurements without any manual parameter tuning. Six fuzzy distance metrics, obtained by combining three defuzzification operators (FOM, LOM, MOM) with the Euclidean and Manhattan distance functions, were evaluated on the public GPVS-Faults benchmark containing approximately 1.8 million samples describing seven fault types in a grid-connected PV system operating under MPPT and IPPT control. In binary anomaly detection, the proposed method achieved an accuracy of 92.97% ± 1.17% (k = 3, MOM defuzzification with Manhattan distance), which is statistically comparable to the Random Forest baseline (92.42% ± 0.65%) while offering approximately one hundred times faster inference (1.2 ms versus 120 ms per sample) and a model footprint of only 2 MB. In multiclass fault type classification, the method reached 96.18% ± 1.80% accuracy against 97.88% ± 0.69% for Random Forest. A consistent and previously unreported observation is that the Manhattan distance systematically outperforms the Euclidean distance on three-phase electrical measurements, improving accuracy by approximately 0.90 percentage points across all tested configurations. The complete source code and the experimental pipeline are publicly released to ensure full reproducibility of the reported results. To assess generalization rigorously, a stratified group cross-validation was additionally performed in which all windows from a given experimental recording are confined to a single fold; under this leakage-free protocol every evaluated method, including Random Forest, degrades to the 50–62% range, which shows that cross-recording transfer is an intrinsic difficulty of the single-run GPVS-Faults benchmark rather than a weakness specific to the proposed classifier. Full article

Lithium-ion batteries have become essential for electric vehicles and energy storage systems; however, safety risks related to thermal runaway have emerged as a critical concern. Conventional monitoring methods based on voltage, current, and temperature cannot detect internal faults at an early stage. To address this limitation, proactive diagnostic methods employing electrochemical impedance spectroscopy (EIS) have been extensively investigated. This study presents an excitation-current generation method for EIS measurement based on a variable-inductance bidirectional Ćuk converter. The proposed circuit operates using the energy stored in the battery system, eliminating the need for an external auxiliary power source for excitation energy, while generating sinusoidal excitation currents over a wide frequency range. A small-signal analysis incorporating a first-order battery equivalent circuit model and the parasitic elements of the Ćuk converter was conducted to evaluate system stability and the effects of key design parameters. The experimental results confirm sinusoidal excitation-current generation over the frequency range of 0.1 Hz to 3 kHz. Under a representative test condition, the prototype generates a sinusoidal excitation current with a 2 A peak-to-peak AC component superimposed on a 3 A DC component. These findings verify the feasibility of the proposed method as a dedicated excitation-current generator for embedded EIS measurements. Full article

Fault diagnosis in grid-connected grid-forming hybrid energy storage station (GFM-HESS) systems is challenging because fault transients are jointly affected by converter control dynamics, multi-source electrical couplings, operating-condition variations, and measurement noise. To address these characteristics, this paper proposes a virtual synchronous generator (VSG)-constrained physics-informed heterogeneous temporal graph neural network (VSG-PI-HTGNN) for multi-class fault diagnosis. Based on VSG control characteristics and electrical relationships, 18-dimensional node-level features and eight-dimensional global physical features are constructed from ten monitored signals. These signals are further represented as a heterogeneous graph with five node types and eight predefined relation types, while node-type-specific transformations, heterogeneous graph convolution, learnable relation-scaling factors, and a primary–auxiliary dual-output framework are integrated for feature learning. A MATLAB/Simulink electromagnetic transient model is established to generate 3200 samples covering normal operation and nine fault conditions. At a signal-to-noise ratio (SNR) of 18 dB, the proposed model achieves 97.25% test accuracy and a macro-F1 score of 0.9727. Ablation results show that removing all physical information reduces the accuracy to 89.83%, while, under the unified experimental setting, the proposed model obtains higher values of the reported diagnostic metrics than the seven considered benchmark methods. Further evaluations show that the accuracy remains between 95.50% and 98.75% across SNR levels of 10–30 dB and reaches 95.38% with only 20% of the training data. Validation using an independently acquired hardware-in-the-loop (HIL) dataset further yields 92.75% accuracy and a macro-F1 score of 0.9282. Overall, these results indicate that the proposed method provides favorable diagnostic accuracy, noise robustness, data efficiency under limited-sample conditions, and simulation-to-HIL transferability under the evaluated conditions. Full article

Symmetry and asymmetry appear together in aero-engine prognostics and health management. A monitoring system should preserve a common decision structure across operating scenarios, while sensor evidence becomes asymmetric under changing operating conditions and fault modes. This study formulates the National Aeronautics and Space Administration (NASA) Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) turbofan benchmark as a four-level health-state assessment problem using remaining useful life (RUL) thresholds of 100, 50 and 15 cycles, and proposes a neural evidential reasoning (ER) framework with decision-structure symmetry. FD001–FD004 share the same health-state space, threshold map, reliability-discounted ER operator and final argmax rule, whereas feature selection, evidence transformation and reliability parameters are estimated independently for each subset. Sliding statistical descriptors and training-fold-only minimum-redundancy maximum-relevance (mRMR) selection retain six compact sensor-derived features. FD001 and FD003 therefore use six inputs; FD002 and FD004 additionally retain the same three operating-setting variables and use nine inputs. Boundary soft labels and auxiliary ordinal/RUL supervision exploit ordered degradation information near adjacent-state boundaries. Evaluation uses engine-disjoint validation, three random seeds, identical subset-specific inputs for every comparator, ensemble/neural/ordinal baselines, component ablation and calibration/error-detection analyses. Across the four subsets, the proposed model achieves the highest mean three-metric average (Avg3), 0.664 (standard deviation 0.007), with Accuracy 0.816 (0.006), Macro-F1 0.582 (0.010) and Balanced Accuracy 0.594 (0.005). It is statistically comparable to histogram gradient boosting, random forest and a plain multilayer perceptron, and significantly exceeds the tested compact-input cumulative ordinal, Feature Transformer and correlation-graph attention controls after Holm correction. Fixed-probe diagnostics identify the five-cycle window as the best overall short-history setting. The mRMR top-6 interface reduces the sensor-feature dimension by 97.1% while retaining 98.2% and 97.0% of full-pool Avg3 on FD002 and FD004, respectively. Unknown mass ranks erroneous predictions above correct predictions on every subset (area under the receiver operating characteristic curve (ROC-AUC) 0.749–0.810), including the highest value on the most heterogeneous FD004 subset. These results support a compact and auditable evidence interface that combines competitive classification with source-wise reliability and uncertainty information. Full article

Steel rails are critical components of industrial dynamic transportation systems, and their in-service fault diagnosis demands reliable discrimination of multiple defect categories under multi-mode Lamb-wave dispersion and sample-level physical-parameter drift. The rail surface is modelled by a thin metal plate instrumented with two EMAT probes (the standard laboratory surrogate for in-service rail inspection), and the proposed architecture is evaluated on this rail-equivalent plate geometry. Purely data-driven one-dimensional classifiers plateau near 80% test accuracy on a 25,000-sample simulated EMAT A-scan benchmark, while conventional physics-informed neural networks (PINNs) that inject the physical prior only at the loss-function level fail to break this ceiling. We propose a hybrid data–physics residual network, the proposed EMAT-PINN, that couples a convolutional backbone with a logit-orthogonal four-head architecture tying each defect class—hole, crack, corrosion, weld—to one simulator-derived physical quantity (reflected energy, S0/A0 ratio, arrival time, or dispersion shift) via a bias-free additive projection of the class logit. The bias-free construction guarantees that deleting or zeroing any head collapses the affected class logit to the shared baseline, so the remaining heads cannot reroute around the missing head—a structural non-replaceability that supports explainable fault diagnosis. Combined with a four-term physics regression loss (${\lambda }_{\mathrm{phys}}=2.0$), the resulting proposed EMAT-PINN attains 95.05% test accuracy at only 0.195 M parameters, with every knockout ablation dropping the model below the $80\%$ threshold commonly referenced as a practical acceptance benchmark. This per-class mapping provides an auditable link between the model’s internal representation and the physical scattering mechanism behind each decision, directly supporting explainable fault diagnosis in industrial deployment. Full article

Power transformers are critical assets in complex power grid systems, yet On-Load Tap Changers (OLTCs) account for over 30% of documented outages. This study introduces a Health Index (HI) model for OLTCs that employs a Fuzzy-Logic system to enhance condition-based maintenance (CBM) techniques. This research presents a component-wise, Fuzzy-Logic–based HI evaluation using the Scoring Methodology. The OLTC component is subdivided into six smaller components or contributors. Every contributor is a crucial subsystem whose efficacy is vital to the transformer’s overall reliability. Each contributor is divided into three sub-contributors/values and assessed using a three-tier classification scale (A, B, or C). These values could indicate condition measurements, operational observations, and diagnostic data. Each value is evaluated against reference ranges or boundary values derived from a synthesis of international standards, statistical population studies, and expert knowledge. To verify the precision of the proposed methodology, several defective OLTC cases were evaluated under diverse operational settings. This Fuzzy-Logic (FL) approach seeks to deliver a more accurate and interpretable assessment of OLTC condition than traditional crisp-value methods by integrating expert knowledge, diagnostic metrics, and standards within a structured Fuzzy-Inference framework. The implicit FL model is inherently more attuned to early indicators of deterioration and hidden risk factors that may be underestimated in conventional expert evaluations. Implementation of this intelligent monitoring approach enables early fault diagnosis, extends the transformer’s operational life, and reduces the risk of catastrophic failures and unplanned outages in the electrical power network. Full article

This systematic narrative review examines recent advances in the application of swarm robotics and swarm intelligence to smart grid systems in the context of renewable energy sources. Smart grids represent a transformative paradigm in power systems, integrating advanced communication, monitoring, and control technologies to enhance the reliability, performance, and sustainability of power distribution. Swarm robotics, drawing inspiration from the collective behaviour of social insects, enables the coordination of numerous autonomous agents to carry out complex tasks in a decentralised, scalable, and fault-tolerant manner. Over the past decade, swarm intelligence approaches—including Particle Swarm Optimisation, Ant Colony Optimisation, and consensus-based distributed control—have emerged as promising methods for addressing smart grid challenges such as decentralised energy management, fault detection, infrastructure monitoring, and maintenance. This review was conducted through a two-stage systematic search of three databases (Scopus, IEEE Xplore, and ACM Digital Library; Web of Science was not accessible during the search period and was therefore excluded), which identified 54 primary studies meeting the full-text inclusion criteria, supplemented by 11 additional records located through hand-search, for a final corpus of 65 references. For each application domain (monitoring and inspection, energy distribution optimisation, fault detection and resilience, and cybersecurity and communication), we summarise the methodologies employed, the reported performance outcomes, and the evidence level of the available studies. We also analyse the scalability limits of current swarm approaches, communication constraints relevant to grid deployment, and the integration of swarm systems with existing SCADA and EMS infrastructure. The review identifies a significant gap between laboratory demonstrations and utility-scale deployment, and outlines priority directions for future research. Full article

Smart sensor devices increasingly require remote firmware updates to deploy new functionality, security patches and algorithmic improvements without interrupting services. Runtime firmware updates remain a significant challenge in embedded sensing systems, particularly in real-time and high-availability applications where service interruption and system reboot are undesirable. This paper presents an innovative runtime firmware update mechanism for 32-bit embedded systems architecture and integrated with a custom Runtime Scheduler (RTS). The proposed approach enables dynamic Flash Program Memory (FPM) reprogramming without reboot while preserving concurrent task execution, making it suitable for intelligent sensors and edge-based IoT devices requiring continuous operation. Three update granularities, application, function and row levels, were implemented and experimentally evaluated. Reducing update granularity significantly decreased both system downtime and update data size. For the demonstration update developed to validate the concept, function-level updates reduced the transmitted patch size to approximately 35% of the data that would have been required for the equivalent application-level update, while row-level updates reduced the maximum continuous system unavailability time to approximately 16 ms by distributing FPM write operations across multiple RTS cycles. The mechanism also incorporates instruction cache invalidation, dynamic task validation and fault recovery procedures. Experiments using an intelligent power quality monitoring sensor testbed demonstrate its feasibility while improving availability, reducing update overhead and maintaining operational continuity. Full article

Unmanned vehicles require reliable embedded systems that can acquire, process, and transmit operational data in real time to support monitoring, navigation, and decision-making. This paper presents a modular Logic Computer architecture designed for telemetry and system supervision. The proposal integrates embedded hardware to provide sensor acquisition, wireless telemetry transmission, and fault reporting within a lightweight, scalable architecture. Experimental results demonstrate the proposed framework’s effectiveness and suitability for unmanned vehicle applications. Furthermore, the results provide insight into data transmission analysis in terms of technical aspects such as latency, jitter, throughput, and packet loss within a low-power-consumption framework. Based on these metrics, a comparison with existing solutions is presented, highlighting differences, drawbacks, and advantages. Tests conducted in the field also provide environmental and geographic positioning data results to demonstrate performance. Consequently, the presented prototype offers strong scalability and performance advantages over current systems, driven by its open-source architecture and high-bandwidth data transmission. Full article

The continued expansion of digital payment technologies has encouraged Tanzanian district councils to use Point-of-Sale (POS) systems to collect service fees. In current POS systems, authorized POS terminals capture payment details, and through the centralized server, transactions are recorded on the POS database, with receipts printed as confirmation. Although this architecture supports recording and monitoring transaction revenue, it provides limited support for verifying the identity of the actor authorizing the transaction and for independent confirmation of transaction integrity during auditing. To address these limitations, this study investigated the essential requirements, standards, and protocols for integrating a Self-Sovereign Identity (SSI) as a blockchain-based identity solution with existing POS systems and for using a permissioned blockchain platform to verify integrity. To achieve the study objectives, an exploratory qualitative approach was used, involving 32 semi-structured interviews with POS operators, revenue accountants, internal auditors, Information and Communication Technology (ICT) administrators, a regulator, and blockchain experts. A hybrid deductive–inductive thematic analysis was used to establish three requirement themes, namely, security and identity management, legal and regulatory compliance, and data management and integrity assurance; two standard themes, namely, security and cryptographic standards, and identity and decentralized identification; and two protocol themes, namely, security and user authentication protocols, and data management and identity portability protocols. The findings were mapped to applicable legal obligations, compliance standards, technical specifications, and implementation controls, indicating how these requirements were translated into the conceptual SSI-POS integration for district-council POS systems. The proposed solution separates credential issuance, credential holder, and device POS management; verifier and POS transaction processing; blockchain and integrity evidence; and the assurance domain across defined trust boundaries. After a signed transaction is approved by the verifier, a complete transaction receipt remains in the existing POS database, while receipt hashes or the corresponding Merkle roots are anchored on the permissioned blockchain to provide tamper-evident verification. Hyperledger Besu with Quorum Byzantine Fault Tolerance (QBFT) was selected for its fit with permissioned, multi-organizational governance and for independent replication of receipt-hash evidence. The study provides a stakeholder-derived, regulatory-aligned conceptual foundation for SSI-POS integration without replacing the existing POS system workflow. Full article

Reliable automated monitoring of photovoltaic modules is essential for improving energy efficiency, operational safety, and predictive maintenance. Infrared thermography is one of the most effective solutions for identifying localised thermal anomalies, such as hotspots, micro-cracks, connection faults, shading effects and other conditions of degradation that can compromise the performance of the photovoltaic system. The interpretation of thermographic images is still frequently reliant on the operator’s experience or on automated procedures based exclusively on image processing techniques or artificial intelligence models often regarded as black-box models, thereby limiting their reliability, robustness and interpretability. This study presents an integrated diagnostic framework combining infrared thermography, computer vision, and artificial intelligence for the automated diagnosis and health monitoring of photovoltaic modules operating under real-world conditions. The proposed methodology extends beyond hotspot detection by integrating thermal image preprocessing, anomaly detection and segmentation, extraction of thermal and geometric descriptors, and intelligent fault classification. The resulting diagnostic information enables automated fault-type classification and quantitative severity assessment, providing interpretable condition indicators for photovoltaic module monitoring. The methodology was validated using a database comprising 1560 thermographic images acquired from photovoltaic modules under representative operating conditions. The experimental evaluation demonstrated an overall classification accuracy of 97.6%, an F1-score of 97.0%, and an area under the ROC curve (AUC) of 0.991 for the fault-type classification task. The proposed framework therefore provides an interpretable and computationally efficient decision-support methodology for photovoltaic condition assessment, while its integration into longitudinal predictive-maintenance systems remains a subject for future investigation. Full article

The output quality of random-number sources directly affects the security of cryptographic systems. Physical-noise degradation, environmental disturbance, device aging, and fault injection may increase output bias, correlation, and predictability. This paper presents a resource-conscious on-chip entropy-quality supervisor for random-number source output streams. The design uses non-overlapping 1024-bit measurement windows and a shared feature engine for bit counts, directional transition counts, and run information. These features support repetition-count, adaptive-proportion, and low-toggle checks, together with two-bit pattern-concentration and first-order conditional-transition indicators, exponentially weighted moving-average trend monitoring, comprehensive scoring, and a seven-bit alarm bitmap. The RTL accepts a 32-bit valid-data interface, makes one decision every 32 valid words, and is integrated into an Artix-7 XC7A35T project configured with a 50 MHz system-clock constraint. The complete project includes a ring-oscillator TRNG, and controlled deterministic fault patterns are inserted into selected windows of the TRNG stream for fault-response verification. Deterministic RTL simulations show complete alarm mappings of 0111111 for fixed-value patterns, 0011000 for an isolated alternating window, and 1011000 for the fourth consecutive alternating window. A parameterized capture simulation also verifies pre-event, injection, and recovery sequencing. FPGA implementation results show that the entropy-supervisor core uses 1009 LUTs and 276 flip-flops without BRAM or DSP resources, while the complete project uses 1313 LUTs, 614 flip-flops, and one BRAM tile. The design meets the 50 MHz clock constraint with a WNS of 1.079 ns and no setup or hold violations. The total on-chip power reported by Vivado is 0.076 W; without simulation-derived switching activity, this value is approximate and is not a board measurement. The results establish the functional behavior of the monitoring and decision paths. The two-bit pattern-concentration and first-order conditional-transition indicators are empirical tools for online anomaly diagnosis; neither is a min-entropy estimator, and they do not replace source-specific entropy assessment under NIST SP 800-90B. Full article

Lithium-ion batteries undergo coupled thermal and mechanical responses during operation, which are closely related to their safety and reliability. However, simultaneously monitoring the surface temperature and strain fields of large-format prismatic batteries remains challenging. In this study, a fiber Bragg grating (FBG) array-based dual-parameter sensing system was developed to synchronously measure the temperature and strain of a 314 Ah lithium iron phosphate battery during charge–discharge cycling under different power conditions. Continuous surface temperature and strain fields were reconstructed from the measured data to investigate their spatiotemporal evolution. The results reveal significant differences between the temperature and strain distributions as well as asynchronous dynamic responses. Spatially, with increasing power, temperature hotspots shifted toward the central region, whereas strain extrema migrated toward the positive electrode side, revealing distinct spatial heterogeneity in the thermo-mechanical response. Temporally, the strain extrema consistently appeared tens to hundreds of seconds earlier than the temperature peaks. These findings provide direct experimental evidence of the thermo-mechanical coupling behavior of large-capacity lithium-ion batteries, establish baseline temperature–strain distributions under normal operating conditions, and offer valuable guidance for battery state evaluation, thermal management, and early fault diagnosis. Full article