Search

(1) Background: As the principal structural element of an unmanned aerial vehicle (UAV), a lightweight, high-performance wing-fuselage connection structure plays an important role in UAV structural integrity. Previous studies focused on the static strength characteristics of aluminum and high-strength steel wing-fuselage connection structures. With the widespread usage of titanium material in aviation, research should address the fatigue performance of titanium wing-fuselage connection structures. This paper aims to investigate the fatigue performance of the titanium wing-fuselage connection structure by using analysis and experimental methods, and to develop a reliable fatigue assessment method based on the experimental results. (2) Methods: General and detail finite element models were established, and the stress distribution at the detail fatigue design point was obtained via finite element analysis. Based on the equivalent life curve theory, a dimensionless value, detail fatigue value (DF), was proposed to characterize the fatigue performance for structure. By using detail fatigue value (DF) method, the theoretical fatigue performance value was calculated. The fatigue test of the wing-fuselage connection structure was conducted, and the fatigue life was determined according to the test results. (3) Results: For the wing joint with fatigue failure in the test, the test fatigue performance was close to the theoretical value. It can be concluded from the DF value that the analysis and test results are consistent, and the values obtained from the analysis are more conservative than test results. (4) Conclusions: The error between the test DF value and the theoretical analysis DF value is small, and the theoretical analysis of fatigue performance can represent the fatigue characteristics of the wing-fuselage connection structure. The detail fatigue value (DF) method can predict the fatigue performance of the structure quite well.

This paper presents an innovative approach for the Feature Extraction method using Self-Attention, incorporating various Feature Selection techniques known as the AtSiftNet method to enhance the classification performance of motor imaginary activities using electrophotography (EEG) signals. Initially, the EEG signals were sorted and then denoised using multiscale principal component analysis to obtain clean EEG signals. However, we also conducted a non-denoised experiment. Subsequently, the clean EEG signals underwent the Self-Attention feature extraction method to compute the features of each trial (i.e., 350×18). The best 1 or 15 features were then extracted through eight different feature selection techniques. Finally, five different machine learning and neural network classification models were employed to calculate the accuracy, sensitivity, and specificity of this approach. The BCI competition III dataset IV-a was utilized for all experiments, encompassing the datasets of five volunteers who participated in the competition. The experiment findings reveal that the average accuracy of classification is highest among ReliefF (i.e., 99.946%), Mutual Information (i.e., 98.902%), Independent Component Analysis (i.e., 99.62%), and Principal Component Analysis (i.e., 98.884%) for both 1 and 15 best-selected features from each trial. These accuracies were obtained for motor imagery using a Support Vector Machine (SVM) as a classifier. In addition, five-fold validation was performed in this paper to assess the fair performance estimation and robustness of the model. The average accuracy obtained through five-fold validation is 99.89%. The experiments’ findings indicate that the suggested framework provides a resilient biomarker with minimal computational complexity, making it a suitable choice for advancing Motor Imagery Brain–Computer Interfaces (BCI).

Mechanical and electrical equipment is an important component of urban rail transit stations, and the service capacity of stations is affected by its reliability. To solve the problem of predicting faults in station mechanical and electrical equipment with sparse data, this study proposes a fault prediction framework based on SSA-CNN-LSTM. Firstly, this article proposes a fault enhancement method for station electromechanical equipment based on TimeGAN, which expands and generates data that conform to the temporal characteristics of the original dataset, to solve the problem of sparse data in the original fault dataset. An SSA-CNN-LSTM model is then established to extract effective data features from low-dimensional data with insufficient feature depth through structures such as convolutional layers and pooling layers in a CNN, determine the optimal hyperparameters, automatically optimize the model network size, solve the problem of the difficult determination of the neural network model size, and achieve accurate prediction of the fault rate of station electromechanical equipment. Finally, an engineering verification was conducted on the platform screen door (PSD) systems in stations on Shanghai Metro Lines 1, 5, 9, and 10. The experiments showed that the proposed prediction method improved the RMSE by 0.000699, the MAE by 0.00042, and the R2 index by 0.109779 when predicting the fault rate data of platform screen doors on all of the lines. When predicting the fault rate data of the screen doors on a single line, the performance of the model was better than that of the CNN-LSTM model optimized with the PSO algorithm.

In order to effectively identify the key causative factors of civil aviation flight accidents, and establish a forward-looking effective prevention mechanism for flight accidents. Firstly, Corrected SHELLO model is established to classify the causes of civil aviation accidents in China (2015–2019) based on the integration of SHELL analysis model and Reason organization system concept. Secondly, in view of the randomness and uncertainty gray characteristics of the flight accidents inducing factors, the improved entropy gray correlation algorithm is established for the purpose of importance recognition, which combined with the characteristics of the data sample of inducement classification. Finally, the improved entropy gray correlation algorithm is used to identify and rank the key causative factors of flight accidents. The results showed that the flight accidents crucial causative factor is the human factors which we should pay more attention including the pilot perceptual errors, skill-based errors, decision errors and violation main factors, environmental and organizational factors also play an important role in inducing flight accidents, including complex terrain for approach landing and poor safety management mechanism factors. The method has great practical significance for identifying critical causative factors of flight accidents and improving flight safety.

Human error is an important risk factor for flight safety. Although the human error assessment and reduction technique (HEART) is an available tool for human reliability derivation, it has not been applied in flight safety assessment. The traditional HEART suffers from imprecise calculation of the assessed proportion of affect (APOA) because it heavily depends on a single expert's judgment. It also fails to provide remedial measures for flight safety problems. To overcome these defects of the HEART, this study proposes an integrated human error quantification approach that uses the improved analytic hierarchy process method to determine the APOA values. Then, these values are fused to the HEART method to derive the human error probability. A certain flight task is completed to assess human reliability. The results demonstrate that the proposed method is a reasonable and feasible tool for quantifying human error probability and assessing flight safety in the aircraft manipulation process. In addition, the critical error-producing conditions influencing flight safety are identified, and improvement measures for high-error-rate operations are provided. The proposed method is useful for reducing the possibility of human error and enhancing flight safety levels in aircraft operation processes.

Compared with federated avionic architecture, the integrated modular avionic (IMA) system architecture in the aircraft can provide more sophisticated and powerful avionic functionality, and meanwhile, it becomes structurally dynamic, variably interconnected, and highly complex. The traditional approach such as fault tree analysis (FTA) becomes neither convenient nor sufficient in making safety analysis of the IMA system. In order to overcome the limitations, the approach that FTA combines with generalized stochastic petri net (GSPN) is proposed. First, FTA is used to establish the static model for the top level of the IMA system, while GSPN is used to build a dynamic model for each cell system. Finally, the combination model is generated, which is called the FTGPN model. Moreover, the FTGPN model is made safety analysis with the PIPE2 tool. According to the simulation result, corresponding measures are taken to meet the safety requirements of the IMA system.

There are different missing flight data due to various reasons in the process of acquisition and storage, especially in general aviation, which cause inconvenience for flight data analysis. Effectively explaining the relationship between flight data parameters and selecting a simple and effective method for fitting and correcting flight data suitable for engineering applications are the main points of the paper. Herein, a convenient and applicable approach of missing data correction and fitting based on the least squares polynomial method is introduced in this work. Firstly, the polynomial fitting model based on the least squares method is used to establish multi-order polynomial by existing flight data since the order of the least squares polynomial has a direct impact on the fitting effect. The order is too high or too small, over-fitting or deviation will occur, resulting in improper data. Therefore, the optimization and selection of the model order are significant for flight data correction and fitting. Because the flight data of the aircraft engine exhaust gas temperature (EGT) are often lost because of the immature detection technology, a series of the multi-order polynomial are established by the relationship of aircraft engine exhaust gas temperature and Revolutions Per Minute (RPM). Case study results confirm the optimal model order is four for the fitting and correction of aircraft engine exhaust temperature, and the least squares polynomial method is applicable and effective for EGT flight data correction and fitting based on RPM data.

This study aimed to evaluate pilot manipulation comfort in different flight scenarios under different ambient temperatures. To achieve this goal, we designed a test plan to devise the physiological indexes used in the evaluation of pilot manipulation comfort, collected and analyzed pilot EMG signals and mean skin temperature, and on this basis revealed the impacts of different ambient temperatures and different flight missions on muscle activation and mean skin temperature. Based on this, we extracted muscle activation and mean skin temperature as the evaluation indices for the evaluation of manipulation comfort and established a model for this evaluation based on an improved particle swarm optimization and least-squares support vector machine (IPSO-LSSVM). Based on this model, the predicted manipulation comfort values of the participants were 4, 5 and 6, which belong to the categories of fair, somewhat comfortable and rather comfortable, respectively. By comparing the prediction results of different evaluation methods, the method proposed in this study was verified to be effective.

Bird-strike failure of fan blades is one of the basic challenges for the safety of aircraft engines. Simplified flat blade-like plates are always used for damage mechanism study of composite laminates. One undesirable issue is the failure at the root of clamped flat plates under high-velocity impact. For this purpose, two different strategies were exploited to obtain desirable impact damage distributions, namely the impact location and the boundary condition. Numerical models of the simplified flat blade-like plate and the bird projectile were constructed by using finite element method (FEM) and smoothed particle hydrodynamics (SPH) approaches. The impact damage distributions were comparatively investigated in detail. The numerical results show that changing the boundary condition is the most effective way to obtain preferable impact damages for further failure analysis of real fan blades. Present results will be useful to the future surrogate experimental design of simplified bird-strike testing.

Human error is one of the most important risk factors affecting aviation safety. The original Cognitive Reliability and Error Analysis Method (CREAM) developed for the nuclear industry is reliable for human reliability quantification, but it is not fully applicable to human reliability analysis in aviation because it neglects the characteristics of long-duration flights. Here, we propose a modified CREAM method to predict human error probability in flight and provide some improvement measures for critical operations. A set of performance influencing factors (PIFs), such as flight procedures and ground support, is established to reflect operational scenarios in flight. Then, we develop the expected affect index of PIFs and the Scenario Influence Index to construct a quantitative model of human reliability. The probability of human error for each operation in the approach and landing phases is obtained with the modified CREAM method, and the results indicate that the most important cognitive function that influences human reliability is missed action. The proposed method may be a suitable tool for human reliability quantification in aviation considering long-duration flights. The method also has great practical significance for improving flight safety.

Because of the light weight and high strength, composite laminates have many advantages in aircraft structures; however, they are frequently subjected to severe dynamic loadings during flight. To understand the dynamic properties of composite laminated thin plates at the stress scale, this paper studies the stress modal analysis (SMA) of composite laminated thin plates by finite element method (FEM). Firstly, the basic theory on SMA of composite laminates was given from the classical displacement modal analysis. Secondly, a square laminated thin plate was numerically studied to obtain some distribution laws of the stress mode shapes (SMSs) from the layup and stress component perspectives. Then, based on the characteristics of SMSs in different plies, a modified layup configuration was conducted for possible lower magnitude and more uniform distributions of SMSs. Results indicate that ±45° layups can improve the performance of SMSs of the square plate, without excessively decreasing the modal frequencies. Such fact manifests that ±45° layups are critically vital for the dynamic stress reduction of the square composite laminated plates. Modal participation factor and strain energy were evaluated to assist the determination of critical modes. Lastly, the aspect ratio of the composite plate on layup design was considered. Numerical investigation in this study can serve as a preliminary step of SMSs perspective for the analysis and optimization of dynamic composite laminates.

A 6-degree-of-freedom (DOF) model of a pilot's upper limb is established in this study. A kinematics analysis is performed by using the screw theory and the product of exponential formula. Kane's equation in screw form, which is a concise form with a definite physical meaning, is used to analyze the dynamic characteristics of a pilot's upper limb. In the Mathematica environment, the man-machine system consisting of a pilot and a joystick is taken as the analysis object to simulate the joystick pushing and pulling processes of a pilot at the 50th percentile of Chinese body dimensions. The analysis yields the angular velocity and angular acceleration curves of the joint, which indicate that the manipulation comfort is rather good. The actual posture data during the pilot manipulation process are measured. Through a comparison with the output data, the correctness of the simulation analysis is verified. The torque curve reflects that the torque of the shoulder joint is greater than that of the elbow joint, and the changing tendency conforms to the actual motion law. Therefore, the correctness of Kane's equation in screw form is verified. At the same time, the results can serve as a theoretical basis for evaluating a pilot's manipulation comfort and as an important reference for cockpit layout design.

To obtain some basic laws for bird-strike resistance of composite materials in aeronautical application, the high-velocity impact behaviors of composite laminates with different materials were studied by numerical methods. The smoothed particle hydrodynamics (SPH) and finite element method (FEM) coupling models were validated from various perspectives, and the numerical results were comparatively investigated. Results show that the different composite materials have relatively little effect on projectile deformations during the bird impact. However, the impact-damage distributions can be significantly different for different composite materials. The strength parameters and fracture energy parameters play different roles in different damage modes. Lastly, modal frequency was tentatively used to explain the damage behavior of the composite laminates, for it can manifest the mass and stiffness characteristics of a dynamic structure. The dynamic properties and strength properties jointly determine the impact-damage resistance of composite laminates under bird strike. Future optimization study can be considered from these two aspects.

The engine bleed air system (BAS) is one of the important systems for civil aircraft, and fault prediction of BAS is necessary to improve aircraft safety and the operator's profit. A dual-stage two-phase attention-based encoder-decoder (DSTP-ED) prediction model is proposed for BAS normal state estimation. Unlike traditional ED networks, the DSTP-ED combines spatial and temporal attention to better capture the spatiotemporal relationships to achieve higher prediction accuracy. Five data-driven algorithms, autoregressive integrated moving average (ARIMA), support vector regression (SVR), long short-term memory (LSTM), ED, and DSTP-ED, are applied to build prediction models for BAS. The comparison experiments show that the DSTP-ED model outperforms the other four data-driven models. An exponentially weighted moving average (EWMA) control chart is used as the evaluation criterion for the BAS failure warning. An empirical study based on Quick Access Recorder (QAR) data from Airbus A320 series aircraft demonstrates that the proposed method can effectively predict failures. [ABSTRACT FROM AUTHOR]

Composite laminates can potentially reduce the weight of aircrafts; however, they are subjected to bird strike hazards in civil aviation. To handle their nonlinear dynamic behaviour, in this study, the impact damage of composite laminates were numerically evaluated and designed by means of smoothed particle hydrodynamics (SPH) and the finite element method (FEM) to simulate the interaction between bird projectiles and the laminates. Attention was mainly focused on the different damage modes in various laminates’ plies induced by bird impact on a square laminated plate. A continuum damage mechanics approach was exploited to simulate damage initiation and evolution in composite laminates. Damage maps were computed with respect to different ply angles, i.e., 0°, 45° and −45°. The damage distributions were comparatively investigated, and then the ply design was considered for crashworthiness improvement. The results aim to serve as a design guideline for future prototype-scale bird strike studies of complex laminated structures.

In spite of potential advantages for aircraft structures, composite laminates can be subjected to bird-strike hazard in civil aviation. For purpose of future surrogate experiments, in this study, impact-damage equivalency for twisted composite blades is numerically investigated by Smoothed Particle Hydrodynamics (SPH) and finite element method (FEM). Cantilever slender flat plates are usually used for basic impact tests, the impact-damage equivalency is being considered by comparing damage modes and energies of three impact configurations: (1) twisted blade; (2) flat blade (axisymmetric); and (3) inclined flat blade (centrosymmetric). The damage maps and energy variations were comparatively investigated. Results indicate that both symmetrical flat and inclined flat blades can be, to a certain extent, regarded as alternatives for real twisted blades under bird impact; however, both types of blade have their own merits and drawbacks, and hence should be used carefully. These results aim to serve as tentative design guideline for future prototype or model experimental study of laminated blades in real aeronautical structures.

To realize an early warning of unbalanced workload in the aircraft cockpit, it is required to monitor the pilot’s real-time workload condition. For the purpose of building the mapping relationship from physiological and flight data to workload, a multi-source data fusion model is proposed based on a fuzzy neural network, mainly structured using a principal components extraction layer, fuzzification layer, fuzzy rules matching layer, and normalization layer. Aiming at the high coupling characteristic variables contributing to workload, principal component analysis reconstructs the feature data by reducing its dimension. Considering the uncertainty for a single variable to reflect overall workload, a fuzzy membership function and fuzzy control rules are defined to abstract the inference process. An error feedforward algorithm based on gradient descent is utilized for parameter learning. Convergence speed and accuracy can be adjusted by controlling the gradient descent rate and error tolerance threshold. Combined with takeoff and initial climbing tasks of a Boeing 737−800 aircraft, crucial performance indicators—including pitch angle, heading, and airspeed—as well as physiological indicators—including electrocardiogram (ECG), respiration, and eye movements—were featured. The mapping relationship between multi-source data and the comprehensive workload level synthesized using the NASA task load index was established. Experimental results revealed that the predicted workload corresponding to different flight phases and difficulty levels showed clear distinctions, thereby proving the validity of data fusion.