Improve Aviation Safety Research Articles

A numerical evaluation of real-time workloads for ramp controller through optimization of multi-type feature combinations derived from eye tracker, respiratory, and fatigue patterns.

Ramp controllers are required to manage their workloads effectively while handling complex operational tasks, a crucial part of improving aviation safety. The ability to detect their instantaneous workload is vital for ensuring operational effectiveness and preventing hazardous incidents. This paper introduces a novel methodology aimed at enhancing the evaluation of the ramp controller's cumulative workload by incorporating and optimizing the feature combination from eye movement, respiratory, and fatigue characteristics. Specifically, a 90-minute simulated experiment related to ramp control tasks, using real data from Shanghai Hongqiao Airport, is conducted to collect multi-type data from 8 controllers. Following data construction and the extraction of multi-type, the workloads of all samples are categorized through unsupervised learning. Subsequently, supervised learning techniques are used to calculate feature weights and train classifiers after data alignment. The optimal feature combination is established by calculating feature weights, and the best classification accuracy is over 98%, achieved by the KNN classifier. Furthermore, numerical evaluation and threshold calculations for different workload levels are interpreted. It is promising to provide insights into future works towards human-centered data construction, processing, and interpretation to promote the progress of workload assessment within the aviation industry.

The Role of Communication and Work Environment on Employee Performance (Case Study: Boeing/Ethiopian Airlines 2019)

As social creatures, humans need good communication when interacting with others. As is the case in the work environment, the existence of good communication between employees enables them to carry out their duties following their respective procedures to achieve the goals set by the agency or work organization. This research uses the literature study method. A literature study is a collection of previous research results in the form of books, articles, scientific journals, research reports, and other sources relevant to the topic of discussion which are then compared theoretically. The Ethiopian Airlines Flight 302 incident highlighted several issues related to Boeing employee communication, work environment, and employee performance. Based on the case study discussed, this incident shows that to improve aviation safety, more attention needs to be paid to communication, work environment, and employee training in the aviation industry to prevent similar incidents from happening in the future.

Critical Connections: Enhancing Aviation Safety Through Network Analysis

The objective of this study is to explore the influence of human factors on aviation accidents across different aircraft types using Network Analysis. This research specifically analyses centrality metrics to pinpoint the most influential errors, providing a more detailed understanding of their impact on aviation safety, while prior studies have acknowledged the role of human factors. The study employed Network Analysis using Python's NetworkX library. A dataset derived from the NTSB aviation accident database is used. Bipartite networks were constructed for each aircraft type, with nodes representing accidents and human error categories and edges indicating their relationships. The analysis revealed that pilot error consistently showed the highest degree centrality across all aircraft types, indicating its frequent involvement in accidents. Closeness centrality further highlighted the central role of pilot error, showing its significant influence on the network. The findings emphasized the critical importance of addressing pilot errors to improve aviation safety. While crew errors and other human errors were less frequent, their presence in the data suggested that a comprehensive safety strategy must also consider these factors. The results of this study demonstrate that pilot error is the most influential human factor in aviation accidents across various aircraft types. By focusing on targeted interventions such as enhanced pilot training and stricter safety protocols, the aviation industry can significantly reduce accident rates.

From Text to Safety: A Novel Framework for Mining Unsafe Aviation Events Using Advanced Neural Network and Feature Network

The rapid growth of the aviation industry highlights the need for strong safety management. Analyzing data on unsafe aviation events is crucial for preventing risks. This paper presents a new method that integrates the Transformer network model, clustering analysis, and feature network modeling to analyze Chinese text data on unsafe aviation events. Initially, the Transformer model is used to generate summaries of event texts, and the performance of three pre-trained Chinese models is evaluated and compared. Next, the Jieba tool is applied to segment both summarized and original texts to extract key features of unsafe events and prove the effectiveness of the pre-trained Transformer model in simplifying lengthy and redundant original texts. Then, cluster analysis based on text similarity categorizes the extracted features. By solving the correlation matrix of these features, this paper constructs a feature network for unsafe aviation events. The network’s global and individual metrics are calculated and then used to identify key feature nodes, which alert aviation professionals to focus more on the decision-making process for safety management. Based on the established network and these metrics, a data-driven hidden danger warning strategy is proposed and illustrated. Overall, the proposed method can effectively analyze Chinese texts of unsafe aviation events and provide a basis for improving aviation safety management.

Causation Correlation Analysis of Aviation Accidents: A Knowledge Graph-Based Approach

Summarizing the causation of an aviation accident is beneficial for improving aviation safety. Currently, accident analysis mainly focuses on causal analysis, while giving less consideration to the correlation between accident causal factors and other accident factors. To clarify accident causal factors and potential patterns affecting aviation safety and to optimize data mining methods for accident causal factors, this work proposes an aviation accident causation correlation analysis model based on a knowledge graph. Firstly, the accident causal factors are identified, and a knowledge graph is constructed. Subsequently, by utilizing multi-dimensional topological analysis metrics, an aviation accident causation correlation analysis model is established, using the relationships within accident causal factors as a foundation, to determine potential patterns among accident causal factors, flight phases, accident types, and consequences and to analyze the key accident causal factors influencing accident occurrences across different flight phases. Finally, preventive measures and recommendations are provided based on the analysis conclusions. Through a case study using 437 global aviation accidents from 2018 to 2022 as samples and employing the knowledge graph-based aviation accident causation correlation analysis model, the causation relationships among accident causal factors can be expressed more clearly, the potential risks of various accident causal factors can be identified, experiences can be gained from historical accident data, and underlying patterns can be unearthed. This work can provide auxiliary decision making and be an effective reference for the prevention of aviation accidents, playing a positive role in enhancing the level of aviation safety management.

Research on Autopilot Technology in Aviation in the Era of Big Data

With the rapid progress and wide application of big data technology, its position in the autopilot technology in the aviation field is increasingly prominent, playing an irreplaceable role. This paper is devoted to a comprehensive analysis of the current development, challenges and future development direction of autopilot technology in the aviation field. We will provide more solid and in-depth theoretical support and practical guidance for improving aviation safety performance and enhancing flight efficiency. Aviation autopilot technology can make full use of massive data resources, accurately grasp the flight state, optimize the flight path, and further improve flight efficiency and aviation safety. This paper expounds its specific application status in aviation field in detail, and looks forward to its possible development trend in the future.

Reducing flight upset risk and startle response: A study of the wake vortex alert with licensed commercial pilots

The study aimed at investigating the impact of an innovative Wake Vortex Alert (WVA) avionics on pilots' operation and mental states, intending to improve aviation safety by mitigating the risks associated with wake vortex encounters (WVEs). Wake vortices, generated by jet aircraft, pose a significant hazard to trailing or crossing aircrafts. Despite existing separation rules, incidents involving WVEs continue to occur, especially affecting smaller aircrafts like business jets, resulting in aircraft upsets and occasional cabin injuries. To address these challenges, the study focused on developing and validating an alert system that can be presented to air traffic controllers, enabling them to warn flight crews. This empowers the flight crews to either avoid the wake vortex or secure the cabin to prevent injuries. The research employed a multidimensional approach including an analysis of human performance and human factors (HF) issues to determine the potential impact of the alert on pilots' roles, tasks, and mental states. It also utilizes Human Assurance Levels (HALs) to evaluate the necessary human factors support based on the safety criticality of the new system. Realistic flight simulations were conducted to collect data of pilots' behavioural, subjective and neurophysiological responses during WVEs. The data allowed for an objective evaluation of the WVA impact on pilots' operation, behaviour and mental states (mental workload, stress levels and arousal). In particular, the results highlighted the effectiveness of the alert system in facilitating pilots' preparation, awareness and crew resource management (CRM). The results also highlighted the importance of avionics able to enhance aviation safety and reducing risks associated with wake vortex encounters. In particular, we demonstrated how providing timely information and improving situational awareness, the WVA will minimize the occurrence of WVEs and contribute to safer aviation operations.

A Comprehensive Survey of Predictive Maintenance Techniques for Aircraft Engines Utilizing the C-MAPSS Dataset

The application of deep learning and sophisticated machine learning techniques is driving the rapid advancement of aircraft engine prognostics and predictive maintenance. Remain ing Useful Life (RUL) of aviation engines has been the subject of numerous studies aimed at improving prediction accuracy and efficacy to improve aviation safety and maintenance plans. Innovative approaches and technologies are demonstrated by these projects, which use a variety of methodologies and datasets, including C-MAPSS and N-CMAPSS. Combining feature engi neering, ensemble learning, and deep learning models such as Restricted Boltzmann Machines (RBMs), Long Short-Term Mem ory (LSTM) networks, Convolutional Neural Networks (CNNs), and Deep Bidirectional Recurrent Neural Networks (DBRNNs) is one prominent method. Features are chosen and models are optimized using a variety of methods, including Genetic Algorithms, Recursive Feature Elimination, Lasso, and Feature Importances. In prognostic modeling, the research emphasize the need of interpretability, model adaptability, and measuring uncertainty. Additionally, in order to pinpoint important features and improve model transparency, the study investigates the use of explainable AI techniques such aggregated feature importances with cross-validation (AFICv) and Shapley additive explanation (SHAP). In order to capture prediction uncertainties, the inte gration of Gaussian Processes (GPs) and Bayesian Deep Neural Networks (DNNs) is also investigated. This provides insights into uncertainty-aware prognosis and predictive analytics for industrial assets. The development and publication of datasets such as the N-CMAPSS dataset also makes it possible to conduct more comprehensive and realistic assessments of prognostic models under real-world flight conditions, providing useful tools for benchmarking and improving machine learning algorithms in predictive maintenance. All things considered, these research projects highlight current developments and the possibility of combining cutting edge technology to improve system reliability, improve predictive maintenance techniques, and guarantee safer airline operations. Key Words: Aircraft engine, Prognostics, Predictive main tenance, Machine learning, Deep learning, Remaining Useful Life (RUL), Aviation safety, Feature engineering, Ensemble learning, Uncertainty quantification, Explainable AI, Bayesian Deep Neural Networks (DNNs), Gaussian Processes (GPs), N CMAPSS dataset, Real flight conditions, Industrial assets, System reliability, Benchmarking

Identification of Airline Turbulence Using WOA-CatBoost Algorithm in Airborne Quick Access Record (QAR) Data

Turbulence is a significant operational aviation safety hazard during all phases of flight. There is an urgent need for a method of airline turbulence identification in aviation systems to avoid turbulence hazards to aircraft during flight. Integrating flight data and machine learning significantly enhances the efficacy of turbulence identification. Nevertheless, present studies encounter issues including unstable model performance, challenges in data feature extraction, and parameter optimization. Hence, it is imperative to propose a superior approach to enhance the accuracy of turbulence identification along airline. The paper presents a combined swarm intelligence and machine learning model based on data mining for identifying airline turbulence. Based on the theory of swarm-intelligence-based optimization algorithm, the optimal parameters of Categorical Boosting (CatBoost) are obtained by introducing the whale optimization algorithm (WOA), and the corresponding WOA-CatBoost fusion model is established. Then, the Recursive Feature Elimination algorithm (RFE) is used to eliminate the data with lower feature weights, extract the effective features of the data, and the combination with the WOA brings robust optimization effects, whereby the accuracy of CatBoost increased by 11%. The WOA-CatBoost model can perform accurate turbulence identification from QAR data, comparable to that with established EDR approaches and outperforms traditional machine learning models. This discovery highlights the effectiveness of combining swarm intelligence and machine learning algorithms in turbulence monitoring systems to improve aviation safety.

Analysis of injuries in aircraft accident victims in 2002–2022 based on the resources of the Jagiellonian University Department of Forensic Medicine in Kraków = Analiza obrażeń ofiar katastrof lotniczych w latach 2002–2022 na podstawie danych pochodzących z materiałów Zakładu Medycyny Sądowej Uniwersytetu Jagiellońskiego w Krakowie

Aim: The study aimed to analyse the most common injuries of victims of aviation accidents and compare it with the most recent literature. Methods and materials: Retrospective analysis of autopsy protocols of aircraft crash victims from the Forensic Medicine Department from years 2002–2022. The case selection has been made by two independent researchers. After applying the exclusion criteria 27 cases were obtained. Further searched for features that the bodies of victims had in common. There were analysed sex, age, circumstances of the incident, injuries, presence of psychoactive substances (alcohol, drugs), and blood concentration of carboxyhemoglobin. Results: Among 27 cases we found 19 airplane crashes, 4 glider accidents, 3 motor glider accidents, and one helicopter crash. The 1 case had no information about the type of aircraft. The injuries that emerged in each of the victims were typical results of direct impact forces. The most common were rupture of the lungs 63.0 % (n = 17/27), heart 55.6% (n = 15/27), liver 51.9% (n = 14/27), aorta 40.7% (n = 11/27) and spleen 29.6% (n = 8/27). Common bone traumas included fractures of the ribs 77.8% (n = 21/27) upper limbs 77.8% (n = 21/27), skull 74.1% (n = 20/27), pelvis 59.3% (n = 16/27) and spine 51.9% (n = 14/27). Injuries of the cervical spine occurred in victims who violently tilted their heads back. Conclusions: In most cases, the immediate cause of death was extensive multi-organ injuries, followed by thermal burns or brain injuries. In almost half of the victims, the presence of carboxyhemoglobin in the blood indicates that they were alive during the crashes. Knowledge of injury patterns can improve aviation safety and guide in reconstructing the mechanisms and sequences of the accidents.

Controlling aeroplane crashing through aviation infrastructure using multi-agent technology

Aeroplane crashing is a major concern for aviation safety. This article aim is to control aeroplane crashing through aviation infrastructure by using Multi-Agent Technology (MAT). For this 6 variables of aeroplane crashing, 20 variables of aviation infrastructure and 34 variables of MAT are extracted from the literatureto develop the information framework for the study. Fuzzy Multi-Criteria Decision-Making (FMCDM) technique is utilised to analyse prioritise, ranking, and identify interrelationship among them. The human error is evolved most significant factor of aeroplane crashing followed by others. For aviation infrastructure, runway is found to be the most important variable in global ranking. Whereas, in MAT pilot assistance agent is most prioritised variable in overall ranking. The proposed framework can assist the aviation industry to understand the behaviour of aviation infrastructure and MAT variables to control aeroplane crashing. The results of this study can be utilised by aviation professionals in improving aviation safety.

Aircraft trajectory prediction and aviation safety in ADS-B failure conditions based on neural network

With the rapid expansion of transportation demand, the number of global flights has rapidly increased, which also poses challenges to air traffic management (ATM). Considering that the radar system in ATM can no longer meet the requirements of flight safety, a very promising next-generation air traffic control technology—Automatic Dependent Surveillance Broadcast (ADS-B) technology has been introduced. However, in the event of on-board equipment failure and local area signal interference, the ADS-B’s signal will disappear or be interrupted. This sudden situation can pose a danger to aviation safety. To solve this problem, this article proposes a bidirectional long short-term memory (Bi-LSTM) network prediction method combining historical ADS-B data to short-term predict the trajectory of aircraft, which can improve aviation safety in busy airspace. Firstly, the problem of frequent dynamic modeling of different types of aircraft was solved by utilizing historical ADS-B data as the data source. Secondly, the data cleansing method is proposed for ADS-B raw data. Furthermore, considering that the spatial trajectory of the aircraft is a complex time series with continuity and interactivity, a bidirectional LSTM based aircraft trajectory prediction framework is proposed to further improve prediction accuracy. Finally, a trajectory with frequent changes was selected for prediction, and compared with 7 prediction methods. The results showed that the proposed method had high prediction accuracy, thus also improving the aviation safety of the aircraft.

The Global Impact of Improving Aviation Safety in Africa

Aviation safety has been a pillar of the aviation industry since the early 20th century when the Wright brothers made their historic maiden flight. Air transport has increased exponentially since then, with tons of cargo and millions of passengers moving monthly around the world. This increase in flights has made it imperative to reduce the number of accidents and incidents and to achieve the same level of aviation safety worldwide, but that has not been the case in Africa. Organizations like the International Civil Aviation Organization (ICAO) and the International Air Transport Association (IATA) have fostered aviation safety since the 1940s. More recently, in 2001, ICAO published Annex 19 on Safety Management, which provides standards and recommended practices under the safety management system (SMS) and the state safety program (SSP). In January 2018, the African Union launched the Single African Air Transport Market (SAATM), which presents a great opportunity for the improvement of aviation safety in Africa. Based on data from various aviation organizations and on-the-ground experiences in the past decade spearheading many aviation projects in Africa, the author, a 30-year aviation veteran with expertise in aviation safety, provides a fresh analysis of the state of aviation safety on this vast and rich continent of 54 countries. Key issues and practical solutions to those issues are presented while highlighting the global interconnectivity of aviation safety with the goal of enhancing the efforts of the various stakeholders working to raise the level of aviation safety in Africa.

Subjective measures of communication errors between pilots and air traffic controllers

Communication errors between aircraft pilots and air traffic controllers are one of the most critical issues for aviation safety. Air traffic controller and pilot interaction may involve communication errors, becoming the most common factor combination that leads to a number of high-severity aviation incidents. This study explored the factors and aviation occurrences of pilot-controller communication errors and offered several recommendations to improve flight safety. The perspectives of pilots and air traffic controllers were separately analyzed via two differently designed survey questionnaires. Then, t-tests, analysis of variance, factor analysis, and linear regression analysis were performed to analyze the survey results, and concluded five factors, two communication errors, and two main aviation occurrences. Based on the results, this study suggested improving controllers' professional abilities, such as using the correct phraseology, listening comprehension, concentration, and giving accurate, clear and concise instructions. Strengthening pilots' training on situational awareness, standard operating processes, and urgent problem-solving is also critical. Crew resource management should be highlighted to improve communication between pilots and controllers. The findings of this study serve as reference to reduce communication errors and improve aviation safety.

Analysis of the Role of Apron Movement Control (AMC) Officers in Handling Foreign Object Debris (FOD) to Improve Aviation Safety at Komodo Labuan Bajo Airport

Foreign Object Debris (FOD) are foreign objects that are animate and inanimate around the airside area which can cause damage to aircraft during landing and take off. The role of Apron Movement Control (AMC) in handling Foreign Object Debris (FOD) at Komodo Labuan Bajo Airport in order to avoid the risk of accidents for air transportation. This research uses qualitative. The data collected using observation (direct observation), interviews and documentation. After obtaining the required data, the researcher processed the data through several stages, namely data reduction, data presentation, and drawing conclusions. Based on the research results obtained by researchers, the role of Apron Movement Control (AMC) officers in handling Foreign Object Debris (FOD) to improve flight safety at Komodo Labuan Bajo Airport has been carried out in accordance with the SOP (Standard Operating Procedure) that applies at the airport. Obstacles that are often faced by Apron Movement Control (AMC) officers when handling Foreign Object Debris (FOD) to ensure flight safety at Komodo Labuan Bajo Airport, the most common of which is gravel or asphalt scraping, then nails, metal, luggage tags are found. , fruit peel, there are also animals such as dogs that get into the apron. This is Foreign Object Debris (FOD) found at Komodo Labuan Bajo Airport. Apron Movement Control (AMC) officers handle Foreign Object Debris (FOD), namely they check and clean around the apron area.

Recent Advancements in Controlled Flight into Terrain (CFIT) Prevention

A properly operating aircraft can experience a controlled flight into terrain (CFIT), which is a type of aviation mishap that causes the loss of life and property when it crashes into land or water. The aviation industry has been very concerned about CFIT accidents, and great efforts have been made to avoid them. The most current developments in CFIT prevention are summarised in this research paper. The study analyses existing CFIT prevention technologies, such as enhanced ground proximity warning systems (EGPWS), synthetic vision systems (SVS), and terrain awareness and warning systems (TAWS). It also looks at the possibility of cutting-edge technologies to improve CFIT prevention, such as automatic dependent surveillance-broadcast (ADS-B) and satellite-based augmentation systems (SBAS). The use of CFIT prevention technologies in different aircraft types is discussed in the paper, which also emphasises how effective these systems are at lowering CFIT accidents. It also examines the difficulties and constraints involved in putting CFIT prevention technologies into practice, such as the requirement for appropriate training and the expense of equipment. In order to further improve aviation safety, the paper emphasises the significance of ongoing study and development of CFIT prevention technologies. The use of these technologies can greatly lower the occurrence of CFIT accidents and increase the safety of air travel, especially when combined with pilot training and situational awareness.

Structure health inspection for aging transport aircraft

Purpose This study aims to present a diagnosis method to inspect the structure health for aging transport aircraft based on the postflight data in severe clear-air turbulence at transonic flight. The purpose of this method development is to assist certificate holder of aircraft maintenance factory as a complementary tool for the structural maintenance program to ensure that the transport aircraft fits airworthiness standards. Design/methodology/approach In this study, the numerical approach to analyze the characteristics of flight dynamic and static aeroelasticity for two four-jet transport aircraft will be presented. One of these two four-jet transport aircraft is an aging one. Another one is used to demonstrate the order of magnitude of the static aeroelastic behaviors. The nonlinear unsteady aerodynamic models are established through flight data mining and the fuzzy-logic modeling technique based on postflight data. The first and second derivatives of flight dynamic and static aeroelastic behaviors, respectively, are then estimated by using these aerodynamic models. Findings Although the highest dynamic pressure of aging aircraft is lower, the highest absolute value of static aeroelastic effects response to the wing of aging aircraft is about 3.05 times larger than normal one; the magnitude variations of angles of attack are similar for both aircrafts; the highest absolute value of the static aeroelastic effects response to the empennage of aging aircraft is about 29.67 times larger than normal one in severe clear-air turbulence. The stabilizer of aging aircraft has irregular deviations with obvious jackscrew assembly problems, as found in this study. Research limitations/implications A lack of the measurement data of vertical wind speed sensor on board to verify the estimated values of damping term is one of the research limitations of this study. This research involved potential problem monitoring of structure health for transport aircraft in different weights, different sizes and different service years. In the future research, one can consider more structural integrity issues for other types of aircraft. Practical implications It can be realized from this study that the structure of aging transport aircraft may have potential safety threat. Therefore, when the airline managed aging transport aircraft, it ought to be conducted comprehensive and in-depth inspections to reduce such safety risks and establish a complete set of safety early warning measures to deal with the potential problem of aircraft aging. Social implications It can be realized that the structure of aging transport aircraft has potential safety threat. The airline managed aging transport aircraft; it should conduct comprehensive and in-depth inspections to reduce safety risks and establish a complete set of safety early warning measures. Originality/value This method can be used to assist airlines to monitor aging transport aircraft as a complementary tool of structural maintenance program to improve aviation safety, operation and operational efficiency.

Using machine learning methods and EEG to discriminate aircraft pilot cognitive workload during flight

Pilots of aircraft face varying degrees of cognitive workload even during normal flight operations. Periods of low cognitive workload may be followed by periods of high cognitive workload and vice versa. During such changing demands, there exists potential for increased error on behalf of the pilots due to periods of boredom or excessive cognitive task demand. To further understand cognitive workload in aviation, the present study involved collection of electroencephalogram (EEG) data from ten (10) collegiate aviation students in a live-flight environment in a single-engine aircraft. Each pilot possessed a Federal Aviation Administration (FAA) commercial pilot certificate and either FAA class I or class II medical certificate. Each pilot flew a standardized flight profile representing an average instrument flight training sequence. For data analysis, we used four main sub-bands of the recorded EEG signals: delta, theta, alpha, and beta. Power spectral density (PSD) and log energy entropy of each sub-band across 20 electrodes were computed and subjected to two feature selection algorithms (recursive feature elimination (RFE) and lasso cross-validation (LassoCV), and a stacking ensemble machine learning algorithm composed of support vector machine, random forest, and logistic regression. Also, hyperparameter optimization and tenfold cross-validation were used to improve the model performance, reliability, and generalization. The feature selection step resulted in 15 features that can be considered an indicator of pilots' cognitive workload states. Then these features were applied to the stacking ensemble algorithm, and the highest results were achieved using the selected features by the RFE algorithm with an accuracy of 91.67% (± 0.11), a precision of 93.89% (± 0.09), recall of 91.67% (± 0.11), F-score of 91.22% (± 0.12), and the mean ROC-AUC of 0.93 (± 0.06). The achieved results indicated that the combination of PSD and log energy entropy, along with well-designed machine learning algorithms, suggest the potential for the use of EEG to discriminate periods of the low, medium, and high workload to augment aircraft system design, including flight automation features to improve aviation safety.

Failure Analysis of the Aviation Piston Engine Lubricating Oil System

With the continuous development and progress of science and technology, aviation has also begun to become an important field related to public life. Aviation is no longer only related to national defense, but also has entered public life. Once an aircraft has a problem, it will bring devastating damage, so it is necessary to eliminate the danger as much as possible and reduce the potential accident. The aviation piston engine oil system has an important impact on the safety of piston type aircraft. Studying the fault problems of this system is very necessary for troubleshooting and improving aviation safety. This article introduces the aviation piston engine oil system, explains its common faults and causes, and explores the troubleshooting countermeasures for the aviation piston engine oil system.

Aviation’s Approach Towards Pilots’ Mental Health: a Review

Aviation safety is and always will be a primary priority. Technological advancements, improved pilot training, and improved procedures have all played a significant role in this regard over the years. According to several studies, human factors currently account for over 70% of all accidents. As a result, in order to improve aviation safety, the focus should be on the variables that contribute to those errors, with mental health being one of the most critical. Fatigue, stress, anxiety, depression, and burnout are all connected with poor mental health in aviation. This paper presents a systematic review of articles on pilots’ mental health, together with an anonymous survey to 182 pilots focused on understanding how pilots, airlines and aviation organizations handle pilots’ mental health. According to the survey, pilots bear most of the responsibility and are the ones who experience the consequences if their mental health declines. Additionally, it seems that pilots do not view therapy as a viable choice; even though the majority of respondents said they would turn to therapy if they felt the need, many believed that doing so would worsen their mental health. From both the literature review and the survey results, it is concluded that the complex and subjective nature of mental health imply that this is not a job for any single organization, but that a collective work across all the aviation industry is needed, for which all must be responsible.