Search Results (114)

The classical V-Model has served as the foundational framework for aerospace systems engineering, but its scope terminates upon aircraft certification, creating a significant gap in addressing reliability degradation during operational service. This study introduces the W-model framework—a comprehensive lifecycle management approach that extends the V-Model to systematically integrate reliability-centered component modifications with established aerospace development practices. The W-model incorporates a structured six-phase reliability-centered modification methodology that transforms operational data into certified design improvements through systematic reliability monitoring, candidate selection, design reviews, development, and certification processes. A detailed case study on the aviation pneumatic bypass valve demonstrates the methodology. Application of the W-model resulted in a 36% improvement in the mean time between failures and a significant reduction in unscheduled removals. The W-model represents a paradigm shift from reactive maintenance strategies to proactive, data-driven reliability enhancement, providing a systematic approach that maintains the rigor and traceability required for commercial aviation while enabling continuous reliability growth throughout the complete aircraft lifecycle. Full article

Prognostic and Health Management (PHM) strategies are gaining increasingly more traction in almost every field of engineering, offering stakeholders advanced capabilities in system monitoring, anomaly detection, and predictive maintenance. Primary flight control actuators are safety-critical elements within aircraft flight control systems (FCSs), and currently, they are mainly based on Electro-Hydraulic Actuators (EHAs) or Electro-Hydrostatic Actuators (EHSAs). Despite the widespread diffusion of PHM methodologies, the application of these technologies for EHAs is still somewhat limited, and the available information is often restricted to the industrial sector. To fill this gap, this paper provides an in-depth analysis of state-of-the-art EHA PHM strategies for aerospace applications, as well as their limitations and further developments through a Systematic Literature Review (SLR). An objective and clear methodology, combined with the use of attractive and informative graphics, guides the reader towards a thorough investigation of the state of the art, as well as the challenges in the field that limit a wider implementation. It is deemed that the information presented in this review will be useful for new researchers and industry engineers as it provides indications for conducting research in this specific and still not very investigated sector. Full article

The traditional approach that separates remaining useful life prediction from maintenance strategy design often fails to support efficient decision-making. Effective maintenance requires a comprehensive consideration of prediction accuracy, cost control, and equipment safety. To address this issue, this paper proposes a multi-objective maintenance optimization method based on stochastic modeling. First, a multi-sensor data fusion technique is developed, which maps multidimensional degradation signals into a composite degradation state indicator using evaluation metrics such as monotonicity, tendency, and robustness. Then, a linear Wiener process model is established to characterize the degradation trajectory of equipment, and a closed-form analytical solution of its reliability function is derived. On this basis, a multi-objective optimization model is constructed, aiming to maximize equipment safety and minimize maintenance cost. The proposed method is validated using the NASA aircraft engine degradation dataset. The experimental results demonstrate that, while ensuring system reliability, the proposed approach significantly reduces maintenance costs compared to traditional periodic maintenance strategies, confirming its effectiveness and practical value. Full article

Structural Health Monitoring (SHM) is an essential technique for continuously assessing structural conditions using integrated sensor systems during operation. SHM technologies have evolved to address the increasing demand for efficient maintenance strategies in advanced engineering fields, such as civil infrastructure, aerospace, and transportation. However, developing a miniaturized, cost-effective, and multi-sensor solution based on Wireless Sensor Networks (WSNs) remains a significant challenge, particularly for SHM applications in weight-sensitive aerospace structures. To address this, the present study introduces a novel WSN-based Multi-Sensor System (MSS) that integrates multiple sensing capabilities onto a 3 × 3 cm flexible Printed Circuit Board (PCB). The proposed system combines a Piezoelectric Transducer (PZT) for impact detection; a strain gauge for mechanical deformation monitoring; an accelerometer for capturing dynamic responses; and an environmental sensor measuring temperature, pressure, and humidity. This high level of functional integration, combined with real-time Data Acquisition (DAQ) and precise time synchronization via Bluetooth Low Energy (LE), distinguishes the proposed MSS from conventional SHM systems, which are typically constrained by bulky hardware, single sensing modalities, or dependence on wired communication. Experimental evaluations on composite panels and aluminum specimens demonstrate reliable high-fidelity recording of PZT signals, strain variations, and acceleration responses, matching the performance of commercial instruments. The proposed system offers a low-power, lightweight, and scalable platform, demonstrating strong potential for on-board SHM in aircraft applications. Full article

The increasing complexity of aircraft systems and the growing global demand for certified maintenance personnel necessitate a fundamental shift in aviation training methodologies. This paper proposes a comprehensive digital-twin-based training ecosystem tailored for aviation maintenance education. The system integrates three core digital twin models: the learner digital twin, which continuously reflects individual trainee competence; the ideal competence twin, which encodes regulatory skill benchmarks; and the learning ecosystem twin, a stratified repository of instructional resources. These components are orchestrated through a real-time adaptive engine that performs multi-dimensional competence gap analysis and dynamically matches learners with appropriate training content based on gap severity, Bloom’s taxonomy level, and content fidelity. The system architecture uses a cloud–edge hybrid model to ensure scalable, secure, and latency-sensitive delivery of training assets, ranging from computer-based training modules to high-fidelity operational simulations. Simulation results confirm the system’s ability to personalize instruction, accelerate competence development, and support continuous regulatory readiness by enabling closed-loop, adaptive, and evidence-based training pathways in digitally enriched environments. Full article

This paper presents an integrated digital training twin framework for adaptive aircraft maintenance education, combining real-time competence modeling, algorithmic orchestration, and cloud–edge deployment architectures. The proposed system dynamically evaluates learner skill gaps and assigns individualized training resources through a multi-objective optimization function that balances skill alignment, Bloom’s cognitive level, fidelity tier, and time efficiency. A modular orchestration engine incorporates reinforcement learning agents for policy refinement, federated learning for privacy-preserving skill analytics, and knowledge graph-based curriculum models for dependency management. Simulation results were conducted on the Pneumatic Systems training module. The system’s validation matrix provides full-cycle traceability of instructional decisions, supporting regulatory audit-readiness and institutional reporting. The digital training twin ecosystem offers a scalable, regulation-compliant, and data-driven solution for next-generation aviation maintenance training, with demonstrated operational efficiency, instructional precision, and extensibility for future expansion. Full article

Braking systems are essential to aircraft safety and operational efficiency; however, the variability of carbon brake wear, driven by the intricate interplay of operational and environmental factors, presents challenges for effective maintenance planning. This effort leverages machine learning classifiers to predict wear severity using operational data from an airline’s wide-body fleet equipped with wear pin sensors that measure the percentage of carbon pad remaining on each brake. Aircraft-specific metrics from flight data are augmented with weather and airport parameters from FlightAware^® to better capture the operational environment. Through a systematic benchmarking of multiple classifiers, combined with structured hyperparameter tuning and uncertainty quantification, LGBM and Decision Tree models emerge as top performers, achieving predictive accuracies of up to 98.92%. The inclusion of environmental variables substantially improves model performance, with relative humidity and wind direction identified as key predictors. While machine learning has been extensively applied to predictive maintenance contexts, this work advances the field of brake wear prediction by integrating a comprehensive dataset that incorporates operational, environmental, and airport-specific features. In doing so, it addresses a notable gap in the existing literature regarding the impact of contextual variables on brake wear prediction. Full article

Estimating Remaining Useful Life (RUL) is crucial in modern Prognostic and Health Management (PHM) systems providing valuable information for planning the maintenance strategy of critical components in complex systems such as aircraft engines. Deep Learning (DL) models have shown great performance in the accurate prediction of RUL, building hierarchical representations by the stacking of multiple explicit neural layers. In the current research paper, we follow a different approach presenting a Deep Equilibrium Model (DEM) that effectively captures the spatial and temporal information of the sequential sensor. The DEM, which incorporates convolutional layers and a novel dual-input interconnection mechanism to capture sensor information effectively, estimates the degradation representation implicitly as the equilibrium solution of an equation, rather than explicitly computing it through multiple layer passes. The convergence representation of the DEM is estimated by a fixed-point equation solver while the computation of the gradients in the backward pass is made using the Implicit Function Theorem (IFT). The Monte Carlo Dropout (MCD) technique under calibration is the final key component of the framework that enhances regularization and performance providing a confidence interval for each prediction, contributing to a more robust and reliable outcome. Simulation experiments on the widely used NASA Turbofan Jet Engine Data Set show consistent improvements, with the proposed framework offering a competitive alternative for RUL prediction under diverse conditions. Full article

The module division scheme of commercial aircraft and other complex system products has a significant impact on the functionality, performance, and cost of the aircraft. To obtain scientifically rational modular division solutions for commercial aircraft, this study establishes an Analytic Hierarchy Process–Gray Fuzzy Comprehensive Evaluation (AHP-GFCE) model by integrating hierarchical analysis method and gray fuzzy evaluation theory. This model develops a comprehensive evaluation methodology for aircraft modular division schemes. The proposed method was applied to evaluate the structural modular division scheme of the nose structure section of a certain type of aircraft. Results demonstrate that the AHP-GFCE model successfully identified the optimal nose structure modular division scheme. Compared with traditional installation processes, this optimal solution achieves a 40% improvement in overall assembly efficiency and a 25% reduction in total production cycle duration while better aligning with the engineering and manufacturing requirements of nose structure fabrication, thus revealing the superiority of the AHP-GFCE model in modular division evaluation. This research provides novel insights for modular division schemes of complex system products like commercial aircraft, and the methodology can be extended to modular maintenance domains of sophisticated products such as aero-engines. Although there remains room for model refinement, the findings carry significant theoretical and practical implications for modular division of complex system products. Full article

This article presents the development and implementation of an automated digital test bench for fault diagnosis in the caution and warning panels of the UH-60 helicopter, using practices based on NASA’s systems engineering process. The research addresses the critical need to improve efficiency and accuracy in aeronautical maintenance by automating processes traditionally relying on manual techniques. Throughout the study, advanced software engineering methodologies were implemented to develop a system that significantly reduces diagnostic times and enhances the accuracy and reliability of results by integrating digital signal processing. The article highlights the economic benefits, demonstrating a substantial reduction in repair costs, and emphasizes the system’s flexibility to adapt to other aeronautical components, establishing a solid foundation for future technological innovations in aircraft maintenance. The novelty of this paper lies in integrating real-time simulation with a closed-loop diagnostic system designed primarily for the UH-60 avionics panels. This approach has not previously been applied to this series of aircraft or aeronautical components, allowing for adaptive and automated fault detection and significant improvement in diagnostic accuracy and speed in unscheduled aeronautical maintenance environments. Full article

The author reviewed and classified maintenance reports that cited smoke, odor, or fumes (SOFs) that US airlines sent to the FAA over four years between 2018 and 2023. The US fleet composition was also calculated to put the number of SOF reports on each aircraft type in perspective. “Fume events” (engine oil or hydraulic fluid) were the most common type of onboard SOFs reported by US airlines (43%), followed by electrical (20%), and fans (6.1%). During these years, A320fam aircraft made up 20% of the US fleet but 80% of the reported fume events. Conversely, B737fam aircraft made up 27% of the US fleet but only 3.0% of the reported fume events. Aircraft design features, airline reporting practices, and maintenance procedures that may contribute to these differences were reviewed. Pilots were most likely to document a fume event during descent (47%) and takeoff/climb (19%). The A320fam, MD80fam, A330, and ERJ140-145 aircraft were over-represented in other types of SOFs reports. Airline narratives show that the APU can be the primary source of oil/hydraulic fumes, even when it is not operating. Additionally, failure to find the source of fumes, rectify it, and clean any secondary sources of fumes can cause repeat events. Full article

Advanced precision gap measurement technologies play a pivotal role in ensuring the design and operational efficiency of aerospace systems. Gaps between aircraft components directly influence assembly accuracy, performance, and safety. This review comprehensively explores the state-of-the-art in precision gap measurement technologies used in the aerospace sector. It categorizes and analyzes various sensors based on their operating principles, including optical, electrical, and other emerging technologies. Each sensor’s principle of operation, key advantages, and limitations are detailed. Furthermore, the paper identifies the significant challenges faced in aerospace gap measurement and discusses future development directions, emphasizing the need for enhanced accuracy, adaptability, and resilience to environmental factors. This study provides valuable insights for researchers and engineers in the field, guiding future innovations in precision gap measurement technologies to meet the evolving demands of aerospace manufacturing and maintenance. Full article

Predictive maintenance is a cornerstone of modern aerospace engineering, critical for maintaining the reliability and operational performance of aircraft engines. As a major component of prognostics and health management (PHM) technology, the accurate prediction of remaining useful life (RUL) enables proactive maintenance strategies, minimizes downtime, reduces costs, and enhances safety. This paper presents an innovative RUL prediction model designed specifically for aircraft engine applications. The model combines a temporal convolutional network (TCN) with multichannel attention and a gated recurrent unit (GRU) network. The framework begins with data pre-processing, followed by temporal feature extraction through an overlaying TCN network. Then, a multichannel attention mechanism fuses information from multiple TCN blocks, capturing rich feature representations. Finally, the fused data are processed by the GRU network to deliver precise RUL predictions. An improvement of at least 8.1% and 12.6% has been observed in two prediction metrics for the CMAPSS dataset when compared to other models. Full article

This paper presents a novel framework integrating evolutionary computation and artificial intelligence for aircraft health monitoring and management systems. The research addresses critical challenges in modern aircraft maintenance through a comprehensive approach combining real-time fault detection, predictive maintenance, and multi-objective optimization. The framework employs deep learning models for fault detection, achieving about 97% classification accuracy with an F1-score of 0.97, while remaining useful life prediction yields an R² score of 0.89 with a mean absolute error of 9.8 h. Evolutionary algorithms optimize maintenance strategies, reducing downtime and costs by up to 22% compared to traditional methods. The methodology includes robust data processing protocols, feature engineering techniques, and a modular system architecture supporting real-time monitoring and decision-making. Simulation experiments demonstrate the framework’s effectiveness in balancing maintenance objectives while maintaining high reliability. The research provides practical implementation guidelines and addresses key challenges in computational efficiency, data quality, and system integration. The results show significant improvements in maintenance planning efficiency and system reliability compared to traditional approaches. The framework’s modular design enables scalability and adaptation to various aircraft systems, offering broader applications in complex technical system maintenance. Full article

The integration of distributed electric propulsion into a wing significantly alters the dynamic behavior of the wing. Consequently, the loads on the wing structure in service, in particular upon transient gust and landing impact loads, change substantially compared with conventional aircrafts with main engines mounted on the inner wing. As this might significantly increase the stress levels and number of load cycles, this paper assesses the impact of wing-integrated distributed propulsion on the fatigue behavior of the wing structure. This assessment is conducted based on a retrofit scenario of a conventional 19-seater commuter aircraft of the CS-23 category retrofitted with distributed electric propulsion. The wing structure is idealized with beam elements. Static and dynamic response analyses followed by stress analyses are conducted for typical load cases occurring during operation of the aircraft. The fatigue analysis is carried out based on the safe life approach. This study concludes that the integration of distributed electric propulsion has a substantial impact on the fatigue behavior of the wing. A significant increase in fatigue damage for the electric configurations compared with the conventional configuration is observed, in particular in the outer wing area. The increased damage accumulation is a result of the higher stress amplitudes and the longer decay duration of the structural vibrations due to gusts. The results suggest that adjustments to the structural design and maintenance procedures of future electric aircrafts may be necessary. Full article