Search Results (1,168)

General aviation (GA), comprised mainly of piston engine airplanes, has an inferior safety history compared with air carriers in the United States. Most studies addressing this safety disparity has focused on pilot deficiencies. Herein, we determined the rates/causes of equipment failure-related GA fatal accidents for type-certificated and experimental-amateur-built airplanes. Aviation accidents/injury severity were per the NTSB Access^R database. Statistical tests employed proportion/binomial tests/a Poisson distribution. The rate of fatal accidents (1990–2019) due to equipment failure was unchanged (p > 0.026), whereas the fatal mishap rate related to other causes declined (p < 0.001). A disproportionate (2× higher) count (p < 0.001) of equipment-related fatal accidents was evident for experimental-amateur-built aircraft with type-certificated references. Propulsion system (67%) and airframe (36%) failures were the most frequent causes of fatal accidents for type-certificated and experimental-amateur-built aircraft, respectively. The components “fatigue/corrosion” and “manufacturer–builder error” resulted in 60% and 55% of powerplant and airframe failures, respectively. Most (>90%) type-certificated aircraft propulsion system failures were within the manufacturer-prescribed engine time-between-overhaul (TBO) and involved components inaccessible for examination during an annual inspection. There is little evidence for a decline in equipment failure-related fatal accident rate over three decades. Considering the fact that powerplant failures mostly occur within the TBO and involve fatigue/corrosion of one or more components inaccessible for examination, GA pilots should avoid operations where a safe off-field landing within glide-range is not assured. Full article

In geophysics, aeromagnetic surveying based on unmanned aerial vehicles (UAV) is a widely employed exploration technique, that can analyze underground structures by conducting data acquisition, processing, and inversion. This method is highly efficient and covers large areas, making it widely applicable in mineral exploration, oil and gas surveys, geological mapping, and engineering and environmental studies. However, during flight, interference from the aircraft’s engine, electronic systems, and metal structures introduces noise into the magnetic data. To ensure accuracy, mathematical models and calibration techniques are employed to eliminate these aircraft-induced magnetic interferences. This enhances measurement precision, ensuring the data faithfully reflect the magnetic characteristics of subsurface geological features. This study focuses on aeromagnetic data processing methods, conducting numerical simulations of magnetic interference for aeromagnetic surveys of UAVs with the Tolles–Lawson (T-L) model. Recognizing the temporal dependencies in aeromagnetic data, we propose a Transformer neural network algorithm for aeromagnetic compensation. The method is applied to both simulated and measured flight data, and its performance is compared with the classical Multilayer Perceptron neural networks (MLP). The results demonstrate that the Transformer neural networks achieve better fitting capability and higher compensation accuracy. Full article

Measurement systems such as laser trackers and 3D imaging systems are being increasingly adopted across the manufacturing industry. These metrology technologies can allow for live, high-precision measurement in a digital system, enabling the spatial component of the digital manufacturing twin. In aircraft wing manufacturing, drilling and fastening operations must be guided by precise measurements from a digital design model. With thousands of fasteners on each aircraft wing, even small errors in alignment of surface covers to wing ribs and spars can impact component longevity due to aerodynamic drag. Determining surface conformance of airstream-facing surfaces is currently largely performed though manual gauge checking by human operators. In order to capture the surface details and reverse engineer components to assure tolerance has been achieved, laser scanners could be utilised alongside a precise registration strategy. This work explores the quality of the aerostructure surface in a captured point cloud and the subsequent accuracy of surface normal determination from planar fastener heads. These point clouds were captured with a reference hand-held laser scanner and two terrestrial laser scanners. This study assesses whether terrestrial laser scanners can achieve <0.5° surface normal accuracy for aerospace fastener alignment. Accuracy of the surface normals was achieved with a nominal mean discrepancy of 0.42 degrees with the Leica RTC360 3D Laser Scanner (Leica Geosystems AG, Heerbrugg, Switzerland) and 0.27 degrees with the Surphaser 80HSX Ultra Short Range (Basis Software Inc., Redmond, WA, USA). Full article

Predicting the remaining useful life (RUL) of aircraft engines is crucial for ensuring flight safety, optimizing maintenance, and reducing operational costs. This paper introduces a novel hybrid deep learning model, Transformer–KAN–BiLSTM, for aero-engine RUL prediction. The model is designed to leverage the complementary strengths of its components: the Transformer architecture effectively captures long-range temporal dependencies in sensor data, the emerging Kolmogorov–Arnold Network (KAN) provides superior approximation flexibility and a unique degree of interpretability through its spline-based activation functions, and the Bidirectional LSTM (BiLSTM) extracts nuanced local temporal patterns. Evaluated on the benchmark NASA C-MAPSS dataset, the proposed fusion framework demonstrates exceptional performance, achieving remarkably low Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) values that significantly surpass existing benchmarks. These results validate the model’s robustness and its high potential for practical deployment in prognostics and health management systems. Full article

The research aims to propose a basic parameter estimation method for high-speed vertical take-off and landing (HSVTOL) aircraft, balancing rotor and fixed-wing mode requirements. Flight profiles and performance indicators are defined based on mission phases, and maximum take-off weight is estimated using the fuel fraction method. A pre-estimation model for a turboshaft–turbofan variable cycle engine (TSFVCE) was established, and the conversion between thrust and power was conducted. Constraints related to different performance requirements were analyzed, and the relationship between the rotor and the wing was established, resulting in the generation of constraint diagrams for the selection of basic parameters. This method allows for the rapid and effective estimation of basic parameters, including maximum take-off weight, rotor disk loading, and wing loading. Two tiltrotor aircraft were analyzed using this method. The estimated results closely matched actual values, with errors within a reasonable range. These findings demonstrate the method’s reliability and provide a reference for HSVTOL conceptual design and engine power matching. Full article

Accurate estimation of engine thrust and overload is crucial for ensuring structural integrity and optimizing the aircraft’s life-cycle design. To address this issue, this study develops an integrated thrust and load prediction framework that combines vision-based flight maneuver recognition with an improved transformer-based deep learning model (YOLO), leveraging measured flight parameters. After maneuver recognition, the model achieves a mean absolute error of 1.86 and R² of 0.97 in prediction. The framework is implemented via a Python-based software system with a MySQL database, supporting functionalities including thrust/load prediction, trajectory visualization, and performance evaluation. Comparative experiments demonstrate that the framework achieves an average maneuver recognition accuracy of 81.06%, outperforming the existing PLR-PIP and DTW methods. This approach provides high-precision and reliable thrust data as well as tool support for real-time thrust estimation, fatigue life assessment, and flight safety risk prevention. Full article

The aviation industry operates as a complex, dynamic system generating vast volumes of data from aircraft sensors, flight schedules, and external sources. Managing this data is critical for mitigating disruptive and costly events such as mechanical failures and flight delays. This paper presents a comprehensive application of predictive analytics and machine learning to enhance aviation safety and operational efficiency. We address two core challenges: predictive maintenance of aircraft engines and forecasting flight delays. For maintenance, we utilise NASA’s C-MAPSS simulation dataset to develop and compare models, including one-dimensional convolutional neural networks (1D CNNs) and long short-term memory networks (LSTMs), for classifying engine health status and predicting the Remaining Useful Life (RUL), achieving classification accuracy up to 97%. For operational efficiency, we analyse historical flight data to build regression models for predicting departure delays, identifying key contributing factors such as airline, origin airport, and scheduled time. Our methodology highlights the critical role of Exploratory Data Analysis (EDA), feature selection, and data preprocessing in managing high-volume, heterogeneous data sources. The results demonstrate the significant potential of integrating these predictive models into aviation Business Intelligence (BI) systems to transition from reactive to proactive decision-making. The study concludes by discussing the integration challenges within existing data architectures and the future potential of these approaches for optimising complex, networked transportation systems. Full article

Safety is a central concern in aviation, where aircraft operations involve complex processes and interactions exposed to multiple hazards. Addressing these hazards requires systematic risk management and the selection of effective safety measures. This study introduces a novel hybrid multi-criteria decision-making (MCDM) framework that integrates the grey Delphi method, the grey Analytic Hierarchy Process (AHP), and the grey Axial-Distance-Based Aggregated Measurement (ADAM) method. The framework provides a rigorous engineering-based approach for evaluating and ranking safety measures under uncertainty and diverse stakeholder perspectives. Application of the model to aircraft operations demonstrates its ability to identify the most effective measures, including the development of critical infrastructure protection plans, rerouting of flight paths from high-risk areas, and strengthening of regulatory oversight. The proposed methodology advances decision-support tools in aviation safety engineering, offering structured guidance for optimizing resource allocation and improving system resilience. Full article

Humans are capable of identifying aircraft based on quantitative features such as aspect ratio, engine count, wingspan, and structural configuration. Inspired by this, a keypoint-based aircraft identification approach is proposed to address the challenge of fine-grained aircraft recognition in high-resolution remote sensing images. First, a dataset of aircraft labeled with keypoints is built, in which aircraft are reclassified into types according to the similarity of keypoint distributions to improve extraction stability and versatility. Then, a keypoint extraction method with topological constraints is proposed, leveraging the nadir imaging characteristics of remote sensing and accounting for the relationships among keypoints. Subsequently, distinctive quantitative features for identification are selected through representativeness and effectiveness analyses for the following matching algorithm. Finally, a comprehensive template matching-based identification strategy is proposed to recognize targets based on quantitative descriptions derived from keypoints. This novel solution achieves significantly more accurate identification than traditional regression–classification approaches, improving recognition accuracy by over $3\%$ on average. Moreover, the method extends aircraft identification capabilities from closed-set to open-set recognition, demonstrating substantial value for the precise interpretation of aircraft targets in high-resolution optical imagery. Full article

Composite engine fan blades are critical aircraft engine components, and their failure can compromise the safe and reliable operation of the entire aircraft. To enhance aircraft availability and safety within a condition-based maintenance framework, effective methods are needed to identify damage and monitor the blades’ condition throughout manufacturing and operation. This paper presents a unique experimental framework for real-time monitoring of composite engine blades utilizing state-of-the-art structural health monitoring (SHM) technologies, discussing the associated benefits and challenges. A case study is conducted on a representative Foreign Object Damage (FOD) panel, a substructure of a LEAP (Leading Edge Aviation Propulsion) engine fan blade, which is a curved, 3D-woven Carbon Fiber Reinforced Polymer (CFRP) panel with a secondary bonded steel leading edge. The loading scheme involves incrementally increasing, cyclic 4-point bending (loading–unloading) to induce controlled damage growth, simulating in-operation conditions and allowing evaluation of flexural properties before and after degradation. External damage, simulating foreign object impact common during flight, is introduced using a drop tower apparatus either before or during testing. The panel’s condition is monitored in-situ and in real time by two types of SHM sensors: screen-printed piezoelectric sensors for guided ultrasonic wave propagation studies and surface-bonded Fiber Bragg Grating (FBG) strain sensors. Experiments are conducted until panel collapse, and degradation is quantified by the reduction in initial stiffness, derived from the experimental load-displacement curves. This paper aims to demonstrate this unique experimental setup and the resulting SHM data, highlighting both the potential and challenges of this SHM framework for monitoring complex composite structures, while an attempt is made at correlating SHM data with structural degradation. Full article

Aircraft engine endoscope probes often face difficulties in effectively detecting internal structures in high-temperature environments. In order to improve the thermal protection characteristics of endoscope probes, this paper designs a probe-cooling structure with a circular ring pressure drop structure and calculates and analyzes the cooling effect of the probe under different gas cooling conditions. Study the influence of size parameters and cooling medium properties of the structure on the cooling characteristics of the probe, analyze the temperature distribution of the probe mirror, cooling efficiency distribution, and cold flow outlet flow distribution. The results show that the larger the outlet width of the annular cooling structure, the better the cooling effect, and the optimal cooling effect structure is 0.7 mm; the larger the opening angle, the lower and then the higher the temperature of the endoscope probe surface, and the best cooling effect occurs when the optimal angle is 40°; the larger the proportion of mixed liquid nitrogen, the lower the temperature of the probe mirror surface. A 5% proportion of mixed liquid nitrogen can reduce the temperature of the probe mirror surface by about 11 K. Full article

Artificial intelligence technology is profoundly reshaping the aviation field, driving the accelerated evolution of air confrontation patterns toward intelligence and autonomy. Given that experimental aircraft platforms are key means to verify intelligent air confrontation technologies, this paper—on the basis of systematically sorting out the progress of intelligent technologies in the air confrontation domain at home and abroad—first focuses on analyzing the connotation, technological evolution path, and application prospects of experimental aircraft platforms, and deeply interprets the technological breakthroughs and application practices of typical experimental platforms such as X-37B and X-62A in the field of artificial intelligence integration. Furthermore, through the analysis of three typical air confrontation projects, it reveals the four core advantages of experimental aircraft platforms in intelligent technology research: efficient iterative verification, risk reduction, promotion of capability emergence, and provision of flexible carriers. Finally, this paper focuses on constructing a technical implementation framework for the deep integration of intelligent technologies and flight tests, covering key links such as requirement analysis and environmental test design, construction of intelligent test aircraft platforms and capability generation, ground verification, and test evaluation, and summarizes various key technologies involved in the technical implementation framework. This study can provide theoretical support for the deep integration of artificial intelligence technology and the aviation field, including an engineering path from intelligent algorithm design, verification to iterative optimization, supporting the transformation of air confrontation patterns from “human-in-the-loop” to “autonomous gaming,” thereby enhancing the intelligence level and actual confrontation effectiveness in the aviation field. Full article

Aero-engine ablation detection is a critical task in aircraft health management, yet existing rotation-based object detection methods often face challenges of high computational complexity and insufficient local feature extraction. This paper proposes an improved YOLOv11 algorithm incorporating Context-guided Large-kernel attention and Rotated detection head, called CLR-YOLOv11. The model achieves synergistic improvement in both detection efficiency and accuracy through dual structural optimization, with its innovations primarily embodied in the following three tightly coupled strategies: (1) Targeted Data Preprocessing Pipeline Design: To address challenges such as limited sample size, low overall image brightness, and noise interference, we designed an ordered data augmentation and normalization pipeline. This pipeline is not a mere stacking of techniques but strategically enhances sample diversity through geometric transformations (random flipping, rotation), hybrid augmentations (Mixup, Mosaic), and pixel-value transformations (histogram equalization, Gaussian filtering). All processed images subsequently undergo Z-Score normalization. This order-aware pipeline design effectively improves the quality, diversity, and consistency of the input data. (2) Context-Guided Feature Fusion Mechanism: To overcome the limitations of traditional Convolutional Neural Networks in modeling long-range contextual dependencies between ablation areas and surrounding structures, we replaced the original C3k2 layer with the C3K2CG module. This module adaptively fuses local textural details with global semantic information through a context-guided mechanism, enabling the model to more accurately understand the gradual boundaries and spatial context of ablation regions. (3) Efficiency-Oriented Large-Kernel Attention Optimization: To expand the receptive field while strictly controlling the additional computational overhead introduced by rotated detection, we replaced the C2PSA module with the C2PSLA module. By employing large-kernel decomposition and a spatial selective focusing strategy, this module significantly reduces computational load while maintaining multi-scale feature perception capability, ensuring the model meets the demands of high real-time applications. Experiments on a self-built aero-engine ablation dataset demonstrate that the improved model achieves 78.5% mAP@0.5:0.95, representing a 4.2% improvement over the YOLOv11-obb which model without the specialized data augmentation. This study provides an effective solution for high-precision real-time aviation inspection tasks. Full article

The starting torque of Self-lubricating Spherical Plain Bearings (SSPBs) has a significant impact on the reliability and service life of aircraft. Due to the low accuracy of the dynamic detection of the starting torque of the bearing, the starting torque cannot be measured accurately under high-frequency swinging conditions. Therefore, the problem of the dynamic detection accuracy of the starting torque of the bearing on a high-frequency swinging friction and wear tester was proposed to be investigated in this paper, and a dynamic simulation model of the swinging system of the tester was constructed. With the combination of the inertia torque test and the least square method, a mathematical model of the inertia torque was developed and the influence of the inertia torque on the results of the dynamic detection of the starting torque was revealed. At the same time, an error compensation procedure for the on-line dynamic detection of the starting torque was written. This research shows that the inertia torque of the swing system of the tester has a great influence on the detection accuracy of the starting torque. As the swing frequency increases, the inertia torque increases, and the dynamic detection accuracy of the starting torque is reduced. The dynamic detection error of the starting torque of the bearing can be efficiently compensated by the error compensation procedure, and then the detection accuracy can be improved. This research provides a good theory for the design of SSPBs and the reasonable control of the starting torque during the use of the bearings, and it is valuable for engineering practice. Full article

Unexpected failures of avionics equipment critically affect flight safety, operational availability, and maintenance costs. To address these issues, Condition-Based Maintenance Plus (CBM+) has emerged as a strategy to optimize maintenance timing based on equipment condition rather than fixed schedules. However, while aviation research has largely focused on engines and structures, studies on avionics systems remain limited, often relying on simulations. This study proposes a novel data-driven approach to predict avionics equipment failures using actual aircraft operational data. Maneuver-related sequences were analyzed to investigate correlations between flight patterns and equipment faults, and a two-stage framework was developed. In the feature extraction stage, a CNN-LSTM encoder compresses 10 s maneuver sequences into compact yet informative representations. In the fault prediction stage, AI models classify failures of the Fire Control Radar based on these features. Experiments with real flight data validated the effectiveness of the method, showing that the CNN-LSTM encoder preserved essential maneuver information, while the combination of Standard Scaling and Multi-Layer Perceptron achieved the best performance, with a maximum Fault Recall of 98%. These findings demonstrate the feasibility of practical CBM+ implementation for avionics equipment using only flight data, providing a promising solution to improve maintenance efficiency and aviation safety. Full article