Search Results (230)

The automatic inspection of airport infrastructure, specifically horizontal surface markings and Airfield Ground Lighting (AGL), is a critical task for maintaining aviation safety and operational efficiency. As the aviation industry shifts from manual surveys to automated visual inspections utilizing unmanned aerial vehicles (UAVs) and smart service vehicles, the demand for robust, real-time computer vision systems has surged. However, deploying computationally intensive deep learning models in the field introduces severe Size, Weight, and Power (SWaP) constraints. This paper presents a comprehensive framework for the semantic segmentation of runway/taxiway markings and the point-localization of AGL lamps, specifically focusing on the deployment paradigm shift from the expensive, GPU-accelerated heterogeneous system on chip (SoC) to highly efficient, dedicated Neural Processing Units (NPUs). We evaluate the performance of U-Net, LinkNet, U-Net-Point and HRNet-Lite-Point architectures trained on a custom dataset from the Poznań-Ławica Airport. Crucially, this study conducts a rigorous comparative hardware analysis between the flagship NVIDIA Jetson AGX Orin and a highly cost-effective heterogeneous setup comprising a Raspberry Pi 5 augmented with an NPU Hailo-8 AI accelerator. Experimental results demonstrate that while both platforms achieve real-time inference, the Hailo-8 integration fundamentally disrupts the traditional cost-to-performance ratio. Furthermore, the Hailo-8 configuration consumed less electrical power and memory footprint required by the Jetson, proving that dedicated NPUs are vastly superior for continuous, battery-operated edge deployment in autonomous airport maintenance systems. Specifically, the Raspberry Pi setup with the Hailo-8 accelerator demonstrated superior energy efficiency, requiring a significantly lower energy consumption per processed video frame compared to the Jetson platform. Full article

Aviation ground safety and protective devices are critical for flight safety; however, their unintentional retention on aircraft after maintenance remains a persistent risk. Existing deep learning-based approaches for aviation safety have predominantly followed a reactive paradigm, detecting FOD on runways or inspecting the aircraft for inadvertently retained tools post-maintenance. In contrast, this paper advocates a proactive philosophy: using a neural network to recognize and inventory all ground safety and protective devices immediately after maintenance closure, thereby preventing retention incidents at their source. However, realizing this proactive verification is technically challenging—object detection for these devices often suffers from loss of fine-grained detail due to downsampling and inherently sparse semantic information of the targets. To this end, we propose an Information Retention and Feature Screening Synergistic Network (RS-Net) grounded in information bottleneck theory. The network comprises a main branch that enhances discriminative features through attention-guided screening, and an auxiliary branch, used only during training, that preserves fine-grained spatial details via information-retentive convolutions. A Dual-State Region Refinement Module (DRM) provides configurable support for both branches, decoupling the conflicting objectives of background compression and detail preservation. Experiments on a self-constructed dataset collected from real airline maintenance operations demonstrate that RS-Net substantially outperforms the strong YOLOv9 baseline, achieving gains of 4.531% in F1-score, 2.533% in mAP_0.5, and 1.429% in mAP_0.5:0.95. Cross-dataset experiments further validate its strong generalization capability. Full article

Airport Economic Zones (AEZs) in China are largely guided by central and local government planning, and official planning documents provide important textual signals of industrial priorities. To identify these priorities, this study examines 17 national-level Airport Economic Demonstration Zones (AEDZs) and collects official documents on industrial development issued by central authorities and relevant local governments. Using dictionary-based named entity recognition, word-frequency analysis, clustering, and association rule mining, we quantitatively analyze stated governmental preferences in AEDZ industrial planning, focusing on emphasized industries and their combinations. The results show that local governments frequently emphasize industries prioritized by the central government, including aviation equipment manufacturing and maintenance, electronic information, aviation logistics, professional exhibitions, and e-commerce. They also tend to combine high-end intelligent manufacturing, electronic information technology services, and air cargo transportation in planning narratives. These patterns indicate policy alignment across AEDZs, while high similarity in stated industrial priorities may signal potential risks of redundant construction and resource misallocation if not matched with differentiated implementation. The findings are interpreted as textual policy signals rather than evidence of actual implementation or industrial performance. Full article

High-value aviation rotable parts require accurate demand forecasting because each failure simultaneously creates a replacement demand, a repair workload, and a potential aircraft on-ground (AOG) risk. This study develops a continuous-time Markov chain (CTMC) queuing framework for forecasting demand and optimizing target stock levels under realistic maintenance, repair, and overhaul (MRO) capacity constraints. Historical service-engineering records from B737 mechanical engine control (MEC) units are first benchmarked and filtered to retain inherent random failures during the useful-life phase. A Kolmogorov-Smirnov goodness-of-fit test is then used to verify the exponential time-between-failure assumption required by the Markov model. Based on this validated failure-pattern classification, the study derives steady-state probability models for both an ideal $M/M/\infty$ repair system and a finite-capacity $M/M/c$ repair system. The main contribution is not the isolated use of Weibull or exponential reliability models, Markov chains, or inventory optimization, which are established methods, but their auditable integration into an airline rotable-parts workflow that links failure-pattern screening, finite repair capacity, service-level constraints, and engineering validation. The empirical results show that repair bottlenecks shift probability mass toward higher numbers of failed units, create a fat-tailed backlog distribution, and double the required target stock from 6 to 12 MEC units under a 95% service-level requirement. Sensitivity experiments further show that repair turnaround time, fleet size, and MRO channel capacity jointly determine the inventory-capacity cost trade-off. The proposed framework provides an interpretable decision tool for airlines to align spare-part procurement with actual repair-system performance. Full article

Over the years, industrial demands have determined the main course of electric drives research and development. Modern drive trains are forced to provide extremely efficient operation under a variety of unfavorable circumstances. Moreover, the maintenance of the drive is often a critical factor, including both its reliability in the long-term perspective and deployment costs. In addition, the sophistication of up-to-date industrial machinery increases the number of stochastic disruptions that affect the final control quality. Thus, the Control Theory satisfies the need for a novel, robust strategy by proposing the Active Disturbance Rejection Control (ADRC) algorithm. It stands out with great dynamic performance and versatility. It has been widely tested in a variety of different industrial applications, including aviation, autonomous and unmanned vehicles, marine robots, automotive solutions, renewable energy, and power systems. Many of the above-mentioned applications use electric drive units. This paper elaborates on the review of the current state-of-the-art in the field of electric drive control with the ADRC strategy employed. Then, the ADRC designs regarding multi-mass drive trains are reviewed with emphasis on the speed control issue. This paper evaluates its variants and control approaches depending on the application purpose. Moreover, an exemplary dynamic properties analysis is performed to verify the default effectiveness of the algorithm. Then, the summary section is followed by an indication of possible future research directions. Full article

High-energy-density fuels (HEDFs) have garnered considerable interest in aerospace fields, primarily due to their superior density and volumetric net heat of combustion (NHOC) compared with traditional petroleum-based fuels. Strained polycyclic hydrocarbons are regarded as one of the most crucial categories of HEDF. As an isomer (C₁₀H₁₆) of JP-10, the target compound is composed of two cyclopropyl rings and one cyclobutyl ring connected in a linear manner. Notably, intramolecular H-migration reactions of peroxyl radicals derived from strained polycyclic hydrocarbons (C₁₀H₁₅OO•) are of great significance for establishing the reaction mechanism of high-energy-density fuels over a broad temperature range. In this work, the intramolecular H-migration kinetics of C₁₀H₁₅OO• with distinct cis and trans configurations are investigated by quantum chemical calculations. Geometry optimization and frequency calculations are carried out for all species using the M06-2X/6-311++G(d,p) level of theory, while single-point energy calculations are performed at the CBS-QB3 level. Our calculated results demonstrate that different types of intramolecular H-migration reactions exhibit significant differences in barrier heights. Based on the ring structures where the reaction centers are located, these reactions can be classified into three categories: the lowest barriers correspond to H-migration reactions occurring between the central cyclopropyl ring and the terminal cyclobutyl ring; the highest barriers correspond to H-migration reactions confined entirely within the terminal cyclobutyl ring; and the barriers for H-migration reactions occurring between the terminal cyclopropyl ring and the central cyclopropyl ring lie between the above two. High-pressure-limit rate constants for 33 elementary reactions are determined in the temperature range of 500 to 2500 K based on the conventional transition-state theory (TST) and expressed in the modified Arrhenius form. Full article

The aviation industry is adopting liquid hydrogen (LH₂) for sustainable flight, requiring robust safety systems. This work is an example of adaptation of a Micro-Electro-Mechanical Systems (MEMS)-based structural health monitoring (SHM) system for LH₂ tanks, developed in the H2ELIOS project. It uses a multisensor approach that combines MEMS sensors to monitor vibration and acceleration, shape memory alloy (SMA) strain sensors for measuring tank expansion, and hydrogen leakage sensors to prevent false alarms. This SHM technology detects cracks and delamination of material and coating, enabling predictive maintenance via digital twins and ensuring structural integrity. Full article

Inverters are widely applied in aviation, distributed power grids, and vehicles, where their health status directly impacts the stable operation of entire systems. Existing health assessment methods suffer from poor real-time performance, require additional measurement circuits, and are prone to misjudgment, while failing to adequately address slow degradation behaviors during inverter operation. To address these challenges, this study proposes an inverter health assessment method based on an improved D-S evidence theory. First, based on the practical requirements of subway auxiliary inverters, 13 key evaluation indicators were selected. Subjective weights were obtained using the Analytic Hierarchy Process (AHP), while objective weights were derived through the Critic method, credibility, and falsity weighting. These were then fused using game theory to obtain composite weights. Next, after data normalization, a ridge-type membership function was employed to describe health state uncertainty. Finally, the improved D-S evidence theory integrates multi-source information to achieve online health status assessment. Experimental validation demonstrates that this method effectively evaluates the impact of IGBT failures, sensor malfunctions, and capacitor–inductor degradation on the inverter. It exhibits strong robustness under DC voltage fluctuations and load variations, enabling real-time output of health scores and grades to provide a reliable basis for maintenance decisions. Full article

Aircraft digital twins are increasingly used for diagnostics, prognostics, and predictive maintenance, but their role as lifecycle-oriented, multi-stakeholder decision-support ecosystems remains insufficiently developed. This paper addresses this gap by proposing a conceptual systems-engineering framework for an aircraft digital twin ecosystem supporting sustainable aviation transport management. The framework integrates physics-based, data-driven, hybrid, probabilistic, and federated modelling approaches and includes a three-layer ecosystem model, formal mathematical representation of aircraft and digital twin lifecycle evolution, federated model updating, lifecycle decision-support scenarios, reference architecture, validation and trustworthiness principles, and a five-level maturity model. Representative aviation industrial cases are used to interpret the framework. The analysis shows that current industrial practice already contains elements of predictive maintenance, fleet analytics, engine health monitoring, and cloud-enabled MRO optimization, but full aircraft-level lifecycle governance, sustainability trade-off analysis, federated validation, and multi-stakeholder decision orchestration remain underdeveloped. The proposed framework positions aircraft digital twins as asset-level instruments for lifecycle planning, coordinated governance, and sustainability-oriented decision support. Full article

This paper examines the transformation of traditional aircraft maintenance into an AI-driven, digitized process through the evolution of the Onboard Maintenance System (OMS). It conceptualizes the OMS as an “airborne e-hangar,” where embedded artificial intelligence functions operate as virtual engineering teams performing continuous monitoring, diagnostics, and predictive maintenance during flight. Using the literature review synthesis of aviation regulations, technical manuals, and industry practices, the study outlines how OMS integrates subsystems, such as condition monitoring, central maintenance, and electronic logbooks, to enable real-time data processing and fault isolation. Findings highlight that AI-enhanced OMS improves maintenance efficiency, reduces human error, and supports proactive decision-making by converting operational data into actionable insights. The system facilitates seamless data exchange between aircraft and ground operations, enhancing troubleshooting, maintenance planning, and airworthiness compliance. Furthermore, the continuous feedback loop among manufacturers, maintenance organizations, and regulatory authorities contributes to improved aircraft reliability and design optimization. The study underscores the role of AI in minimizing downtime, optimizing maintenance schedules, and enhancing flight safety while maintaining human oversight through advanced interfaces. The originality lies in framing OMS as a fully digitized, intelligent maintenance ecosystem that redefines aircraft maintenance practices and supports safer, more efficient aviation operations. Full article

Sustainable aviation supply chains (SCs) are increasingly exposed to risks arising from environmental regulations, social responsibility pressures, and economic uncertainties. These risks are associated with different SC members and may propagate through operational dependencies among suppliers, maintenance service providers, and airline operators. To support systematic risk assessment, this study proposes a hybrid Analytical Hierarchy Process-Bayesian network (AHP-BN) framework for sustainable aviation SC risk management. The intended contribution is a contextual and structural extension of existing AHP-BN logic to member-level sustainability risk propagation in aviation SCs, rather than a claim that AHP-BN integration itself is fundamentally new. The proposed framework first classifies sustainability risks into environmental, social, and economic dimensions and identifies the risk exposure relationship between SC members and risk factors. For the weighting component, Analytical Hierarchy Process (AHP) is used to derive relative importance weights from specified illustrative pairwise comparison matrices in the numerical experiment. Bayesian network (BN) is employed to model probabilistic dependencies among nodes defined by SC members and risk factors. The two methods are coupled through a weighted expected risk index, which integrates AHP-derived weights, member-specific exposure intensities, probabilities inferred by BN, and losses associated with different risk states. A numerical illustration based on a synthetic aviation SC with suppliers, maintenance service providers, and airline operators is conducted to demonstrate the computational procedure and diagnostic use of the proposed framework rather than to validate an empirical risk profile of the aviation industry. Within this illustrative setting, cost volatility, supplier reliability, emissions regulation, and sustainable aviation fuel availability emerge as the major contributors to the overall risk index under the assumed inputs. The analysis further indicates that the proposed framework can identify critical active pairs of SC members and risk factors, reveal vulnerabilities at the levels of SC members and sustainability dimensions, and provide a transparent decision-support tool for sustainable aviation SC risk assessment, while the resulting rankings should be interpreted as conditional outputs under the assumed input parameters. Full article

This paper aims to develop an on-board shock absorber detection method for general aviation aircraft. The effects of common gas and oleo leakage are analyzed in this paper. Based on the principle of landing gear dynamics, it is found that gas leakage and oleo leakage would mainly affect air spring force of shock absorbers in various ways. A rigid–flexible coupled landing gear multi-body system (MBS) model is developed by considering strut flexibility, aiming to offer more accurate simulated responses. A database is developed that considers common leakage faults and typical landing conditions using the developed landing gear model. A deep learning model is proposed in this paper. The proposed model is trained and tested using the database simulated from the rigid–flexible coupling landing gear model. The proposed method demonstrates robust detection performance, achieving over 95% precision for most fault types. This work provides a practical, sensor-efficient solution for real-time health monitoring of landing gear shock absorbers, contributing to improved maintenance strategies and operational safety for general aviation aircraft. As this is a preliminary feasibility study, full validation requires future drop tests or instrumented flight tests. Full article

High-velocity multi-projectile impacts from accidental external debris (e.g., uncontained engine debris or runway stones) on the liquid-filled fuel tanks of modern unmanned aerial vehicles (UAVs) induce complex Fluid–Structure Interaction (FSI) and Hydrodynamic Ram (HRAM) effects, resulting in highly complex dynamic response mechanisms. This study combines high-velocity impact tests with Three-Dimensional Digital Image Correlation (3D-DIC) technology and employs FSI finite element simulations based on the Structured Arbitrary Lagrangian–Eulerian (S-ALE) algorithm to thoroughly investigate the dynamic response mechanisms of liquid-filled containers penetrated by dual projectiles under different spatial spacings and temporal intervals. The results indicate that variations in the spatiotemporal parameters of dual projectiles significantly reconstruct the fluid load field: small spacing and short temporal intervals induce strong wave interference and superposition, generating an amplified composite loading effect that causes a sharp increase in target plate impulse and deformation energy. Conversely, small spacing and long temporal intervals trigger a significant “cavity shielding” phenomenon, causing the subsequent projectile to travel through the existing cavity, which massively suppresses the effective generation of its load and energy transfer. Furthermore, fluid displacement induced by cavity intersection generates secondary pressure waves; the petal hole evolution of the rear plate is dictated by the formation of plastic hinge lines, presenting four typical deformation modes—oblique cross, normal cross, asymmetric pentagon, and hexagon—depending on the degree of spatiotemporal coupling. This study reveals the laws governing the enhanced HRAM effect of dual projectiles, providing key theoretical support for the lightweight protection design and crashworthiness evaluation of long-endurance commercial UAV fuel tanks. Full article

Across four decades of AI deployment, the same six human challenges (trust calibration, reliance behavior, cognitive engagement, skill retention, accountability, and transparency) recur, yet fragmentation across research communities obscures this continuity and limits knowledge transfer. Functionally similar phenomena are repeatedly relabeled (a jangle fallacy): what aviation researchers call “automation complacency,” decision scientists call “algorithm appreciation,” and LLM researchers describe as “over-reliance.” This systematic review synthesizes 152 papers spanning aviation, healthcare, manufacturing/supply chain, and cross-domain contexts across three AI technology generations: decision support systems, autonomous systems, and large language model (LLM) agents. We introduce the Collaboration Convergence Framework (CCF), a 6 × 3 matrix with solution-maturity indicators that maps each challenge across generations. The framework shows that Gen 3 designers can transfer decades of evidence from automation and decision support research (particularly reliance calibration, cognitive forcing, and skill maintenance) rather than rediscovering them. Cross-generational synthesis also isolates three Gen 3 phenomena without direct precedent in earlier generations: epistemia (attributing genuine knowledge to LLMs based on surface fluency), attribution ambiguity in co-creation, and motivational withdrawal. We distill twelve transferable design principles and propose ten research directions, prioritizing skill-retention interventions and accountability frameworks. These findings carry direct sustainability implications aligned with Industry 5.0: protecting workforce capability under increasing automation (SDG 8), reducing duplicated research effort through cross-generational knowledge reuse (SDG 9), and supporting responsible deployment by treating collaboration risks as predictable rather than novel (SDG 12). The CCF provides conceptual infrastructure for cumulative learning across AI generations and industries. Full article

Herringbone gear pairs are critical in high-speed aerospace transmissions, where windage power loss significantly impacts efficiency and thermal management. This study proposes a prediction method that decomposes the total windage loss into five components based on structural features: the tooth, end, circumferential, and relief groove surface losses for both gears, and the meshing extrusion loss. Theoretical models for each component are established to form a complete prediction method using fluid–structure interaction principles. CFD simulations analyze the velocity, pressure, and energy fields around the gear pair, with windage loss integrated via fluid torque on gear surfaces. Results indicate that windage loss escalates rapidly and becomes non-negligible when the driving gear speed exceeds 7000 rpm. The prediction model demonstrates strong agreement with CFD simulations, with a maximum relative error of 13.6%. Analysis reveals that the driving gear contributes the largest share of the total gear pair loss, with meshing extrusion accounting for 20.1–23.6%. For a single herringbone gear, the tooth surface is the primary source of loss (~83%), followed by the end surface (~8%), while relief groove and circumferential losses remain below 10%. This research provides a validated theoretical foundation for optimizing efficiency and thermal control in high-speed aerospace gear systems. Full article