Aircraft Accidents Research Articles

How Pilot Fatigue Affects Operational Flight Risk in Scheduled Commercial Aviation

Abstract: Pilot fatigue remains a hazard in scheduled commercial aviation, contributing to 31 aircraft accidents between 2005 and 2022, according to International Air Transport Association (IATA) (2023) . Therefore, this study further investigated fatigue and its impact on operational flight risk in commercial aviation. More than 16,000 safety reports were analyzed using mixed methods. The results showed that fatigue-related occurrences were more likely to be reported confidentially. Factors contributing to an increased risk of fatigue included pilots’ personal factors, inadequate rest periods, or poor hotel environments. Fatigue was observed in 6% of all safety-related occurrences analyzed, but these events consumed 9% of the airlines’ risk budget. Further empirical research is required focusing on the influence of pilots’ personal factors on fatigue risk.

A Neural-Metaheuristic Kalman Filter for Moving Microburst Wind Shear Identification

Microburst wind shears (MB) are powerful localized three dimensional (3D) columns of wind that occur when cooled air drops from the base of thunderstorms at high speeds. They eventually hit the ground and spread out in all directions. As such MBs are considered a major atmospheric hazard for aircrafts (ACs), especially in terminal flight phases. Though pilots train regularly to survive it, yet the most recommended practice within the aviation community is to avoid its encounter upon detection. Some modern aircrafts have predictive wind shear warning systems, but they have limited short range capability and do not provide quantitative data for engineering application. In this sense, accurate onboard detection and identification of MBs and its characteristics is of importance for flight safety, whose success can lead to design of automatic flight control systems (FCSs) that reduce the risk of aircraft crash landings and accidents. Even though there are some studies on FCS designs for MB encounter, research on MB identification is rare but ongoing. To this aim, the present study is among the firsts that focuses on online identification of multiple and moving MB parameters using onboard aircraft air data and inertial sensors. The identification task is initially performed using the aircraft six degrees of freedom (6DoF) equations of motion (EOM) integrated with a 3D MB model via the utility of nonlinear Kalman filtering (KF) algorithms. Subsequently, an enhanced neural metaheuristic Kalman filter (NMKF) is proposed that improves the estimation accuracy of the MB model parameters. The results demonstrate the efficacy of the proposed NMKF in comparison with previous studies.

Risk Management Analysis Based on ISO 31000:2018 at Andi Djemma Masamba Airport, Luwu Regency

The density of activities that take place around the operating facilities at Andi Djemma Masamba Airport so that the Management of the Andi Jemma Airport Operator Unit (UPBU) Office has launched the development of Andi Djemma Masamba Airport as an aviation company so that it continues to carry out various facility improvements, especially in risk prevention with potential activities with high losses, therefore it is necessary to conduct research to find out how to map, risk assessment and design a risk control and mitigation system at the Operations facility at Andi Djemma Masamba Airport caused by activities that have the potential to cause risk. The stages of the risk management process refer to ISO 31000: 2018 including risk identification, risk analysis and risk evaluation. Expected impact and likelihood of occurrence are mapped through the risk matrix from the Federation Aviation Administration (FAA). The results showed that at the andi djemma masamba airport there were 6 events that fell into the red category, namely Aircraft accidents during take off/landing, Aircraft accidents in the apron area, Escape of dangerous goods into the aircraft, Fuel Spills on the Apron/Taxiway, Lack of building maintenance, The security inspection process is less than optimal.

Reliability modeling and analysis of uncertain competing failure systems

The competing failure system is quite common in real life, such as automotive system and aerospace system. Two distinct failure modes usually coexist in this type of system: gradual internal degradation and sudden external shock, where the occurrence of any failure mode can lead to the system failure. The system failure such as aircraft accident may cause a terrible loss of human life and property. Hence, it is worth reducing the rate of system failure by improving the system reliability. While, reliability model is the foundation of any research of reliability and epistemic uncertainty exists in the process of modeling and estimating. Therefore, we introduce uncertainty theory to investigate the competing failure system with epistemic uncertainty. The internal degradation and external shock of the system are characterized by an uncertain differential equation and an uncertain renewal process, respectively. The system reliability is defined as the uncertain measure that neither internal degradation nor external shock surpass corresponding thresholds. In addition, some reliability formulas are derived for the systems subjected to extreme shock, cumulative shock, running shock and δ-shock, respectively. Finally, a case study with respect to micro-electro-mechanical system is given to show how the proposed model is implemented.

A Probabilistic Approach to Assessing Passenger Survival in Aircraft Accidents Near an Airport Area

ABSTRACT This study proposes a probabilistic model to assess the likelihood of passenger survival in fires resulting from aircraft accidents. The model evaluates the risk of passenger death in a fire, considering the type of aircraft and airline. By comparing the time available for passengers to fully evacuate a burning plane with the time required for evacuation using modern means and technologies, onboard rescue equipment, and the qualifications of crew members and rescue personnel, we introduce a comprehensive approach to quantify passenger survival rates. Additionally, the concept of the hazard coefficient is introduced, which accounts for factors such as cabin temperature and toxic components.

The Feel of Peace: Noise Pollution and the Sovereignty of Wind

Abstract: This essay analyzes the interconnected struggles of US military occupation in Okinawa and Hawai'i through analysis of noise pollution, aircraft crashes, and community responses to these issues. While the violence of airbases transforms air into a space of violence, I turn to the ways that Okinawan and Hawaiian communities articulate their respective sovereignties through the wind. I read legal testimonies from the first noise pollution lawsuit in Okinawa and community testimonies from across O'ahu, putting them in conversation with poetry by Nakazato Yūgō and Brandy Nālani McDougall. Against military reports that implicate wind as a threat which facilitates aircraft accidents and death, the testimony and poetry elucidate how wind builds relation and enables the preservation of intergenerational memory. This archive challenges state formulations of "resolution" by elucidating how deeper histories than the state has considered press onto the body, carrying into the present not only historical wounds but also the capacity for their healing. The sovereignty of wind that they put forth marks a renewal of relationships to air and memory at the same time they are organized around radical returns of land, of night, of the body itself.

Turbofan engine health status prediction with neural network pattern recognition and automated feature engineering

PurposeThis study aims to present the concept of aircraft turbofan engine health status prediction with artificial neural network (ANN) pattern recognition but augmented with automated features engineering (AFE).Design/methodology/approachThe main concept of engine health status prediction was based on three case studies and a validation process. The first two were performed on the engine health status parameters, namely, performance margin and specific fuel consumption margin. The third one was generated and created for the engine performance and safety data, specifically created for the final test. The final validation of the neural network pattern recognition was the validation of the proposed neural network architecture in comparison to the machine learning classification algorithms. All studies were conducted for ANN, which was a two-layer feedforward network architecture with pattern recognition. All case studies and tests were performed for both simple pattern recognition network and network augmented with automated feature engineering (AFE).FindingsThe greatest achievement of this elaboration is the presentation of how on the basis of the real-life engine operational data, the entire process of engine status prediction might be conducted with the application of the neural network pattern recognition process augmented with AFE.Practical implicationsThis research could be implemented into the engine maintenance strategy and planning. Engine health status prediction based on ANN augmented with AFE is an extremely strong tool in aircraft accident and incident prevention.Originality/valueAlthough turbofan engine health status prediction with ANN is not a novel approach, what is absolutely worth emphasizing is the fact that contrary to other publications this research was based on genuine, real engine performance operational data as well as AFE methodology, which makes the entire research very reliable. This is also the reason the prediction results reflect the effect of the real engine wear and deterioration process.

Analisis Penggunaan Drone untuk Meningkatkan Respons Cepat dalam Penanganan Kecelakaan Pesawat di Area Terpencil

The use of drones in responding to aircraft accidents in remote areas is an important innovation to improve response and efficiency in emergency situations. The purpose of this study is to explore the optimization of drone use in this context using qualitative research methods and a descriptive approach with interviews, observations, and document analysis. The research showed that drones have a variety of benefits, among others: These include fast and accurate monitoring of accident sites, detailed real-time data collection for situation assessment, and the ability to provide medical assistance and rescue equipment quickly and efficiently. In addition, drones can reach areas that are difficult for traditional rescue teams to access, thereby reducing the risk to rescue workers and speeding up the search for victims. However, some obstacles have also emerged, including limited communication infrastructure that can hinder drone operations in remote areas and difficulties in coordinating drone operators with rescue teams on the ground. Other technical challenges include the limited battery life of drones, extreme weather conditions that can affect drone performance, and the need for drone operators to use their drones. effectively and safely. Future research is suggested to focus on evaluating the effectiveness of drone use in various emergency situations, further development of drone technology, and analysis of the regulatory and legal aspects of drone use. The aim of this initiative is to ensure the optimal and safe use of drones in future rescue operations, especially in remote areas that are difficult to reach by traditional methods. Cooperation between the government, technology service providers and rescue teams is also essential to overcome barriers and maximize the potential of drone use.

Past and Future Trends in Clear‐Air Turbulence Over the Northern Hemisphere

AbstractClear‐Air Turbulence (CAT) is associated with wind shear in the vicinity of jet streams in upper atmospheric levels. This turbulence occurs in cloudless regions and causes most weather‐related aircraft accidents. Recent studies have shown that in response to climate change, CAT could significantly increase over certain regions as a consequence of strengthening of jet streams. In this study we use several atmospheric reanalyses and coupled model experiments database to evaluate CAT recent and future changes in the Northern Hemisphere. Several CAT diagnostics are computed to assess the sensitivity of the results to different turbulence representations. A significant positive trend in CAT frequency is found in the reanalyses in different Northern Hemisphere regions over the period 1980–2021. The signal‐to‐noise analysis shows that over North Africa, East Asia and Middle East the increase of CAT occurrence in the last decades is likely attributed to the response to anthropogenic forcing. In contrast, over the North Atlantic and North Pacific the response to external forcing is not detectable due to a weak signal to noise ratio related to large internal variability. Future climate projections show that over several Northern Hemisphere regions, CAT is projected to increase with a high model agreement and independently of the CAT diagnostic used. The largest increase in CAT is projected to occur over East Asia. In the North Atlantic, large uncertainty remains due to lack of model agreement and different results between the various CAT diagnostics.

РАЗРАБОТКА МЕТОДИЧЕСКИХ ОСНОВ ОРГАНИЗАЦИИ ПРОВЕДЕНИЯ УЧЕНИЙ АВАРИЙНО-СПАСАТЕЛЬНЫХ СЛУЖБ ДЛЯ ОТРАБОТКИ ДЕЙСТВИЙ В ЧРЕЗВЫЧАЙНОЙ СИТУАЦИИ АВАРИИ ВОЗДУШНОГО СУДНА

The tasks that need to be worked out during exercises to improve the safety and efficiency of the organization of air traffic are considered. The main problem in solving the problems of fire safety is the time to make a single correct decision when carrying out rescue operations. Recently, exercises involving all responding units have become widespread because they can help to work out the practical tasks of rescuing people and the most rational management decisions in conditions of limited initial information and the presence of many criteria that can not be foreseen. This article considers the issues of management and interaction of fire-fighting, rescue, canine and other services during fire-extinguishing and tactical exercises to organize fire-fighting and rescue of people in situations of aircraft accidents. On the basis of the accumulated world experience in aircraft accidents and the analysis of the consequences of various aircraft accidents, the methodological recommendations for training of rescue units in simulating emergency situations of aircraft in various variants of abnormal situations were worked out.

Aircraft Accident and Crash Images Processing with Machine Learning

The aviation industry is in constant need of innovations in terms of safety and operational efficiency. In this context, low-light image enhancement technologies play an important role in a numerous areas of disciplines, from night flights to accident and collision investigations. Machine learning, deep learning methods and traditional methods not only provide the aviation industry with an effective image processing and improvement capacity in low light conditions, but also reveal important information by analysing the data of low-light images of crashed and destroyed aircraft. Within the scope of the study, traditional methods, deep learning method and machine learning are combined in order to enhance and process low-light ambient images of crashed and destroyed aircraft. By using Swish and Tanh activation functions together in the deep learning model, the performance of the neural networks used in the process of improving low-light environment images was improved and the image quality was increased. The enhanced images were evaluated and compared using PSNR and MSE as objective quality assessment measures. According to the PSNR and MSE criteria, the numerical results obtained from the image enhancement studies of the deep learning model were calculated as 29.85 and 100.44, respectively. The results introduce that the deep learning model provides better image enhancement than traditional methods. In conclusion, improvement of low-light image and processing is an important technological advancement in the aviation industry, enabling safer and more efficient operations. The successful of machine learning include deep learning and traditional methods shows that the aviation industry will achieve a safer and innovative structure in the future.

An information flow field model for human–machine interaction

PurposeAbout 70% of all aircraft accidents are caused by human–machine interaction, thus identifying and quantifying performance shaping factors is a significant challenge in the study of human reliability. An information flow field model of human–machine interaction is put forward to help better pinpoint the factors influencing performance and to make up for the lack of a model of information flow and feedback processes in the aircraft cockpit. To enhance the efficacy of the human–machine interaction, this paper aims to examine the important coupling factors in the system using the findings of the simulation.Design/methodology/approachThe performance-shaping factors were retrieved from the model, which was created to thoroughly describe the information flow. The coupling degree between the performance shaping factors was calculated, and simulation and sensitivity analysis are based on system dynamics.FindingsThe results show that the efficacy of human–computer interaction is significantly influenced by individual important factors and coupling factors. To decrease the frequency of accidents after seven hours, attention should be paid to these factors.Originality/valueThe novelty of this work lies in proposing a theoretical model of cockpit information flow and using system dynamics to analyse the effect of the factors in the human–machine loop on human–machine efficacy.

ИССЛЕДОВАНИЕ ПРОБЛЕМЫ УПРАВЛЕНИЯ ПОЖАРНЫМИ ПОДРАЗДЕЛЕНИЯМИ И РАЗРАБОТКА АЛГОРИТМА ПРИНЯТИЯ РЕШЕНИЙ РУКОВОДИТЕЛЕМ ЛИКВИДАЦИИ ЧРЕЗВЫЧАЙНОЙ СИТУАЦИИ ПРИ АВАРИИ ВОЗДУШНОГО СУДНА

This article discusses the problem of managing the emergency response process using the example of aircraft accidents. The mechanism of operation of fire and rescue units in extinguishing aircraft fires is investigated. The analysis of the origin of emergency situations of aircraft accidents has been carried out, on the basis of which the operations of aircraft operation in which the origin of emergency situations is statistically the most likely are determined. Based on the results of the analysis, typical situations of aircraft accidents are presented in the form of graph models. On these models, certain significant and critical situations are highlighted, divided by various aircraft operations. With the help of these graph models and the introduction into the system of dependence of the conducted rescue and other urgent work on the place of occurrence of an emergency, an algorithm was developed to support decision-making for the person responsible for coordinating units and conducting rescue and other urgent work related to the elimination of this kind of emergency situations, which reflects the full range of potential accidents that can happen to an aircraft. This algorithm can be used both for training the heads of fire and rescue units, and for creating emergency response techniques in case of aircraft accidents.

Navigating Turbulence: Analyzing the Crisis Response of Sriwijaya Air following the SJ182 Plane Crash using Situational Crisis Communication Theory (SCCT)

Aircraft accidents show that the industry is vulnerable to crisis and capture the complexity of crisis management and communication. This study examines the crisis response strategies employed by Sriwijaya Air in the aftermath of the SJ-182 plane crash on January 9, 2021. Utilizing the Situational Crisis Communication Theory (SCCT) framework, the research categorizes and analyzes the airline's crisis communication actions based on primary and secondary strategies. The content analysis of news articles from Kompas.com, Detik.com, CNN Indonesia, and Reuters, as well as posts from Sriwijaya Air's social media accounts, reveals the implementation of excuse, compensation, partial apology, and ingratiation strategies. The findings indicate that while Sriwijaya Air avoided denial strategies, its delayed response negatively impacted public perception. The study highlights the importance of prompt and transparent communication in crisis management. It suggests areas for future research, including examining internal decision-making processes and the long-term impacts on organizational reputation and resilience.

Quantitative assessment of whether phosphorus-based flame retardants are optimizing or degrading the fire hazard of aircraft carbon fiber/epoxy composites

Fire hazards are a primary concern in aircraft accidents, and controlling combustible materials is pivotal in managing cabin fire risks. This work aims to elucidate the impact of widespread measures that introduce phosphorus-based flame retardants (PFRs) on the fire safety of carbon fiber epoxy composites (CF/EP). Firstly, the hazardous indicators for flame retardant CF/EP composites exhibit two categories of optimization and degradation, which are caused by the intrinsic feature, pyrolysis characteristics and chemical valence states of PFRs. Secondly, the fire hazards of composites are assessed by fusing the analytic network process and information weights guided by Nash Equilibrium theory. The method combines the merits of subjective and objective weights from dimensions of intrinsic hazards, interrelationships and data dispersion of indicators. Finally, the assessed fire hazard rankings under 35 and 65 kW/m2 both are EP > CF/EP16%DOPO-HQ > CF/EP > CF/EP7%DPO. In conclusion, DOPO-HQ (+1) primarily increases the fire hazard of CF/EP due to gas-phase flame-retardant effects, while DPO (+3) predominantly reduces the fire hazard of CF/EP by contributing to condensed-phase effects. This work highlights the contradictory effects of PFRs on the fire safety of CF/EP composites due to structure and thermal stability. The proposed ANP and IW hybridization method provides a theoretical foundation for selecting flame retardants and assessing material fire hazards comprehensively.

REVIEW OF MINIMUM AGE REQUIREMENT FOR IN-FLIGHT PASSENGERS

The issue related to passenger age requirement on airplanes has emerged as a significant concern. There have been numerous instances of young passengers boarding incorrect flights, highlighting the critical need for attention. Additionally, the potential risk of aircraft accidents poses a grave danger to children onboard. The criteria and regulations surrounding minimum passenger age requirements vary widely. This study focuses on the necessity of establishing a minimum age for airline passengers and the criteria for determining passenger age in alignment with legal standards. This normative juridical research reviewed and analyzed the regulations concerning minimum age requirements and age policies adopted by airline companies. The result showed that minimum age requirement for passenger differs across various legal systems and policies implemented by countries and airlines.

Approach and Landing Energy Prediction Based on a Long Short-Term Memory Model

The statistical analysis of civil aircraft accidents reveals that the highest incidence of mishaps occurs during the approach and landing stages. Predominantly, these accidents are marked by abnormal energy states, leading to critical situations like stalling and heavy landings. Therefore, it is of great significance to accurately predict the aircraft energy state in the approach and landing stages to ensure a safe landing. In this study, a deep learning method based on time sequence data for the prediction of the aircraft approach and landing energy states is proposed. Firstly, by conducting an extensive overview of the existing literature, three characteristic parameters of altitude, velocity, and glide angle were selected as the indicators to characterize the energy state. Following this, a semi-physical simulation platform for a certain type of aircraft was developed. The approach and landing experiments were carried out with different throttle sizes and flap deflection under different wind speeds and wind directions. Then, a deep learning prediction model based on Long Short-Term Memory (LSTM) was established based on the experimental data to predict the energy state indicators during the approach and landing phases. Finally, the established LSTM model underwent rigorous training and testing under different strategies, and a comparative analysis was carried out. The results demonstrated that the proposed LSTM model exhibited high accuracy and a strong generalization ability in predicting energy states during the approach and landing phases. These results offer a theoretical basis for designing energy early warning systems and formulating the relevant flight control laws in the approach and landing stages.

Human factors in aviation maintenance: understanding errors, management, and technological trends

Aircraft maintenance and inspection are complex systems that work on a time-based schedule and require teamwork of different professionals to maintain the airworthiness of aircraft. Errors in maintenance and inspection processes cause in-flight engine shutdowns, flight delays, flight cancellation, sometimes resulting in accidents and incidents that cause significant economic consequences. Due to the substantial impact on both safety and financial aspects of an air carrier, this paper focuses on hangar maintenance as the work is carried out across several shifts by different technicians, addressing various human factor issues that contribute to errors. The paper will also briefly discuss shift work and the health problems of maintenance technicians, human factor issues affecting maintenance, the significance of understanding human factor models, and the impact of maintenance and inspection errors with case studies, particularly aircraft accidents predominantly caused by human factor issues. Additionally, the paper also explores recent advances and trends in aircraft maintenance technologies, highlighting their transformative potential through predictive maintenance, robotics, augmented and virtual reality, big data analytics, blockchain, and additive manufacturing. These technologies promise enhanced efficiency, safety, and effectiveness for maintenance practices. The paper concludes by presenting insights into error management methods and providing recommendations for future research. By integrating traditional insights with cutting-edge technological considerations, this comprehensive analysis aims to significantly contribute to the evolution of safety protocols and practices within the aviation maintenance domain.

Research on the Drift Prediction of Marine Floating Debris: A Case Study of the South China Sea Maritime Drift Experiment

Annually, hundreds of individuals tragically lose their lives at sea due to shipwrecks or aircraft accidents. For search and rescue personnel, the task of locating the debris of a downed aircraft in the vastness of the ocean presents a formidable challenge. A primary task these teams face is determining the search area, which is a critical step in the rescue operation. The movement of aircraft wreckage on the ocean surface is extremely complex, influenced by the combined effects of surface winds, waves, and currents. Establishing an appropriate drift motion prediction model is instrumental in accurately determining the search area for the wreckage. This article initially conducts maritime drift observation experiments on wreckage, and based on the results of these experiments, analyzes the drift characteristics and patterns of the debris. Subsequently, employing a wealth of observational experimental data, three types of drift prediction models for the wreckage are established using the least squares method. These models include the AP98 model, the dynamics model, and an improved model. In conclusion, the effectiveness and accuracy of the three models is evaluated and analyzed using Monte Carlo techniques. The results indicate that the probability of positive crosswind leeway (CWL) is 47.4%, while the probability of negative crosswind leeway (CWL) is 52.6%. The jibing frequency is 7.7% per hour, and the maximum leeway divergence angle observed is 40.4 degrees. Among the three drift prediction models, the refined AP98 drift model demonstrates the highest forecasting precision. The findings of this study offer a more accurate drift prediction model for the search of an aircraft lost at sea. These results hold significant guiding importance for maritime search and rescue operations in the South China Sea.

Prognosis of Blade Icing of Rotorcraft Drones through Vibration Analysis

Weather is one of the main causes of aircraft accidents, and among the phenomena caused by weather, icing is a phenomenon in which an ice layer is formed when an object exposed to an atmosphere below a freezing temperature collides with supercooled water droplets. If this phenomenon occurs in the rotor blades, it causes defects such as severe vibration in the airframe and eventually leads to loss of control and an accident. Therefore, it is necessary to foresee the icing situation so that it can ascend and descend at an altitude without a freezing point. In this study, vibration data in normal and faulty conditions was acquired, data features were extracted, and vibration was predicted through deep learning-based algorithms such as CNN, LSTM, CNN-LSTM, Transformer, and TCN, and performance was compared to evaluate blade icing. A method for minimizing operating loss is suggested.