Search Results (4,328)

Electrochemical energy storage is pivotal in constructing new-type power systems. However, the large-scale deployment of energy storage stations poses severe safety challenges, particularly the risk of deflagration. The coupling of combustible accumulation within battery systems and the confined structure of storage units can trigger cascading thermal runaway and deflagration accidents. Existing research still falls short in systematically analyzing the deflagration risks and process evolution mechanisms in energy storage stations. To address this gap, this study develops a probabilistic risk assessment model that enables analysis of risk propagation through the integration of fault tree analysis (FTA) with a static fuzzy Bayesian network (BN). The proposed approach delineates the complete risk evolution pathway from battery thermal runaway to deflagration in a confined space. Diagnostic reasoning identifies a dominant risk escalation path initiated by internal short circuits, leading to thermal runaway, flammable gas release, and pressure accumulation due to inadequate pressure relief. Sensitivity analysis highlights gases ejected during thermal runaway (C22) and lack of pressure relief devices or insufficient venting area (C31) as the most influential risk drivers. This study thus offers a practical, model-based framework for enhancing targeted risk prevention and safety resilience in electrochemical energy storage station infrastructure. Full article

This study addresses a critical knowledge gap in quantifying strategic mineral resources within hyper-arid, tectonically complex terrains by establishing a recursive framework that reconciles deterministic resource estimation with the nonlinear dynamics of tectonically mediated metamorphic systems. Using Libya’s Wadi El Shati as a case study, legacy lithological misclassifications are rectified through the fusion of Sentinel-1 Synthetic Aperture Radar, Sentinel-2 multispectral imagery, and Digital Elevation Model analytics within a unified geospatial workflow. The methodology synergizes atmospherically corrected optical data, processed via supervised Maximum Likelihood Classification, with calibrated radar-derived structural lineaments. Classified marble-bearing zones within the Al Mahruqah Formation are integrated with DEM data and field-validated thickness measurements using Triangulated Irregular Network models to resolve surface–subsurface dependencies and compute volumes. The results demonstrate a 91% lithological classification accuracy, rectifying a 22% error in legacy maps. Structural analysis of 1213 lineaments confirms a dominant NE–SW extensional regime (σ₃) that facilitated fluid conduits. The quantified marble-bearing horizon spans ~334 km² with a volume of 6.0 km³ (±9%). Spatial analysis reveals a causal link between high-grade marble clusters, basaltic intrusions, and NE–SW fault systems, refining models of contact metamorphism in rift-related settings. Full article

This paper investigated the mechanism and dynamic process of a significant water and mud inrush disaster that occurred in the Baiyunshan Tunnel, which crosses a karst fault zone. By integrating multi-source data including geological exploration and geophysical surveys, a three-dimensional geological model characterizing the cave–conduit–tunnel system was developed. A numerical approach coupling the Phase-Field and Particle-Tracking Methods was employed, successfully reconstructing the entire disaster process involving the transport of water-air-mud three-phase flow. Simulation results demonstrated that the dynamic viscosity of the mudflow predominantly controls the dynamic characteristics of the particle, such as transport distance and mudflow velocity. Parameter sensitivity analysis revealed quantitative relationships between key mudflow parameters (transport distance, velocity, and drag force) and the Reynolds number, identifying an exponential decay of drag force with increasing Reynolds number in high-viscosity mudflows. This study establishes a comprehensive methodology from geological identification to numerical simulation, providing a theoretical basis and technical support for precise risk assessment and the design of preventive measures for tunnel water and mud inrush disasters. Full article

High-efficiency electric motors represent a core enabling technology for sustainable industrial systems, providing substantial opportunities to reduce electricity consumption, operating costs, and associated greenhouse gas emissions across motor-driven processes. This paper presents a structured synthesis of recent progress in high-efficiency motor technologies within the IE3–IE5 efficiency classes, with emphasis on design innovations in electromagnetic optimization, advanced materials, and thermal management that collectively improve efficiency retention, reliability, and service lifetime under practical duty cycle conditions. Beyond component-level advances, the review analyses how high-efficiency motor–drive systems are being embedded within Industry 5.0 manufacturing environments, where human-centric automation and data-driven intelligence extend motor functionality toward adaptive, condition-aware operation. In this context, the integration of IoT-enabled sensing, AI-based analytics, and digital twin models supports predictive maintenance, real-time condition assessment, fault diagnostics, adaptive control, and duty cycle-responsive energy optimization, thereby improving both energy management and operational resilience. The paper also discusses implementation considerations that commonly constrain industrial adoption, including interoperability with legacy infrastructure, control architecture compatibility, data quality and model robustness, cybersecurity concerns, and lifecycle-oriented sustainability requirements such as material criticality and end-of-life pathways. Representative industrial case studies are synthesized to illustrate typical deployment architectures, observed implementation effects, and recurring technical challenges, together with practical mitigation strategies. This article advances the viewpoint that, under the Industry 5.0 paradigm, the value of high-efficiency motors is evolving from a component-level efficiency upgrade to a cyber-physical enabling asset that shapes lifecycle carbon performance and manufacturing resilience; realizing this shift requires integrated co-design spanning electromagnetics, thermodynamics, information science, and control. Full article

Rotating machinery is a crucial element of mechanical equipment, and during serving life, failures are inevitable due to human and non-human factors. Signal processing techniques serve as essential tools for diagnosing such faults. Among them, mathematical morphology (MM) has attracted considerable research interest in this domain owing to nonlinear filtering, simple computation rules and well-established theoretical foundation. Thus, numerous papers have been published in academic journals and conference proceedings. This review paper attempts to outline the morphological framework and to summarize these applications focusing on rolling element bearings and gears. Finally, it provides an analysis of the relevant discussions on MM, and suggests several potential prospects. Full article

In this paper, frequency control of shipboard microgrids is achieved in the presence of measurement noise, dynamic uncertainty, and actuator faults. Measurement noise arises from incorrect signal processing, electromagnetic interference, converter switching dynamics, mechanical vibrations from propulsion and generators, and transients caused by sudden changes in load or generation. Actuator faults are caused by intense mechanical vibrations, temperature-induced stress, degradation of power electronic devices, communication latency, and wear or saturation in fuel injection and governor components. To regulate the frequency deviation under these challenges, a cross-entropy-based fault-tolerant model predictive control method, utilizing a discrete-time linear Kalman filter, is developed. Firstly, the discrete-time linear Kalman filter ensures that uncertain states of the shipboard microgrids are measurable in a noisy environment. Afterward, the model predictive control scheme is employed to obtain an optimal control input based on the measurable states. This controller ensures the frequency regulation of shipboard microgrids in the presence of measurement noise. Furthermore, a fault-tolerant control technique that utilizes the concept of cross-entropy is extended to provide a robust controller that verifies the frequency regulation of shipboard microgrids with actuator faults. To demonstrate the stability of the closed-loop system of the shipboard microgrids based on the proposed controller, considering the effects of measurement noise, state uncertainty, and actuator faults, the Lyapunov stability concept is employed. Finally, simulation results in MATLAB/Simulink R2025b are provided to show that the proposed control method for frequency regulation in renewable shipboard microgrids is both effective and practicable. Full article

With the rapid development of the Internet of Vehicles (IoV), the explosive growth of data traffic within the system has led to a surge in demand for spectrum resources. However, the strict limitations on spectrum supply make the construction of an efficient and reasonable resource allocation scheme crucial for IoV. To maximize social benefits and improve security in the resource allocation process under IoV spectrum scarcity, this paper proposes a dynamic spectrum allocation (DSA) scheme based on a consortium blockchain framework. In this scheme, we design a demand-based vehicle priority classification method and propose a novel hybrid consensus mechanism—PhDPoR—which integrates practical byzantine fault tolerance (PBFT) and Hierarchical Delegated Proof of Reputation. Furthermore, we construct a multi-leader, multi-follower (MLMF) Stackelberg game model and utilize smart contracts to implement an immutable on-chain record of spectrum resource allocation, thereby deriving the optimal spectrum pricing and purchase strategy. Experimental results show that the proposed scheme not only effectively optimizes the utility of both supply and demand sides and improves overall social benefits while ensuring efficiency, but also significantly outperforms baseline algorithms in identifying and mitigating malicious nodes, thus verifying its feasibility and application value in complex IoV environments. Full article

The dynamic behavior of the DS306 detacher, a critical component in industrial fiber processing lines, plays a decisive role in maintenance performance and overall operational reliability. This study introduces a strengthened preventive maintenance strategy that leverages vibration analysis and dynamic modeling with a strong emphasis on early fault anticipation. A detailed numerical finite element model of the detacher was developed to determine its natural frequencies, critical modes, and dynamic response under real operating conditions. Experimental vibration measurements were conducted to validate the numerical model and identify characteristic frequencies associated with imbalance and wear. The results show that the proposed predictive framework not only reproduces the machine’s dynamic behavior with high accuracy but also anticipates mechanical degradation trends well before the occurrence of critical failures. This early-warning capability allows maintenance teams to plan interventions proactively, significantly reducing unexpected downtime, avoiding cascading damage, and improving long-term equipment availability. Overall, the study provides a robust and practical methodology for dynamic diagnosis, fault prediction, and optimized preventive maintenance in industrial rotating machinery. Full article

Tunnel ahead prospecting is important for reducing construction risks associated with faults, fractured zones, and cavities ahead of the tunnel face, but controlled active-source surveys are often impractical during continuous TBM operation. TBM drilling-noise records provide persistent passive excitation; however, strong nonstationarity and narrowband tonal contamination can hinder stable retrieval of interpretable impulse-like responses. We propose an adaptive impulse reconstruction algorithm that couples CEEMDAN-based mode screening with MDD interferometry. CEEMDAN screening suppresses quasi-stationary tonal components while preserving coherent propagation-related wavefields, producing effective signals suitable for interferometric processing. The MDD stage is stabilized using band-limited inversion, phase-only whitening, and a multi-reference strategy. Numerical experiments with a 3D elastic tunnel model indicate that the proposed workflow yields a more compact and laterally coherent virtual-source gather than correlation-based baselines (CC and PHAT-CC) and single-reference deconvolution interferometry, supporting reflection-oriented interpretation beyond simple wavelet compression. Field measurements from an operating TBM tunnel, together with a hammer-impact benchmark, are consistent with the feasibility of the workflow under real tunneling conditions and with physically plausible moveout behavior in the reconstructed gathers. Full article

This paper presents a novel edge-computing-based architecture for optimal inverse time overcurrent relays installed to protect mesh microgrids (MGs) with distributed generation. The procedure employs graph theory to automate the detection of network changes, fault locations, and relay pairs in an MG. In addition, an automated process obtains the initial protection settings based on the operating conditions of the MG. Furthermore, the Continuous Genetic Algorithm (CGA), Salp Swarm Algorithm (SSA), and Particle Swarm Optimization (PSO) were implemented to determine the optimal protection settings to obtain better coordination between primary and backup protection relays. These processes were implemented using PowerFactory 2024 Service Pack 5A and Python 3.13.1. The proposal was validated in 68 operating scenarios that considered the islanded and connected operation modes of the MG, charging and discharging cycles of electric vehicle stations, and the presence or absence of photovoltaic generation. The overcurrent protection relays were organized into 100 primary–backup relay pairs to ensure proper coordination and selectivity. The total miscoordination time (TMT) index was used to measure when all pairs of relays were coordinated, with a minimum time close to zero. The results of the graph theory show that all the meshes, fault locations, and relay pairs were identified in the MG. The approach successfully coordinated 100 relay pairs across 68 scenarios, demonstrating its scalability in complex real-world MGs. The automation process obtained an average TMT of 12.2%, while the optimization obtained a TMS of 91.6% with the CGA, and a TMT of 99% was obtained with the SSA and PSO, demonstrating the effectiveness of the optimization process in ensuring selectivity and appropriate fault clearing times. Full article

Single-crystal diamond (SCD) demonstrates immense potential in high-power electronics, quantum information, and precision sensing due to its exceptional hardness, high thermal conductivity, wide bandgap, and superior chemical stability. Focusing on the crystallographic dependence of chemical vapor deposition (CVD) synthesis, this review systematically examines the growth mechanisms, defect characteristics, and application progress of typical low-index planes, specifically (100), (111), and (110). The (100) plane, leveraging stable step-flow growth modes and a mature process window, has established itself as the primary orientation for large-size, high-quality homoepitaxy. Conversely, while the (111) plane faces challenges regarding growth rate and the suppression of twins and stacking faults, it offers unique advantages for high-efficiency doping and the preferential alignment of quantum color centers, such as NV and SiV centers. The (110) plane, characterized by its anisotropic surface structure and high effective growth rate, shows significant potential for textured film preparation, N-type doping epitaxy, and quantum sensing based on surface termination control. Furthermore, this article outlines progress in high-index planes (e.g., (113)) and hexagonal diamonds (HDs), highlighting their possibilities for rapid thick-film deposition, directional color center regulation, and novel superhard/quantum material design. Finally, from an integrated “Material-Defect-Device” perspective, we identify current critical scientific and engineering challenges, providing a roadmap for the synergistic optimization of crystal plane selection, defect engineering, and device structure. Full article

High-strength bolted connections are fundamental load-bearing components in critical engineering infrastructures such as wind turbines, bridges, and heavy machinery. Under complex service environments involving dynamic loading, vibration, corrosion, and temperature variations, bolts are prone to interacting failure mechanisms, including fatigue fracture, corrosion-assisted cracking, hydrogen embrittlement, and progressive preload loss, which pose significant challenges for reliable condition monitoring and early fault diagnosis. This review provides a structured synthesis of recent advances in bolt health monitoring and intelligent fault diagnosis. A unified framework is established to link multi-physics failure mechanisms with multi-modal sensing technologies and data-driven diagnostic methods. Key sensing approaches—such as piezoelectric impedance techniques, ultrasonic phased array inspection, and computer vision-based monitoring—are critically reviewed in terms of their physical principles, diagnostic capabilities, and limitations. Furthermore, the transition from traditional model-based and signal-processing-driven methods to machine learning- and deep learning-based approaches is examined, with emphasis on multi-modal data fusion, real-time monitoring, and lifecycle-oriented health management enabled by IoT and digital twin technologies. Finally, key challenges and future research directions toward robust and scalable intelligent bolt health management systems are outlined. This review’s primary contribution lies in establishing a novel, integrated framework that links failure physics to sensing and diagnosis, thereby providing a structured roadmap for transitioning from isolated component monitoring to lifecycle-oriented, intelligent health management systems for critical bolted connections. Full article

Conventional assessments of fault rupture propagation in overlying soil layers often rely on static or quasi-static analysis, neglecting the dynamic nature of fault displacement and inertial effects. This study develops a comprehensive simulation method for the entire process from rupture initiation to propagation under dynamic fault displacement. The method integrates a nonlinear elastic constitutive model based on the Hardin backbone curve with a non-uniform input technique for seismic waves on both sides of the fault using viscoelastic artificial boundaries. To demonstrate the distinct capabilities of this dynamic method, we conduct a comparative study on normal and reverse faulting driven by fault displacement time histories of identical magnitude but opposite sense. The simulations reveal that: (1) the fault displacement required for rupture initiation and propagation remains consistent between dynamic and quasi-static analyses; (2) crucially, the proposed method captures the transient dynamic response of fault rupture in the overlying soil. The study confirms that the proposed dynamic simulation framework is essential for resolving transient peak responses, oscillatory behavior, and deformation features associated with different faulting mechanisms, providing a more realistic tool for seismic risk assessment compared to conventional static approaches. Full article

During oilfield injection and production operations, fluid injection and withdrawal can significantly alter the stress state around faults, potentially triggering fault reactivation and even seismic events, which has become a focal issue in both industry and academia. In this study, based on fluid–solid coupling theory and the rate-and-state friction constitutive model, a mechanical framework was developed to evaluate fault shear slip behavior induced by injection–production activities. Numerical simulations were conducted using COMSOL Multiphysics to systematically investigate the effects of injection–production rate, operational schemes, well placement, reservoir permeability, and fault dip angle on fault stability. The results indicate that higher injection–production rates, non-steady operational schemes, injection wells located closer to faults, production wells farther from faults, lower fault core permeability, and larger fault dip angles can significantly enhance fluid pressure buildup and effective stress variations within the fault core zone. These processes lead to pronounced increases in Coulomb Failure Stress (CFS) and reductions in critical stiffness, thereby elevating the risk of fault instability and slip. Overall, the findings suggest that optimizing injection–production parameters and well placement can effectively mitigate the likelihood of fault reactivation. This study provides theoretical insights into the mechanisms of injection–production-induced fault slip and offers valuable references for safe oilfield operations and seismic risk assessment. Full article

Quantum computing has the potential to transform modern computation by offering exponential advantages in areas such as cryptography, optimization, and intelligent data processing. To effectively realize these advantages, particularly in fault-tolerant and Noisy Intermediate-Scale Quantum (NISQ) environments, quantum circuits must be both resource-efficient and error-resilient. This paper presents a novel Binary-Coded Decimal (BCD) to Excess-3 code converter designed exclusively using the Clifford+T gate set, which is widely supported by fault-tolerant quantum hardware. The proposed design eliminates conventional 4-bit reversible adder-based implementations and instead employs an optimized logic structure based on Clifford+T-decomposed Peres gates. By leveraging Temporary Logical-AND gates and CNOT operations, the circuit achieves reduced T-count, circuit depth, and quantum cost as key metrics in fault-tolerant quantum computation. Functional correctness is verified through IBM Qiskit, Version 2.1 simulations for all valid BCD inputs. The proposed converter serves as a scalable and hardware-compatible arithmetic building block for resource-aware and AI-oriented quantum architectures. Full article