Search Results (367)

Rotating machinery is essential to energy, aerospace, manufacturing, and other safety-critical industries. Although deep-learning-based fault diagnosis has achieved high accuracy, its black-box nature limits trust, verification, and industrial deployment. This review examines explainable artificial intelligence for rotating machinery fault diagnosis and classifies existing methods into ante hoc and post hoc approaches according to their integration with model architectures. Their physical interpretability, applicable fault scenarios, explanation quality, computational overhead, robustness, and edge-deployment potential are critically compared. Quantitative criteria, including fidelity, stability, robustness, localization, and physical consistency are discussed to support objective evaluation of explanations. The review further highlights the gap between laboratory validation and industrial operation, particularly under sensor degradation, electromagnetic interference, variable working conditions, limited computing resources, and scarce fault data. It also discusses how model-relative explanations can be mapped to calibrated vibration quantities, fault-characteristic frequencies, industrial diagnostic standards, and actionable maintenance decisions. The distinction between correlation-based attribution and causal root-cause analysis is clarified, together with the role of digital twins and human-in-the-loop decision support. Finally, future research priorities are identified in standardized benchmarking, robust lightweight models, causal reasoning, and human-centered industrial deployment. Full article

Internet of Things(IoT)/edge intrusion-detection systems operate on distributed traffic and system-state data whose distributions vary across gateways, services, and attack conditions. We study a server-assisted federated setting in which a teacher reference is fitted from a permitted server-accessible training pool and explicitly distinguish this simulation assumption from fully decentralized deployment. The proposed framework evaluates progressive local training through boundary stabilization, confidence-weighted decision distillation, representation alignment, and validation-quality-aware aggregation. The evaluation uses a leakage-controlled protocol: server and client validation subsets are held out before federated training, update quality and early stopping use validation data only, and the final-test split is evaluated once. Results on NSL-KDD, CIC-IDS2017, Edge-IIoTset, and the ToN-IoT network dataset show competitive primary performance and stronger robustness in several severe label-skew settings. On the Telemetry of Things(ToN-IoT) with Dirichlet alpha = 0.1, the proposed method achieves 91.46 ± 5.54 F1, compared with 53.73 ± 49.00 for FedAvg and 53.77 ± 48.92 for FedProx. The results do not establish universal superiority or a universally optimal stage order: competing methods remain stronger in selected stable and attack-shift settings. The framework is therefore presented as a bounded, server-assisted robustness-oriented training strategy for heterogeneous IoT/edge intrusion detection. Full article

Normative AI assistants are increasingly used in domains governed by duties, permissions, prohibitions, exceptions, priorities, and institutional policies. Existing retrieval-augmented generation (RAG) and legal AI benchmarks evaluate answer accuracy, retrieval quality, citation grounding, natural-language inference, clause extraction, or general legal reasoning ability. These dimensions are necessary but insufficient when supplied evidence is incomplete, mutually inconsistent, or defeasible. The objective of this study is to introduce ParaTraceBench, a paraconsistent trace-based benchmarking framework for post-retrieval normative reasoning over fixed evidence packages. Each scenario contains a query, evidence fragments, extracted facts, defeasible rules, typed attack edges, priority relations, an expected conclusion status, and a gold diagnostic trace. The formalism uses evidence-grounded arguments, a single edge-based attack representation, explicit attack-licensing rules, acyclic priority bases with a transitive closure, grounded argument labeling, trace-normal-form alignment, and deterministic scoring. The operational NER metric is explicitly interpreted as inconsistency-conditioned unsupported-conclusion avoidance rather than proof of logical non-explosion. We evaluated the framework using 140 scenarios, external validation on 567 anonymized Russian-language cases from Russian Federation and EAEU-related materials, reasoning-oriented baseline adaptations, five-run prompt-fairness and stability controls, and a deterministic component-dependency audit. On the full external set, the trace-based configuration reached 85.7% answer-status accuracy, 85.5% contradiction-localization accuracy, 94.2% operational NER, 84.1% priority-handling accuracy, and 83.7% belief-revision accuracy. These results indicate that contradiction-aware trace evaluation provides diagnostic information beyond final-answer accuracy under the evaluated fixed-evidence conditions, while not establishing causal architectural superiority, logical non-triviality, or end-to-end RAG performance. Full article

Periodic fluctuations in pump head are a common unsteady phenomenon in tubular pumps; however, their underlying energy dissipation mechanism remains insufficiently understood. This study conducted transient numerical simulations on a two-blade tubular-flow pump with highly twisted blades operating at the design flow condition. Using entropy production theory, the research quantitatively examined Rotor–Stator Interaction (RSI), vortex development, and hydraulic loss characteristics. The findings reveal that turbulent entropy production is the primary contributor to total energy dissipation, while entropy generated by wall friction is minimal. Although the impeller experiences the greatest absolute energy loss, the fluctuations in entropy production within the guide vane are significantly larger. This indicates that the energy loss represented by the entropy production in the guide vane primarily drives the periodic head fluctuations of the pump. High entropy production is concentrated near both the leading and trailing edges of the guide vane, exhibiting a trough-shaped radial distribution influenced by the leading-edge hub vortex and tip leakage vortex. Additionally, the transient changes in entropy production under RSI are governed by the periodic formation and strengthening of the guide vane passage vortex, along with the shedding and breakdown of the wake vortex. Velocity analysis shows that the circumferential movement of the impeller wake continuously modifies the instantaneous inflow conditions at the guide vane’s leading edge, causing periodic changes in the incidence angle that enhance the passage vortex while weakening the wake vortex. This study provides deep insights into the operational stability of pumps and pumping stations in water transfer projects. Full article

Owing to its inherent flight safety, low takeoff and landing requirements, and favorable economic efficiency, the autogyro UAV, especially its electric and hybrid-electric variants, has become a core platform for low-altitude aviation missions such as transportation, inspection, and surveillance in plateau and offshore regions. However, the low air density and low Reynolds number conditions encountered in plateau regions can induce aerodynamic issues such as premature laminar flow separation, dynamic stall, and increased induced drag, which directly reduce payload capacity and endurance of small electric autogyro UAVs. In offshore environments, strong winds, turbulence, and gust disturbances intensify rotor–wake interactions, cause abrupt variations in aerodynamic loads, and reduce control margins, which severely restricts the mission reliability and flight safety of low-altitude unmanned platforms. These environmental effects collectively degrade rotor performance, including reduced aerodynamic efficiency and insufficient lift generation, and further amplify the energy constraint of electric/hybrid-electric propulsion systems. In response to bottlenecks that restrict the practical application of autogyro UAVs in extreme environments, this paper systematically reviews research progress on lift vector control for autogyro UAV rotors operating under such conditions. First, the typical aerodynamic problems encountered by autogyro UAVs in plateau and offshore environments are summarized, and their underlying physical mechanisms are analyzed from both system-level and local-flow perspectives, with a focus on how environmental factors affect the autorotation stability of unmanned platforms. Subsequently, the development of passive lift vector control technologies is reviewed, with an emphasis on the aerodynamic benefits of passive pitch mechanisms, vortex generators, and blade-tip winglets, as well as their engineering feasibility for small autogyro UAV blades. Active lift vector control technologies are then examined, including air-jet flow control, synthetic jets, and trailing-edge flaps, with discussions of their potential to delay flow separation and stall, enhance rotor aerodynamic efficiency, and an assessment of their adaptability to the energy and structural constraints of unmanned platforms. Finally, a lift vector control strategy suitable for autorotating rotors of autogyro UAVs is proposed, based on careful consideration of energy consumption, structural constraints, and control effectiveness. It provides a reference for aerodynamic optimization and flight control research on electric and hybrid-electric autogyro UAVs operating in extremely low-altitude environments. Full article

To address the challenges of integrating multi-source heterogeneous data, fragmented fault knowledge, and the limited capability of traditional rule engines in recognizing edge cases for ultra-high voltage (UHV) bushing fault diagnosis, this paper proposes a fault identification and decision-making method based on knowledge graph (KG) rule reasoning and inductive graph convolutional network (Inductive GCN). First, a triple-matching strategy is employed to perform entity extraction and relation mining from fault cases, constructing a fault knowledge graph that transforms unstructured fault case texts into a structured knowledge graph. Second, a rule engine based on a multi-source feature rule set is designed, utilizing the entropy weight method and the RETE algorithm to achieve interpretable symbolic reasoning. On this basis, a double-layer inductive graph convolutional network is introduced to learn implicit fault patterns by aggregating topological information from neighboring nodes, and a confidence-driven dynamic weighted fusion strategy is adopted to achieve complementary advantages between the two models. Finally, a large language model is introduced to generate operation and maintenance decision recommendations. Experimental results demonstrate that the proposed method achieves an identification accuracy of 98.1% on a test set of 159 samples, which is 10.7 percentage points higher than that of a single rule engine and 6.9 percentage points higher than that of a single inductive graph convolution network. The standard deviation of accuracy across different test batches is only 0.0029. These results demonstrate the effectiveness and stability of the proposed method, providing a practical technical solution for UHV bushing fault identification. Full article

Video Anomaly Detection (VAD) has emerged as a promising technology for automated surveillance and industrial monitoring. However, selecting an appropriate VAD architecture for real-world deployment remains challenging because benchmark-oriented evaluations often overlook practical considerations such as computational efficiency, latency, maintainability, supervision requirements, and long-term operational stability. To address this gap, this study proposes an expert-driven Multi-Criteria Decision Analysis (MCDA) framework for the deployment-oriented selection of VAD architectures. Eight representative architecture families were evaluated against six industrially relevant criteria, with scenario-specific priorities derived using the Best–Worst Method (BWM) from five domain experts across four representative industrial deployment scenarios. Edge-oriented architectures ranked first in three scenarios, achieving MCDA scores of 4.26, 3.90, and 4.07, whereas lightweight CNN-based architectures achieved the highest score (4.12) in the resource-constrained scenario. Inter-expert agreement ranged from Kendall’s W = 0.54 to 0.85, and Monte Carlo analysis confirmed the robustness of rankings, with top-rank probabilities of 69–74% for edge-oriented architectures and 100% for lightweight CNNs in the resource-constrained scenario. These findings demonstrate that architectural suitability depends on the deployment context rather than on a universally superior modeling paradigm, and that industrial VAD should be approached as a deployment-oriented systems engineering problem. The proposed framework provides a transparent and robust basis for aligning VAD architecture selection with operational requirements. Full article

Deep drilling equipment operates under high-load, strong-vibration, intermittent-impact, and variable environmental conditions, which motivate sensing systems that provide low-noise acquisition, synchronized triaxial measurements, local data integrity, and quantitative measurement-chain characterization. This paper presents a Zynq UltraScale+ MPSoC-based triaxial vibration sensing station for deep drilling equipment applications. The modular station integrates conditioned-voltage triaxial accelerometer interfaces, analog signal conditioning, 24-bit simultaneous analog-to-digital conversion, electrical isolation, local solid-state-drive storage, Ethernet/wireless communication, and GPS-disciplined oven-controlled crystal oscillator (OCXO) timing. The programmable logic performs deterministic acquisition, GPS pulse processing, oscillator calibration, and DMA transfer, while the processing system facilitates storage, network communication, device-state management, and host computer interaction. The sensing electronics are evaluated through zero-input noise, an experiment-specific input-amplitude-to-noise ratio, gain linearity, thermal stability, repeatability, and station-to-station local-PPS timing tests. The characterized electronics achieve a mean equivalent input noise of 0.31 microvolts, a test-derived ratio of 135.08 dB, and a mean station-to-station local-PPS falling-edge difference of 0.34 microseconds. A lightweight post-acquisition interpretation workflow using learnable multichannel weighted fusion, a convolutional autoencoder, a training-distribution-based quantile threshold, and an auxiliary classification branch achieves 0.9705 accuracy and 0.9704 F1-score on a public triaxial bearing dataset under the reported protocol. A crane-based experiment evaluates deployment feasibility and the sensing–analysis workflow using controlled operating events and a removable stationary mass disturbance. The results provide an engineering sensing basis for distributed monitoring studies on deep drilling equipment. Full article

Unstable well operations, driven by reservoir depletion, high gas–oil ratios, unstable inflow, and surface network interactions, have become a major challenge in modern production—especially in Western Siberian fields—causing flow instabilities and production losses. While numerous studies have advanced transient modeling for field optimization, their practical application remains largely limited to recommendation systems running on hourly or daily cycles, making recommendations irrelevant by the time they are applied. At the opposite end, PLCs (programmable logic controllers) relying on PID (proportional–integral–derivative) control cannot solve the main problem: determining the structure of the intermittent cycle. This work proposes a reduced-order transient model of the coupled “reservoir–tubing–annulus” system that captures the essential behavior of transient multiphase flow while remaining compact enough for on-edge real-time model predictive control. Constrained optimization algorithms built on this model provide autonomous closed-loop well control, ensuring equipment and reservoir limits are respected and enabling safe, smooth mode transitions. The solution was validated against high-fidelity simulations and real operating data from Western Siberian fields, demonstrating reliable intermittent ESP (electrical submersible pump) well operation, robust response to rapid operational changes, and effective disturbance rejection. Full article

With the rapid growth of distributed Internet of Things (IoT) services and edge-intelligence applications, conventional cloud computing is increasingly limited in latency-sensitive scenarios. Mobile Edge Computing (MEC) reduces latency by moving computation closer to end devices, while Unmanned Aerial Vehicles (UAVs) further extend MEC services to remote, emergency, or congested areas through flexible aerial deployment. However, UAV-enabled MEC still faces coupled challenges caused by heterogeneous tasks, limited resources, device energy constraints, time-varying channels, and UAV mobility. To address these challenges, this paper develops a Lyapunov-based joint optimization framework for UAV-enabled MEC systems. A dual-queue model is established to characterize local task uploading and UAV-MEC task execution, and a long-term stochastic energy minimization problem is formulated under queue-stability, resource-capacity, and energy constraints. By applying the drift-plus-penalty principle, the problem is transformed into online per-slot control decisions that jointly coordinate MEC scheduling, uplink power control, and UAV flight decisions. Structure-matched solutions are then developed, including a Lyapunov-drift-based MEC scheduling scheme, a queue-weighted closed-form water-filling power-control policy, and a gradient-based UAV flight controller with exponential smoothing and a fly-or-hover gate. A power–position alternating optimization algorithm is further introduced to handle the coupling between transmit power and UAV position. The simulation results demonstrate that the proposed framework maintains queue stability while reducing system energy consumption under heterogeneous and bursty workloads. It also achieves a balanced tradeoff between delay, energy consumption, and UAV flight activity, supporting energy-efficient and delay-aware UAV-MEC operation. Full article

The increasing penetration of distributed energy resources, variable renewable generation, battery energy storage systems, electric vehicles, and power-electronic interfaces is changing the way modern smart grids and microgrids are operated, protected, and controlled. This systematic literature review follows the PRISMA 2020 framework and examines 87 original research papers, complemented by a supplementary synthesis of 18 contextual studies that provide bibliometric, historical, and conceptual perspectives on the evolution of AI in smart grids. The primary studies are organized into six thematic clusters: energy management and forecasting; cybersecurity and intrusion detection; renewable energy integration and microgrid management; fault detection, diagnosis, and grid stability; explainable and trustworthy artificial intelligence; and emerging technologies, including digital twins, blockchain, the Internet of Things, edge computing, and federated learning. The review shows that deep learning, reinforcement learning, and ensemble machine learning are increasingly used for load forecasting, demand response, converter-interfaced renewable integration, intrusion detection, and operational optimization. However, the literature remains uneven. Fault detection, converter-aware protection, and real-time stability assessment receive considerably less attention than energy management and cybersecurity, despite their importance for inverter-based resources, grid-forming converters, electric-vehicle charging systems, and battery interfacing. Four critical gaps are identified: limited cross-grid generalizability, weak validation under realistic converter and protection constraints, insufficient adversarial robustness of AI-enabled defense systems, and limited explainability in real-time safety-critical applications. The paper provides a structured taxonomy, identifies deployment barriers, and proposes research directions for trustworthy AI in power-electronic-rich smart grids and microgrids. Full article

Accurate measurement of dynamic coal gangue size directly affects the grasping strategy of the coal gangue sorting robot, but there is still a lack of fast and effective measurement methods. To target this problem, a dynamic coal gangue size measurement method combining image coordinate-pixel calibration and Canny edge detection is proposed. Firstly, a dynamic coal gangue image acquisition system is built, and the actual size of the coal gangue is obtained by three-dimensional scanning reconstruction as a comparison benchmark. Then, through image coordinate measurement, pixel benchmark setting, and coordinate-actual size conversion model construction, combined with gray-scale conversion, Gaussian filtering, and Canny edge detection instead of traditional binarization processing, the accurate extraction of coal gangue image contour is realized. On this basis, non-maximum suppression, double threshold lag selection and morphological operation are introduced to optimize the edge detection effect, and the shadow removal technology is combined to further improve the measurement accuracy. Finally, dynamic measurement experiments are carried out under seven different working conditions to compare the measurement accuracy and stability before and after optimization. The results show that the average measurement accuracy of the optimized method is more than 98%, the measurement error is less than 1.2 mm, and the single frame processing time is about 6 ms, which meets the real-time sorting requirements. This research provides an effective technical scheme for the size measurement of dynamic irregular objects. Full article

The von Neumann architecture, due to the physical separation between memory and processor, has limited the development of data-intensive applications. Neuromorphic computing technologies inspired by the brain’s parallel and event-driven operation mechanisms have enabled low-power in-memory computing. Memristors with tunable conductance can emulate biological synapses, while flexible memristors further offer mechanical flexibility, making them suitable for wearable electronics and intelligent sensing systems. This review systematically summarizes the switching mechanisms of flexible neuromorphic memristors, including conductive filaments, interface effects, ferroelectricity, phase change, and multiple synergistic mechanisms. It categorically discusses natural and bio-derived materials, synthetic organic/polymer materials, and inorganic functional materials, and introduces strategies for enhancing flexibility. The article also covers device architectures such as sandwich structures, crossbar arrays, and fiber-based textile structures, along with low-temperature fabrication techniques. Finally, it reviews recent advances in neuromorphic computing, in-memory computing, biomimetic sensing, and biomedical wearable systems. Challenges related to mechanical stability and device uniformity are analyzed, and future directions toward self-healing materials and integrated sensing-storage-computing systems are outlined. This comprehensive review bridges the gap between material innovation and system-level integration in flexible neuromorphic memristors, providing a valuable roadmap for accelerating the development of next-generation wearable artificial intelligence, edge computing, and bio-integrated electronic technologies. Full article

Unmanned aerial vehicles (UAVs) have become essential cyber-physical platforms for applications such as surveillance, infrastructure inspection, emergency response, and intelligent transportation. However, their tight coupling among sensing, communication, control, actuation, and swarm coordination also exposes them to sophisticated cyber-physical attacks that are difficult to detect using conventional intrusion detection systems. Existing machine learning, deep learning, graph-based, and federated intrusion detection approaches generally rely on statistical feature representations or temporal patterns, providing limited capability to model causal dependencies among interacting UAV subsystems and to generalize across heterogeneous operating environments. To address these limitations, this paper proposes Fed-CGIDS-UAV, a federated causal graph learning framework for cross-domain intrusion detection in cyber-physical UAV networks. The proposed framework models each telemetry window as a typed causal graph in which nodes represent navigation, sensing, communication, control, actuation, and swarm states, while directed edges capture stable operational dependencies. Intrusions are detected by identifying violations of these learned causal relationships, and the framework provides interpretable node-edge explanations to support root-cause analysis. Furthermore, federated learning enables collaborative model training across distributed UAV clients without sharing raw telemetry, thereby preserving data privacy while improving robustness under heterogeneous operating conditions. The proposed framework was implemented and experimentally evaluated in a controlled simulation environment covering four UAV operating domains and six representative attack classes. All experiments were repeated over five independent runs using different random seeds, and the reported results correspond to the measured average performance. The proposed framework was implemented using Python 3.12 (Python Software Foundation, Wilmington, DE, USA) and PyTorch 2.3 (Meta Platforms, Menlo Park, CA, USA). UAV flight data were generated using Microsoft AirSim 1.9.1 (Microsoft Corporation, Redmond, WA, USA), integrated with PX4 Autopilot v1.14 (Dronecode Foundation, San Francisco, CA, USA) and Gazebo Sim 11 (Open Source Robotics Foundation, Mountain View, CA, USA). Within this simulation-based evaluation, Fed-CGIDS-UAV achieved an accuracy of 0.968, an F1-score of 0.956, and an internal–external stability gap (IESG) of 0.028, outperforming conventional machine learning, deep learning, graph-based, and centralized causal baselines while maintaining competitive computational latency. Although these results demonstrate the effectiveness of the proposed framework under controlled simulation conditions, validation using real-flight UAV telemetry remains an important direction for future research. These results demonstrate that integrating causal graph learning with federated optimization provides an effective and interpretable solution for privacy-preserving intrusion detection in heterogeneous cyber-physical UAV environments. Full article

The present work examines how variations in working fluid temperature govern the hydrodynamic behavior of a space-rated micropump. Using perfluorotriethylamine as the operating medium, three-dimensional CFD simulations employing the SST k-$\omega$ turbulence closure were carried out across a broad thermal spectrum, and the resulting flow physics were interpreted through entropy generation analysis. Based on the entropy production theory, the influence laws of different inlet temperatures on the external characteristics, internal characteristics, and flow loss characteristics of the micropump were quantitatively analyzed. The results show that temperature mainly affects the micropump performance by changing the viscosity and density of the working fluid. At low temperatures, the fluid viscosity increases significantly, leading to increased flow resistance, intensified internal friction, reduced head and efficiency, and increased shaft power. As the temperature increases to 0 °C and above, the viscosity change tends to moderate, and the external characteristic parameters tend to stabilize. The internal characteristic analysis shows that under low-temperature conditions, the high-pressure region in the impeller area expands and the turbulent kinetic energy decreases, but the flow separation is to a certain extent suppressed. The region near the volute tongue and the impeller outer edge are the main areas of entropy production loss, and their entropy production rates increase significantly with decreasing temperature. Moreover, at low temperatures, the high entropy production regions expand from locally isolated distributions to continuous large-scale distributions. The impeller outer edge dominates total entropy production, driven by peak fluid linear velocity and intense shear interaction with the volute wall. The findings elucidate how working fluid temperature governs both the hydrodynamic performance and the irreversible loss characteristics of the micropump. These insights can directly inform the engineering design of thermal management loops intended for orbital applications under severe temperature swings. Full article