Aviation Accidents Research Articles

Improved gray correlation analysis and combined prediction model for aviation accidents

PurposeThe purpose of this paper is to identify the key influencing factors of aviation accidents and to predict the aviation accidents caused by the factors.Design/methodology/approachThis paper proposes an improved gray correlation analysis (IGCA) theory to make the relational analysis of aviation accidents and influencing factors and find out the critical causes of aviation accidents. The optimal varying weight combination model (OVW-CM) is constructed based on gradient boosted regression tree (GBRT), extreme gradient boosting (XGBoost) and support vector regression (SVR) to predict aviation accidents due to critical factors.FindingsThe global aviation accident data from 1919 to 2020 is selected as the experimental data. The airplane, takeoff/landing and unexpected results are the leading causes of the aviation accidents based on IGCA. Then GBRT, XGBoost, SVR, equal-weight combination model (EQ-CM), variance-covariance combination model (VCW-CM) and OVW-CM are used to predict aviation accidents caused by airplane, takeoff/landing and unexpected results, respectively. The experimental results show that OVW-CM has a better prediction effect, and the prediction accuracy and stability are higher than other models.Originality/valueUnlike the traditional gray correlation analysis (GCA), IGCA weights the sample by distance analysis to more objectively reflect the degree of influence of different factors on aviation accidents. OVW-CM is built by minimizing the combined prediction error at sample points and assigns different weights to different individual models at different moments, which can make full use of the advantages of each model and has higher prediction accuracy. And the model parameters of GBRT, XGBoost and SVR are optimized by the particle swarm algorithm. The study can guide the analysis and prediction of aviation accidents and provide a scientific basis for aviation safety management.

Pengendalian Wildlife Hazard oleh Unit Safety Risk & Quality Control di Area Airside Bandar Udara Internasional H.A.S. Hanandjoeddin Tanjung Pandan

The importance of the role of the Safety Risk &; Quality Control unit at H.A.S. Hanandjoeddin Tanjung Pandan International Airport, makes the Executive General Manager must implement safety in the Safety Risk &; Quality Control unit assigned to handle wildlife hazard with standards that must be met to ensure flight safety can be managed properly because aviation accidents can cause many material losses, financial and fatalities. This study aims to find out how the Safety Risk &; Quality Control Unit controls the Wildlife Hazard and to find out how the Safety Risk &; Quality Control unit coordinates with other units in handling Wildlife Hazard at the airside of H.A.S. Hanandjoeddin Tanjung Pandan International Airport. This study used qualitative research methods using three data collection techniques in the form of observation, interviews and documentation. The interview used a semi structured technique and five sources as information. To analyze the data that has been collected using three techniques, namely data reduction, data display and Conclusion drawing / verification. The results showed that in order to wildlife hazard control at the International airport H.A.S. hanandjoeddin tanjun pandan, the safety & quality control unit conducted the three-way prepararation of toxins, wedges and shootings. If there is a report about the presence of wild animals the SRQC unit will coordinate with the AMC, ATC, AVSEC, PKP-PK units and the Maintenance Unit for handling wildlife hazard. This action is in the form of expulsion using vehicles and the provision of poison and shooting to reduce the population of wild animals passing through the airport area.

Legal Status of Accident Investigation Results of Space Activities in The Process of Enforcement of Space Law Enforcement in Indonesia: Between Procedural Justice and Substance Justice

Investigation findings and information obtained as a consequence of investigations cannot be treated as state secrets or utilized as evidence in court. This principle, which is followed by international aviation law, has generated controversy in Indonesian law enforcement practices, particularly when it comes to criminal and civil procedural laws, which acknowledge that the findings of investigations can serve as a foundation for further inquiries to identify suspects. The study’s findings revealed that, first, the ad hoc team’s philosophical investigation of space accidents within the framework of international law aims to determine the causes of spacecraft accidents to ensure that they don’t happen again in the future within the context of the main legal goal of benefit and justice for more people (the most people with the most happiness possible). While compensation claims are based on liability and without error (strict liability), police investigations as described in Articles 95 through 100 are intended to find criminal acts in space activities. There is no functional relationship between the two processes, so the findings of police or special team investigations cannot be used as the basis for carrying out legal actions. Given the obvious differences between the team’s investigation and the police’s investigation, there must be coordination between the technical team of the ministry and the police regarding the responsibilities and functions of these two institutions, each of which has a unique position and set of legal obligations. With this knowledge, it is believed that there won’t be any criminal prosecutions brought against the crews of spacecraft, similar to those brought against pilots in situations involving aviation accidents.

A Quantitative Study of Aircraft Maintenance Accidents in Commercial Air Transport

Aircraft maintenance is defined by the ICAO as the tasks that need to be carried out on an aircraft to ensure its continuing airworthiness. Accidents that result from aircraft maintenance activities are a direct measurable outcome that can be used to broadly assess the effectiveness of maintenance activities. This research seeks to understand the characteristics of aircraft-maintenance-related accidents and how these have changed over time. An exploratory design was utilized, which commenced with a content analysis of 358 accidents from the Aviation Safety Network, followed by a quantitative ex post facto study. The results showed that aircraft-maintenance-related accidents were 1.7 times less fatal compared to all aviation accidents in the database. Fatalities were reduced significantly from the 1990s following major accidents with many fatalities; this was countered by several industry-wide initiatives. However, the number of accidents have continued to grow by one each year. Relative to all accidents, it was found that maintenance contributes to (2.0 ± 0.4)% of all accidents, which increased to (3.8 ± 0.7)% from 1998 to 2019, up from (1.3 ± 0.2)% from 1941 to 1997. However, the rate of maintenance accidents per kilometer flown has decreased exponentially halving every 27.7 years. The results showed that the most common age of an aircraft involved in a maintenance accident was 5 to 15 years, corresponding to the first heavy maintenance period of an aircraft (6 to 12 years). Further results for age showed no correlation to the fatalness of accidents; however, older aircraft were more likely to be written off.

Intelligent Vision Based Decision Making System for Aviation Accidents and Incidents

Safety has become the primary concern for the air transportation system nowadays primarily due to increasing air traffic throughout the world. Various regulatory bodies have been maintaining enormous amount of aviation accidental data repositories. This past data is highly complex because of its many temporal and geographical components along with multiple variables. To be able to analyze this past data, there is always a need of user friendly and GUI based System. This article has proposed an intelligent vision-based decision-making system for the exploration of past aviation accidents and incidents dataset. The proposed visual query-based model is capable to analyse the major factors like flight phases, human factors, weather conditions and faulty components in particular aircraft models which are responsible for those unsafe events and may claim life of many passengers who are traveling and crew personnels. This model enables the users to express “what” visuals should be created instead of “how” to create them. Various case studies conducted through visual queries have proved that the system will be highly able to improve situational awareness regarding flight conditions to the crew members and air traffic controllers along with aviation authorities so that they are able to take timely decisions and deciding on what kind of training staff members need to reduce the consequences of such accidents and incidents.

Editorial

Dear Readers,  Welcome to the seventh issue in 2023. I am very pleased to announce the journal’s continued high Scopus CiteScore of 2.7 and Web of Science Impact Factor of 1.0 for 2022, indicating another scientifically successful year. On behalf of the J.UCS team, I would like to thank all the authors for their sound research contributions, the reviewers for their very helpful suggestions for improvements, and the consortium members for their financial support. Your commitment and dedicated work have contributed significantly to the long-lasting success of our journal.  As we want to secure the financial support also for the years to come, we are looking for institutions and libraries to financially support our diamond open access journal as consortium members, who will then benefit from the research community, international visibility, and the opportunity to manage special issues and focused topics within the journal. Please think about the possibility of such financial participation of your institution, we would be very grateful for any kind of support.  In this regular issue, I am very pleased to introduce six accepted papers from seven different countries and 20 involved authors.  Ana P. Allian, Leandro F. Silva, Edson OliveiraJr and Elisa Y. Nakagawa from Brazil present VMTools-RA, a reference architecture that encompasses the knowledge and practice for developing and evolving variability tools. In a collaboration between researchers from the UK and Estonia, Vimal Dwivedi, Mubashar Iqbal, Alex Norta and Raimundas Matulevičius are focusing their research on the evaluation of a legally binding smart-contract language for blockchain applications. Monika, Seema Verma, and Pardeep Kumar from India discuss an intelligent vision-based decision-making system for the exploration of past aviation accidents and incidents, which is based on a visual query-based model capable of analyzing the major factors including flight phases, human factors, weather conditions, and faulty components in particular aircraft models. Luis Eduardo Ordoñez Palacios, Víctor Bucheli Guerrero and Hugo Ordoñez from Colombia present their research on integrating satellite imagery and meteorological data to estimate solar radiation applying and evaluating five machine learning models. In a collaboration between researchers from Palestine and Egypt, Muath Sabha, Thaer Thaher, and Marwa M. Emam apply cooperative swarm intelligence algorithms to adaptive multilevel thresholding segmentation of COVID-19 CT scan images. Geovana Ramos Sousa Silva, Genaína Nunes Rodrigues and Edna Dias Canedo from Brazil introduce their work on a modeling strategy to design and verify chatbot conversational flows via the Uppaal model checking tool.  Enjoy Reading! Cordially, Christian Gütl, Managing Editor 

Research on Vietnamese Attention in Aviation Insurance

The article has clarified the theoretical basis of interest and aviation insurance products. The aviation insurance business is still underdeveloped while the aviation industry is an important factor in Vietnam's economic development, which makes aviation development unsustainable. To solve the research purpose set out, the research team conducted a survey on the understanding and perception of the benefits of aviation insurance products with 196 Vietnamese customers traveling by plane. The results of the data analysis show that the majority of people traveling by plane do not know about aviation insurance products. Many interviewees indicated that they knew that the cost of purchasing aviation insurance products was very high and that they knew through an aviation insurance product consultant. Respondents also said that aviation accidents rarely happen, so they see the benefits of this product are not high. In addition, based on descriptive statistical data of the survey results, the gender factor did not affect the understanding and perception of the benefits of aviation insurance products.

Uncovering the Hidden Correlations between Socioeconomic Indicators and Aviation Accidents in the United States

Rules and regulations for accident mitigation have been implemented between all players. It is necessary to use new technologies and resources for human factors to mitigate future accidents to decrease accidents. It has been verified that accidents by sabotage are currently non-existent and that most of the fatalities are during the flight and in the runway approach phase. Severe accidents with associated fatalities are a small number that tend to decrease over time. Human errors, although with all the mitigations over time, are still the most significant cause of accidents; although accidents have decreased, other factors may be related to this type of error, such as the lack of personnel for the operation of a flight. Accidents can also be related to other factors, such as economic factors. GDP growth is positively correlated with accidents, and inflation is negatively correlated. It is also found that the inflation factor is also related to the number of flights due to a lack of demand.

The influence of safety-specific transformational leadership on safety behavior among Chinese airline pilots: The role of harmonious safety passion and organizational identification

Safety is a key consideration in the worldwide civil aviation industry, in which an increasing number of aviation accidents are related to flight crew performance. Based on social exchange theory and social identity theory, the primary objective of this study is to examine the relationships among safety-specific transformational leadership, harmonious safety passion, organizational identification, and safety behavior among Chinese airline pilots. Data was collected from 332 commercial airline pilots from China Eastern Airlines Co., Ltd. using self-report questionnaires. Correlation analysis, mediation analysis and moderated analysis were conducted. The results show that safety-specific transformational leadership has a positive effect on safety behavior and an indirect effect on safety behavior through the influence of harmonious safety passion. Harmonious safety passion acts as a mediating role, whereas organizational identification acts as a moderating role in the relationship between safety-specific transformational leadership and safety behavior. The managerial implications stemming from the findings of this study may promote pilots’ safety behavior, improve airline management and maintain safety of flight operation.

The Impact of Covid-19 on Human Factors in the Aircraft Maintenance Environment - Effects within Approved Aircraft Maintenance Organisations Operating in Malta

Since the aviation industry itself creates a risky environment, a high levelof safety must be ensured during the flight of an aircraft. Aircraft maintenance up tohigh standards is a main requirement intended to ensure the safety of flights and hascontributed to drastically reduce the number of aviation accidents. In addition, for everyaviation accident the cause must be identified to prevent re-occurrence and reduce thenumber further down the line. Statistically, most aviation accidents occurring nowadaysare due to human errors, making awareness of human factors in every area of aviation veryimportant, including the maintenance area. With the outbreak of COVID-19 pandemic in2020, peoples everyday life across the whole world was affected. The aviation industrywas heavily impacted by this pandemic with reduction in aviation operations, affectingairports, airlines, as well as approved aircraft maintenance organisations. The effect ofCOVID-19 on human factors has been both physiological and psychological. Since thephysiological and psychological aspects of human factors are known to have considerableeffects on the human error element in aviation, it can be concluded that with COVID-19the risk of committing errors in aviation has increased. This research is aimed to discoverthe element of such increased risk in aviation, particularly within the aircraft maintenanceenvironment. Research participants are people working in the aircraft maintenanceenvironment. Several independent variables identified from the literature review, known toaffect an identified dependent variable, form an initial conceptual framework. A survey hasbeen conducted through which exploratory statements have been set up to address theindependent variables, each in a context of the impact of human factors on the requiredsafety standards in aircraft maintenance. Data collected from a total of 75 respondents hasbeen checked for validity using a test question inserted in the survey. This has given a totalof 68 valid responses which have been analysed. Analysis has been conducted using SPSSSoftware. Analysis for data consistency revealed an acceptable Cronbachs Alpha value ofover 0.8. SPSS which was then used to carry out dimension reduction using ExploratoryFactor Analysis (EFA). Through the EFA technique the number of independent variableshas been reduced for further research from 22 to 6. Final analysis of the data shows thatmost of the respondents agree that in general Covid-19 has had a similar impact on humanfactors in aircraft maintenance, as it has had on the rest of the aviation industry. Based onthis research it is suggested that measures should be taken so that a phenomenon likea pandemic situation, that would affect the physiological and psychological aspects ofpeople, is addressed in the human factors element of aviation, in particular the aircraftmaintenance environment. The pilot-study conducted also sets up a basis for futureresearch in human factors in aircraft maintenance, and how they can be influenced bysituations that can pose higher risks for a person to commit errors while performing workrelated to aviation, particularly while carrying out aircraft maintenance performance andcertification activities before an aircraft can be released for further flights.

Comparison of Microphysics Schemes on WRF-HAILCAST Model to Study Hailstone in Aeronautics (Case Study: Hail Strike on AirAsia A320-200, November 16, 2018)

A hail strike is caused by the hail phenomenon, an aviation accident caused by environmental factors. Hail is a type of extreme weather in the form of precipitation ice grains which is generally caused by cumulonimbus clouds. Research on hail modeling can be carried out using the Weather Research Forecasting Hail Forecast (WRF-HAILCAST), which can model the structure, dynamics, microphysics, hailstone distribution, and estimate the maximum diameter of the hailstone. In this study, the hail modeling that caused the hail strike on the AirAsia A320-200 aircraft on November 16, 2018, was carried out using the WRF-HAILCAST model with six different microphysics schemes. This study compares the results of the microphysics scheme on the WRF-HAILCAST model in hail modeling. The results of this study indicated that each microphysics scheme produces a different outcome. Generally, the spatial and temporal distribution of the entire scheme could indicate the presence of convective clouds reaching 40–55 dBZ. Each scheme showed the microphysical processes of clouds consisting of different hydrometeor particles. However, only MY2 and NSSL showed the presence of supercooled water, and those schemes could estimate maximum diameter hailstone worth 25.8 mm and 13.2 mm. So, NSSL is the best microphysics scheme in this study.DOI: 10.17977/um024v8i12023p020

PKP-PK Unit Personnel Strategy Analysis in Mitigating Aircraft Accidents at Adi Soemarmo Boyolali International Airport

This study aims to find out how well and how hard the Aviation Accident Assistance and Fire Fighting (PKP-PK) personnel handle fires at Adi Soemarmo Boyolali International Airport. This research will help by facilitating PKP-PK to follow the safety SOP (Standard Operation Procedure) if an incident occurs. Case study method descriptive qualitative research was used for this study. The PKP-PK unit at Adi Soemarmo Boyolali International Airport was used as a research location. The results of the review show how the progress and progress of the Avionics Accident Management and Fire Fighting (PKP-PK) workforce in tackling fire incidents at the Adi Soemarmo Boyolali World Air Terminal. PKP-PK personnel always carry out their responsibilities in accordance with applicable regulations.

Qualitative Analysis of General Aviation Pilots’ Aviation Safety Reporting System Incident Narratives Using the Human Factors Analysis and Classification System

ABSTRACT NASA's Aviation Safety Reporting System (ASRS) gathers data from voluntary aviation safety reports for nonfatal accidents and incidents. These reports are a valuable resource in identifying unsafe occurrences and hazardous situations in the aviation industry. A tool known as the Human Factors Analysis and Classification System (HFACS), initially developed for military use, has proven useful for root cause analysis of human causes in aviation accidents and incidents, including in commercial contexts. This research study utilizes HFACS to classify the ASRS safety reports to identify key indicators in General Aviation (GA) non-fatal accidents and incidents. Qualitative data analysis reveals relationships between incidents and causal factors, indicating 53% of incidents to be perceptual errors, 32% decision errors, and 26% skill-based errors. Prevalent preconditions for these errors include crew resource management (53%), physical environment (32%), and technological environment (16%). The study aligns with previous research findings, suggesting that crew resource management is a common factor in both GA incidents and commercial and military aviation. While GA pilots may make different types of errors, such discrepancies are not prevalent in the voluntarily reported data.

Aircraft overrun warning systems capabilities and disadvantages

Currently, attention of the aviation community and authorities is being increasingly focused on flight safety in the landing phase. It is accounted for the increased frequency of incidents in the final phase of flight and significant threats associated with the consequences of these events. The statistics of aviation accidents reveals that from 1959 to 2019, 55% of aircraft crashes in the world occurred in the phases of landing and takeoff. The given crashes resulted in 51% of all fatalities on board aircraft. In most cases, causes of these aviation accidents are involved with some kind of human error. Off-design conditions at an aerodrome also have a significant adverse effect on the aviation accident potential and severity. The increasing intensity of flights, airspace congestion, strict ATC-imposed restrictions, the necessity to perform a variety of procedures and general flight crew stress in conjunction with dynamically changing external conditions can disorient a flight crew and lead to a landing with a flight envelope overrange. The search for a solution in terms of preventing aircraft overruns is actively being conducted both by aviation authorities and aircraft manufacturers and operators. Within the framework of this review, the major external and operational factors, affecting the dynamics and the nature of the aircraft roll via the runway, are analyzed, including the context of several accidents that have occurred in recent years. In addition, the article emphasizes the methods to prevent and anticipate aircraft overruns based on the principles of active protection. In particular, the article examines the main operation aspects of onboard avionics systems installed on Boeing and Airbus aircraft and highlights the focus areas of their upgrading. Special attention is paid to the influence of the pilot and the possibility of taking his actions into account to predict an outcome of landing.

Analysis of the existing approaches to in-flight aircraft rerouting

Currently, the large number of aircraft accidents is associated with the loss of control in flight and a controlled flight into terrain. It frequently occurs due to a change of flight conditions, relatively which a preparation for departure was carried out, and involves the necessity to reroute efficiently in the conditions of increased psychophysiological load and time constraint for decisionmaking. Generated thunderstorm cells on route, artificial or natural obstacles, not considered while planning a route, can result in amending a flight plan, which was earlier accepted and implemented in the automatic, flight director or manual modes of control. The lack of comprehensive situational awareness is fairly a frequent cause of aviation accidents for general aviation aircraft. Aviation accidents of transport category aircraft are typically associated with incorrect crew actions when dangerous flight zones are detected along the route. The article represents an overview and analyzes modern onboard facilities to detect obstacles, as well as required pilot actions to reroute a flight for in-flight detected obstacle avoidance. The current level of avionics development provides situational awareness necessary for obstacles avoidance but requires timely, correct and sometimes non-obvious flight crew rerouting decisions. The algorithms used with robotic packages of various applications in related fields ensure the automatic rerouting for obstacle avoidance. They cannot be directly used or adapted for the implementation on board an aircraft due to the lack of consideration for aircraft specific features when obstacle avoidance routing, i.e., restrictions of control parameters (an angle of attack, overload, roll angle), capabilities of a control system (available rate of overload, available and maximally allowable angular rolling velocity, etc.). Therefore, the issue to develop a system to support pilot decisions for obstacle avoidance is relevant. It encompasses the synthesis of safe alternatives for obstacle avoidance which are optimal by a pilot-assigned criterion (minimum loss of time, minimum additional fuel consumption, etc.).

MAJOR AIR DISASTERS: ACCIDENT INVESTIGATION AS A TOOL FOR DEFINING ERAS IN COMMERCIAL AVIATION SAFETY CULTURE

The air transport evolution has been littered with major air disasters, the occurrence of which has not only had the negative effects inherent to any disaster. Air accident investigation has provided a wealth of knowledge that has advanced the aviation industry and its safety. International Civil Aviation Organization has exercised international regulatory leadership since 1947, developing tools with international cooperation, such as the air accident investigation methodology. ICAO has forged a change in perspective on safety attributions or factors in different historical eras, using this methodology to deepen the understanding of the causes and thereby achieve aviation safety improvement. Authors aimed to analyze, through a detailed study of the world’s worst aviation accidents, their contribution to understanding the details of aviation safety culture. Beyond the technical issues and fatality rates, the necessary analysis is what knowledge researchers have gained from the beginning, exploring the attributions of reports and knowing what diverse factors have predominated in different eras. Descriptive analyzes of air disaster investigation have two objectives: to identify the beginnings of a global safety culture resulting from evolution in operational safety and to relate the different eras to the attributions of air accident investigations.

Securing air transportation safety through identifying pilot's risky VFR flying behaviours: An EEG-based neurophysiological modelling using machine learning algorithms

Most general aviation accidents result from pilot errors while flying under visual flight rules (VFR), many of which are linked to situation awareness prone to meteorological and situational effects. This study reveals the impacts of weather and flight route factors contributing to unsafe flight behaviours on pilots’ performance (flight operation & neurophysiology). Importantly, another purpose is to identify risky flight behaviours by executing EEG-based neurophysiological modelling for securing air transportation safety. A two-by-two (adverse/clear weather; experienced/inexperienced flight route) within-subject experiment was conducted. Inexperienced flight routes have an explicit impact on contributing to unsafe flight operations except during departure and descent. However, the neurophysiological activity in power spectrum density and cooperative correlation showed that meteorology deterioration may require more mental efforts and have an implicit impact on maintaining safe flight awareness, making it challenging to correct risky flight operations due to mental resource exhaustion possibly. For the pilot's risky behaviour identification, neurophysiological modelling using EEG data was established, and a leave-one-subject-out cross-validation was used to evaluate the modelling feasibility, with the highest accuracy of 80.8%. This work could serve as the foundation for developing a real-time risky flight behaviour prediction modelling that could support safety-enhancing air transportation via artificial intelligence.

Probabilistic and Statistical Analysis of Aviation Accidents

The aim of this paper is to study the role that probabilistic and statistical analysis tools, such as ARIMA models, can have in the improvement of aviation safety levels. This is to be achieved by using available accident data, organizing it probabilistically, based on the different variables involved giving a better understanding of them as well as trough time series analyses forecast future values and trends. The results achieved provide valuable information usable by different aviation entities to be used on the accident prevention field through a proactive approach with regards to identified key areas.

Predicting the Severity of Runway Excursions from Aviation Safety Reports

Despite all technological advances, runway excursions (REs) remain among the most frequent aviation accidents. Several factors have been shown to contribute to RE incidents. Although different studies have addressed the prediction of REs using quantitative data, the present study contributes to RE research by applying advanced quantitative analysis methods using text mining of voluntarily submitted reports to predict aircraft damage as the outcome of a runway excursion event. The study also intended to unveil relevant predictors from the voluntary safety reports data. The random forest model presented better results than the naïve Bayes and the gradient boosting approaches. The positive predictive value of the random forest method was measured at 74.90%, which is in contrast to 63.58% of the naïve Bayes method and 72.56% of the gradient boosting method. Although several generalizable topics were identified in the reports (such as runway and touchdown zone characteristics, flight control during approach, aircraft malfunction, weather factors, and runway surface and braking action conditions), no defined predictors for runway excursion events resulting in aircraft damage could be established.

Using Catalyst Mass-Based Clustering Analysis to Identify Adverse Events during Approach

Discovering and mitigating potential risks in advance is essential for preventing aviation accidents on routine flights. Although anomaly detection-based explanation techniques have successfully uncovered potential risks for proactive flight safety management, explaining group-scale precursors using these methods is challenging due to the assumption that risky flights are significantly fewer in number than normal flights, as well as the reliance on non-domain knowledge for hyperparameter adjustment. To characterize the group-scale precursors more accurately, we propose a novel technique called Catalyst Mass-Based Clustering Analysis (CMCA), which employs a composite entropy-energy dissipation index during approach to evaluate the energy management performance. On this basis, an optimization objective is constructed to identify clusters exhibiting significant energy management differences during the approach phase. We successfully identify group-scale precursors with energy management issues by applying CMCA to a combination of minority-labeled and majority-unlabeled flights. Comparative experiments show that these precursors have energy levels that deviate from normal flights by 5.83% and 10.93%, respectively, 1000 ft above touchdown, demonstrating the effectiveness of our method. The analysis suggests that poor energy management awareness on the part of pilots could be responsible for these group-scale precursors. Notably, the results obtained using CMCA are comprehensible for Subject Matter Experts, making the method a valuable tool for proactive flight safety management.