Methodological Research in the Field of Civil Aviation Safety

In order to promote the application and development of civil aviation safety(CAS)in the field of safety and enrich the theoretical system of CAS research,the methodology of CAS was studied.The definition of CAS was reconstructed,its connotation was explained from four aspects,and its research object was clarified.This paper discussed the mechanism of CAS,that is,to maintain system security through the establishment of safety prevention and control system(SPCS),and explained the research content of CAS from theory and technology application.Five principles and applicable methods for CAS research were summarized.In addition,it put forward the general procedure of CAS research,explained the specific content and implementation method or basis of each step.The research results show that the methodology of CAS is a systematic theoretical system with certain guiding significance,combined with the mechanism,research content,research principles and methods,and research procedures of CAS.

Classification of aviation incident causes using LGBM with improved cross-validation

Aviation accidents are currently one of the leading causes of significant injuries and deaths worldwide. This entices researchers to investigate aircraft safety using data analysis approaches based on an advanced machine learning algorithm.To assess aviation safety and identify the causes of incidents, a classification model with light gradient boosting machine (LGBM)based on the aviation safety reporting system (ASRS) has been developed. It is improved by k-fold cross-validation with hybrid sampling model (HSCV), which may boost classification performance and maintain data balance. The results show that employing the LGBM-HSCV model can significantly improve accuracy while alleviating data imbalance. Vertical comparison with other cross-validation (CV) methods and lateral comparison with different fold times comprise the comparative approach. Aside from the comparison, two further CV approaches based on the improved method in this study are discussed:one with a different sampling and folding order, and the other with more CV. According to the assessment indices with different methods, the LGBMHSCV model proposed here is effective at detecting incident causes. The improved model for imbalanced data categorization proposed may serve as a point of reference for similar data processing, and the model’s accurate identification of civil aviation incident causes can assist to improve civil aviation safety.

Hazardous Attitude Factors Influence Risk Perception in General Aviation Pilots

This manuscript presents a research proposal to investigate how hazardous attitudes among general aviation pilots influence pilot performance in aviation accidents. General aviation pilots train to maintain safe flying conditions, but accidents still occur, and human factors figure prominently among the causes of aviation accidents. The levels of hazardous attitudes among pilots may influence the likelihood of engaging in risky flight behaviors that can lead to accidents. This quantitative study aims to determine whether dangerous attitudes impact risk perception in general aviation pilots. The study will focus on two specific hazardous attitudes, “Anti-Authority” and Macho” behaviors. Among the hazardous attitudes identified by the Federal Aviation Administration (FAA), the two attitudes often stand out in accident investigations and pilot narratives. While all hazardous attitudes have inherent dangers, these two attitudes tend to be more frequently cited in accident reports and investigations. Despite rigorous training in safe flying conditions, general aviation accidents still transpire due to human factors. This research hypothesizes that the five attitudes from the hazardous attitude model, particularly Anti-Authority and Macho, significantly shape pilots’ risk perception. The insights from this study would benefit stakeholders, like the Aircraft Owners and Pilots Association (AOPA), Air Safety Institute, and aviation training programs, in creating training modules tailored to reduce such attitudes.

An Ensemble Classification Model Based on Imbalanced Data for Aviation Safety

Nowadays aviation accidents have become one of the major causes of severe injuries and fatalities around the world. This attracts the research community to look into aviation safety by applying data analysis techniques based on an advanced machine learning algorithm. An ensemble classification model based on Aviation Safety Reporting System(ASRS) has been proposed to analyze aviation safety targeting the people injured in the system.The ensemble classification model shall contain two modules: the data-driven module consisting of data cleaning, feature selection,and imbalanced data division and reorganization, and the modeldriven module stacked by Random Forest(RF), XGBoost(XGB),and Light Gradient Boosting Machine(LGBM) separately. The results indicate that the ensemble model could solve the data imbalance while vastly improving accuracy. LGBM illustrates higher accuracy and faster run in the analysis of a single model of the ASRS-based imbalanced data, while the ensemble model has the best performance in classification at the same time. The ensemble model proposed for imbalanced data classification can provide a certain reference for similar data processing while improving the safety of civil aviation.

Aviation Accident Causation Analysis Based on Complex Network Theory

Accident causation analysis is of great importance for accident prevention.In order to improve the aviation safety,a new analysis method of aviation accident causation based on complex network theory is proposed in this paper.Through selecting 257 accident investigation reports,45 causative factors and nine accident types are obtained by the three-level coding process of the grounded theory,and the interaction of these factors is analyzed based on the“2-4”model.Accordingly,the aviation accident causation network is constructed based on complex network theory which has scale-free characteristics and small-world properties,the characteristics of causative factors are analyzed by the topology of the network,and the key causative factors of the accidents are identified by the technique for order of preference by similarity to ideal solution(TOPSIS)method.The comparison results show that the method proposed in this paper has the advantages of independent of expert experience,quantitative analysis of accident causative factors and statistical analysis of a lot of accident data,and it has better applicability and advancement.

A Meta-Learning Approach for Aircraft Trajectory Prediction

The aviation industry has seen significant advancements in safety procedures over the past few decades, resulting in a steady decline in aviation deaths worldwide. However, the safety standards in General Aviation (GA) are still lower compared to those in commercial aviation. With the anticipated growth in air travel, there is an imminent need to improve operational safety in GA. One way to improve aircraft and operational safety is through trajectory prediction. Trajectory prediction plays a key role in optimizing air traffic control and improving overall flight safety. This paper proposes a meta-learning approach to predict short- to mid-term trajectories of aircraft using historical real flight data collected from multiple GA aircraft. The proposed solution brings together multiple models to improve prediction accuracy. In this paper, we are combining two models, Random Forest Regression (RFR) and Long Short-term Memory (LSTM), using k-Nearest Neighbors (k-NN), to output the final prediction based on the combined output of the individual models. This approach gives our model an edge over single-model predictions. We present the results of our meta-learner and evaluate its performance against individual models using the Mean Absolute Error (MAE), Absolute Altitude Error (AAE), and Root Mean Squared Error (RMSE) evaluation metrics. The proposed methodology for aircraft trajectory forecasting is discussed in detail, accompanied by a literature review and an overview of the data preprocessing techniques used. The results demonstrate that the proposed meta-learner outperforms individual models in terms of accuracy, providing a more robust and proactive approach to improve operational safety in GA.

Optimization of Continuous Airworthiness Problems

Recently aviation accident data shows that many fatal accidents in aviation are due to airworthiness issues despite the fact that all civil and private aircraft are required to comply with the airworthiness standards set by their national airworthiness authority.This paper presents a unique approach to continuous airworthiness problems optimization needed to reduce the risk associated by the gap between aircraft designers&manufacturing organization and continuing airworthiness(state of civil aviation authority and air operators).As a result of the paper summaries these problems and searching of the possible solutions to optimized,these problems are achieved to get more integration between(designers&manufacturing and air operators),finally there is recommendations are drawn to address the safe operation of the aircraft and can be given to the International Civil Aviation Organization(ICAO),Federal Aviation Administration(FAA)and European Aviation Safety Agency(EASA)and Civil Aviation Authorities(CAAs)for more integrate between all of them structure.

Computational fluid dynamics simulation of the wind flow over an airport terminal building

Turbulence in the wake generated by wind flow over buildings or obstacles may produce complex flow patterns in downstream areas.Examples include the recirculating flow and wind deficit areas behind an airport terminal building and their potential impacts on the aircraft landing on nearby runways.A computational fluid dynamics(CFD) simulation of the wind flow over an airport terminal building was performed in this study of the effect of the building wake on landing aircraft.Under normal meteorological conditions,the studied airport terminal building causes limited effects on landing aircraft because most of the aircraft have already landed before entering the turbulent wake region.By simulating the approach of a tropical cyclone,additional CFD sensitivity tests were performed to study the impacts of building wake under extreme meteorological conditions.It was found that,in a narrow range of prevalent wind directions with wind speeds larger than a certain threshold value,a substantial drop in wind speed(>3.6 m/s) along the glide path of aircraft was observed in the building wake.Our CFD results also showed that under the most critical situation,a drop in wind speed as large as 6.4 m/s occurred right at the touchdown point of landing aircraft on the runway,an effect which may have a significant impact on aircraft operations.This study indicated that a comprehensive analysis of the potential impacts of building wake on aircraft operations should be carried out for airport terminals and associated buildings in airfields to ensure safe aviation operation under all meteorological conditions and to facilitate implementation of precautionary measures.