Recent Advancements in Morphing Applications: Architecture, Artificial Intelligence Integration, Challenges, and Future Trends- A Comprehensive Survey

Aerospace Science and Technology, Journal Year: 2025, Volume and Issue: 161, P. 110102 - 110102

Published: Feb. 26, 2025

Language: Английский

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Decision-Making Framework for Aviation Safety in Predictive Maintenance Strategies

Applied Sciences, Journal Year: 2025, Volume and Issue: 15(3), P. 1626 - 1626

Published: Feb. 6, 2025

The implementation of predictive maintenance (PM) in aviation presents unique challenges due to strict safety requirements, complex operational environments, and regulatory constraints. This paper develops a comprehensive decision-making framework for evaluating the feasibility implementing PM aircraft components, addressing critical need systematic integration technical, economic, considerations. Through expert surveys involving 78 professionals application multi-criteria decision analysis, this study identifies validates 14 key criteria across four categories: technical operational, economic feasibility, compliance, organizational human factors. analytic hierarchy process is employed establish weights, with flight impact, reliability predictability, data sufficiency emerging as primary drivers. framework’s effectiveness demonstrated through case studies comparing turbofan engines avionics units, validating its ability discriminate between components suitable implementation. Results indicate that successful requires not only technological readiness but also alignment compliance. contributes practice by providing structured, evidence-based approach decisions, while establishing foundation future innovations strategies. practical applicability enhanced detailed roadmap validation methods, ensuring relevance decision-makers maintaining standards.

Language: Английский

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Framework for Addressing Imbalanced Data in Aviation with Federated Learning

Information, Journal Year: 2025, Volume and Issue: 16(2), P. 147 - 147

Published: Feb. 16, 2025

The aviation industry generates vast amounts of data across multiple stakeholders, but critical faults and anomalies occur rarely, creating inherently imbalanced datasets that complicate machine learning applications. Traditional centralized approaches are further constrained by privacy concerns regulatory requirements limit sharing among stakeholders. This paper presents a novel framework for addressing challenges in through federated learning, focusing on fault detection, predictive maintenance, safety management. proposed combines specialized techniques handling with privacy-preserving to enable effective collaboration while maintaining security. incorporates local resampling methods, cost-sensitive weighted aggregation mechanisms improve minority class detection performance. is validated extensive experiments involving demonstrating 23% improvement accuracy 17% reduction remaining useful life prediction error compared conventional models. Results show the enhanced rare faults, improved maintenance scheduling accuracy, risk assessment distributed datasets. provides scalable practical solution using both imbalance concerns, contributing operational efficiency industry.

Language: Английский

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Using Big Data for Predictive Maintenance in Transportation Systems

IGI Global eBooks, Journal Year: 2025, Volume and Issue: unknown, P. 297 - 314

Published: April 4, 2025

The economic and social health of contemporary urban centers is greatly dependent on the transportation industry. Transportation infrastructure must be dependable efficient because any disruptions can have a domino effect mobility as whole. Predictive maintenance, facilitated by analysis big data, gives chance to proactively address maintenance needs minimize service interruptions. use data analytics for predictive in systems examined this chapter. It starts going over special sources that are available industry, such sensor from infrastructure, cars, traffic control systems. explores essential phases procedure, encompassing gathering, analysis, modeling, production practical insights. application data-driven various contexts—such public fleets, road rail networks—is demonstrated through several case studies.

Language: Английский

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Construction and Application of an Agent-Based Intelligent Operation and Maintenance System for UAV

Drones, Journal Year: 2025, Volume and Issue: 9(4), P. 309 - 309

Published: April 16, 2025

As a crucial component in the evolution of modern warfare toward digitization and intelligentization, unmanned aerial vehicle (UAV) equipment requires more precise efficient operation maintenance (O&M) system. Based on Department Defense Architecture Framework (DoDAF) 2.0, integration Multi-Agent Systems (MAS) military simulation technology provides comprehensive, rational, feasible theoretical foundation for construction validation an intelligent O&M system UAV equipment. Firstly, starting from tasks warfare, this study analyzes capability requirements by following generation path scenarios, activities, capabilities. Three core capabilities are proposed: situational awareness, decision support, mission execution. Secondly, various decomposed into behaviors multiple types agents, based this, is designed using “cloud-edge-terminal” distributed architecture. Finally, simulations conducted to model validate tasks. Experimental results demonstrate that MAS-based significantly enhances support efficiency, accuracy, response speed, offering novel solution future operations.

Language: Английский

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AI-driven propulsion optimization with a graph neural network-based framework for service level agreement prediction and performance enhancement in aerospace systems

Aircraft Engineering and Aerospace Technology, Journal Year: 2025, Volume and Issue: unknown

Published: April 21, 2025

Purpose This paper aims to suggest a graph neural network (GNN)-based framework named GraphSLA for the purpose of enhancing real-time (RT) service level agreement (SLA) predictions, is suggested in this study. method optimize aerospace propulsion systems. Design/methodology/approach The software-defined networking (SDN) used by framework. Here, wide area (WAN) configuration with five SDN controllers and one OpenFlow controller are included method. Scheduling rules, control algorithms operating conditions analyzed GNN identifying relationships among fuel consumption, thermal dynamics engine performance. Mean square error, coefficient determination, mean absolute error (MAE) percentage (MAPE) were validation metrics. Findings From outcomes, 98% prediction accuracy 99.12% high fault detection 98.43% management efficiency was attained GraphSLA. Thus, it clear from outcomes that have potential ensuring RT effective system. Originality/value work pioneers applying GNNs SLA-based optimization systems, offering transformative approach autonomous, adaptive operational sustainability.

Language: Английский

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Cybersecurity as a Strategic Lever for Innovation in Smart Aviation Ecosystems

IGI Global eBooks, Journal Year: 2025, Volume and Issue: unknown, P. 103 - 122

Published: May 14, 2025

As aviation undergoes digital transformation, cybersecurity has become essential to innovation and operational resilience. This chapter reviews recent advancements in across predictive maintenance, unmanned aircraft traffic management (UTM), smart airport systems. Technologies like AI improve threat detection, blockchain ensures data integrity, quantum cryptography enhances communication security. The also highlights the importance of organizational factors—such as leadership culture—in achieving Key challenges include fragmented governance cyber vulnerabilities low-altitude operations. Despite these, strategic opportunities exist integrate into long-term plans. Overall, positions not just protection, but a driver sustainable, secure, forward-looking management.

Language: Английский

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Unified Aviation Maintenance Ecosystem on the Basis of 6G Technology

Electronics, Journal Year: 2024, Volume and Issue: 13(19), P. 3824 - 3824

Published: Sept. 27, 2024

The advent of 6G technology will transforms aviation, particularly in the realm aircraft health monitoring systems (AHMSs). This paper explores transformative potential enhancing real-time data exchange, predictive maintenance, and overall communication efficiency within aviation sector. By using ultra-fast transmission, low latency, advanced AI integration, enables development a unified AHMS architecture that significantly improves safety, operational efficiency, reliability. proposed eight-layer model, incorporating digital twins, federated learning, edge computing, showcases how can revolutionize maintenance by providing continuous, decision-making capabilities.

Language: Английский

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Predictive Maintenance in Aerospace Industry Using Convolutional Neural Network

Published: Aug. 13, 2024

Language: Английский

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NFT-Based Framework for Digital Twin Management in Aviation Component Lifecycle Tracking

Algorithms, Journal Year: 2024, Volume and Issue: 17(11), P. 494 - 494

Published: Nov. 2, 2024

The paper presents a novel framework for implementing decentralized algorithms based on non-fungible tokens (NFTs) digital twin management in aviation, with focus component lifecycle tracking. proposed approach uses NFTs to create unique, immutable representations of physical aviation components capturing real-time records component’s entire lifecycle, from manufacture retirement. This outlines detailed workflows key processes, including part tracking, maintenance records, certification and compliance, supply chain management, flight logs, ownership leasing, technical documentation, quality assurance. introduces class designed manage the complex relationships between components, their twins, associated NFTs. A unified model is presented demonstrate how are created updated across various stages ensuring data integrity, regulatory operational efficiency. also discusses architecture system, exploring sources, blockchain, NFTs, other critical components. It further examines main challenges NFT-based future research directions.

Language: Английский

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