Predicting Pilot Misperception of Runway Excursion Risk Through Machine Learning Algorithms of Recorded Flight Data

The research used predictive models to determine pilot misperception of runway excursion risk associated with unstable approaches. The Federal Aviation Administration defined runway excursion as a veer-off or overrun of the runway surface. The Federal Aviation Administration also defined a stable approach as an aircraft meeting the following criteria: (a) on target approach airspeed, (b) correct attitude, (c) landing configuration, (d) nominal descent angle/rate, and (e) on a straight flight path to the runway touchdown zone. Continuing an unstable approach to landing was defined as Unstable Approach Risk Misperception in this research. A review of the literature revealed that an unstable approach followed by the failure to execute a rejected landing was a common contributing factor in runway excursions. Flight Data Recorder data were archived and made available by the National Aeronautics and Space Administration for public use. These data were collected over a four-year period from the flight data recorders of a fleet of 35 regional jets operating in the National Airspace System. The archived data were processed and explored for evidence of unstable approaches and to determine whether or not a rejected landing was executed. Once identified, those data revealing evidence of unstable approaches were processed for the purposes of building predictive models. SAS™ Enterprise MinerR was used to explore the data, as well as to build and assess predictive models. The advanced machine learning algorithms utilized included: (a) support vector machine, (b) random forest, (c) gradient boosting, (d) decision tree, (e) logistic regression, and (f) neural network. The models were evaluated and compared to determine the best prediction model. Based on the model comparison, the decision tree model was determined to have the highest predictive value. The Flight Data Recorder data were then analyzed to determine predictive accuracy of the target variable and to determine important predictors of the target variable, Unstable Approach Risk Misperception. Results of the study indicated that the predictive accuracy of the best performing model, decision tree, was 99%. Findings indicated that six variables stood out in the prediction of Unstable Approach Risk Misperception: (1) glideslope deviation, (2) selected approach speed deviation (3) localizer deviation, (4) flaps not extended, (5) drift angle, and (6) approach speed deviation. These variables were listed in order of importance based on results of the decision tree predictive model analysis. The results of the study are of interest to aviation researchers as well as airline pilot training managers. It is suggested that the ability to predict the probability of pilot misperception of runway excursion risk could influence the development of new pilot simulator training scenarios and strategies. The research aids avionics providers in the development of predictive runway excursion alerting display technologies

Automated Operations and Safety Data Collection and Usage in Contemporary Flight Operations Quality Audit Programs

Flight Operations Quality Audit (FOQA) programs are becoming more common to airlines of today. Flight data recording devices modified for repeated and daily data readouts have been dem-onstrating their unquestionable advantages in FOQA programmes. They demonstrate the interest of airlines, that use them, to transport people, cargo and mail in safe and efficient way. The paper will present general FOQA structure, historical developments in this field together with common obstacles when introducing FOQA to an airline. It also brings the latest data and understanding of benefits that FOQA has on airlines operation with potential applications of similar concepts to other means of transportation as well

Aviation Safety Risk Analysis and Flight Technology Assessment Issues

This text highlights the significance of flight safety in China's civil aviation industry and emphasizes the need for comprehensive research. It focuses on two main areas: analyzing exceedance events and statistically evaluating non-exceedance data. The challenges of current approaches lie in insufficient cause analysis for exceedances. The proposed solutions involve data preprocessing, reliability assessment, quantifying flight control using neural networks, exploratory data analysis, flight personnel skill evaluation with machine learning, and establishing real-time automated warnings. These endeavors aim to enhance flight safety, personnel assessment, and warning mechanisms, contributing to a safer and more efficient civil aviation sector

The Analysis of Flight Operational Quality Assurance (FOQA) Data: Exploration of a Proposed List of Improved Safety Parameters

Several FOQA parameters that could enhance air carrier safety have not been recorded and/or analyzed. Airlines have traditionally chosen a limited number of parameters for analysis (thousands have been available for recording). No study in the U.S. had addressed FOQA parameters, leading to an exploratory mixed methods study. A conference call comprising FOQA industry experts was planned and completed to (a increase researcher knowledge and (b) discuss a list of improved safety FOQA parameters. A questionnaire, derived from the suggested list, was electronically sent to 40 FOQA professionals. The respondents represented 75% of the airlines with an FAA-approved FOQA program. The quantitative and qualitative responses reported a high importance for the 11 suggested parameters (confidence level from 0 to 100; M= 82.79, SD = 14.05). The industry indicated amenability to the discussion of improved safety parameters. The addressing of technical difficulties, combined with further studies/debates, has been recommended

Health management design considerations for an all electric aircraft

This paper explains the On-board IVHM system for a State-Of-the-Art “All electric aircraft” and explores implementing practices for analysis based design, illustrations and development of IVHM capabilities. On implementing the system as an on board system will carry out fault detection and isolation, recommend maintenance action, provides prognostic capabilities to highest possible problems before these became critical. The vehicle Condition Based Maintenance (CBM) and adaptive control algorithm development based on an open architecture system which allow “Plug in and Plug off” various systems in a more efficient and flexible way. The scope of the IVHM design included consideration of data collection and communication from the continuous monitoring of aircraft systems, observation of current system states, and processing of this data to support proper maintenance and repair actions. Legacy commercial platforms and HM applications for various subsystems of these aircraft were identified. The list of possible applications was down-selected to a reduced number that offer the highest value using a QFD matrix based on the cost benefit analysis. Requirements, designs and system architectures were developed for these applications. The application areas considered included engine, tires and brakes, pneumatics and air conditioning, generator, and structures. IVHM design program included identification of application sensors, functions and interfaces; IVHM system architecture, descriptions of certification requirements and approaches; the results of a cost/benefit analyses and recommended standards and technology gaps. The work concluded with observations on nature of HM, the technologies, and the approaches and challenges to its integration into the current avionics, support system and business infrastructure. The IVHM design for All Electric Hybrid Wing Body (HWB) Aircraft has a challenging task of addressing and resolving the shortfalls in the legacy IVHM framework. The challenges like sensor battery maintenance, handling big data from SHM, On-Ground Data transfer by light, Extraction of required features at sensor nodes/RDCUs, ECAM/EICAS Interfaces, issues of certification of wireless SHM network has been addressed in this paper. Automatic Deployable Flight Data recorders are used in the design of HWB aircraft in which critical flight parameters are recorded. The component selection of IVHM system including software and hardware have been based on the COTS technology. The design emphasis on high levels of reliability and maintainability. The above systems are employed using IMA and integrated on AFDX data bus. The design activities has to pass through design reviews on systematic basis and the overall approach has been to make system highly lighter, effective “All weather” compatible and modular. It is concluded from the study of advancement in IVHM capabilities and new service offerings that IVHM technology is emerging as well as challenging. With the inclusion of adaptive control, vehicle condition based maintenance and pilot fatigue monitoring, IVHM evolved as a more proactively involved on-board system

Helicopter Flight Operational Quality Assurance (HFOQA): Development of HFOQA Analysis Software

Flight Operational Quality Assurance (FOQA), or Flight Data Monitoring (FDM), has benefited flight safety in both fixed-wing and helicopter operations. The relative youth of FOQA programs has resulted in their minimal application among the helicopter fleets of the world; thus, Helicopter FOQA (HFOQA) has merited consolidation and expansion. This mixed methods design developed HFOQA analysis software via a blend of the qualitative data from helicopter and FOQA experts with quantitative data represented by a sample of de-identified digital flight data from 1,014 helicopter flights. Development of the software emphasized three domains of interest: (a) helicopter flight phases; (b) helicopter operational and maintenance events; and (c) helicopter event-related and safety/efficiency flight profile measurements. This study\u27s resultant HFOQA analysis software has direct application to multifaceted helicopter operations (Emergency Medical Services [EMS], sightseeing, military, and others), and, in fact, has been utilized by an offshore helicopter operator in its daily operations

History’s Slowest Digital Transformation: The Long Road to Flight Data Monitoring

Flight data monitoring (FDM) began in the flight test community in 1939 and entered the airline industry in 1974. In the 48 years since, however, very few operators have chosen to adopt this practice, which has shown clear safety benefits where it has found acceptance. While technical issues have created some obstacles, cultural issues have proven the greatest hindrance to wider FDM adoption. These cultural issues originated in the traits associated with pilots’ personalities, especially distrust of the regulators and operators who would administer flight data analysis programs (FDAP) that used FDM information. U.S. regulators have relied on voluntary adoption, rather than regulatory mandates, to increase FDM participation, emphasizing the collective benefits of FDAP outputs in increasing the safety of flight for operators using that information. Leadership by both experienced and new employees, as well as regulators and other industry stakeholders, will best serve to increase FDM participation until it becomes ubiquitous

Application of an AIS to the problem of through life health management of remotely piloted aircraft

The operation of RPAS includes a cognitive problem for the operators(Pilots, maintainers, ,managers, and the wider organization) to effectively maintain their situational awareness of the aircraft and predict its health state. This has a large impact on their ability to successfully identify faults and manage systems during operations. To overcome these system deficiencies an asset health management system that integrates more cognitive abilities to aid situational awareness could prove beneficial. This paper outlines an artificial immune system (AIS) approach that could meet these challenges and an experimental method within which to evaluate it

Система підготовки екіпажу до дій в особливих випадках польоту

Робота публікується згідно наказу ректора від 27.05.2021 р. №311/од "Про розміщення кваліфікаційних робіт вищої освіти в репозиторії НАУ". Керівник дипломної роботи: професор кафедри авіоніки, Грищенко Юрій ВіталійовичAs we can see, flight safety standards are improving every year, but despite the best efforts of engineers, psychologists and scientists around the world, there is still a chance of an emergency during the flight due to the technical condition of the aircraft or human factor, so the crew must always be ready for various challenges to ensure its own safety and the safety of passengers. This topic is quite relevant, as it will allow to analyze the shortcomings of the crew training system in special flight cases and will allow to implement improvements in flight safety standards as soon as possible.Як бачимо, стандарти безпеки польотів з кожним роком покращуються, але незважаючи на всі зусилля інженерів, психологів і вчених у всьому світі, існує ще ймовірність аварійної ситуації під час польоту через технічний стан літака або людський фактор, тому екіпаж повинен бути завжди готовий до різних викликів, щоб забезпечити своє безпеки та безпеки пасажирів. Ця тема є досить актуальною, оскільки дозволить проаналізувати недоліки системи підготовки екіпажу в особливих випадках польоту та дозволить реалізувати покращення стандартів безпеки польотів якнайшвидше

The Pilot Proficiency Audit of Knowledge, Skills, and Abilities: Adding to the Air Carrier Safety Toolbox

The correlation between air carrier pilot performance and age, total flight time, total airline flight time, and time in current position has not been fully documented. This study observed 62 F/Os in line operations and graded 74 knowledge, skills, and abilities performance variables, utilizing a five-point Likert scale. Knowledge items scored slightly below the referent, with no improvement over time; skills improved with both flight time and years of service; and abilities decline markedly across all independent variables. Changes to pilot training syllabi and techniques, as well as hiring practices, may be indicated. Integrating Pilot Proficiency Audit data into existing LOSA, AQP, ASAP, and FOQA programs can provide a more robust air carrier safety program