Automated Discovery of Flight Track Anomalies

As new technologies are developed to handle the complexities of the Next Generation Air Transportation System (NextGen), it is increasingly important to address both current and future safety concerns along with the operational, environmental, and efficiency issues within the National Airspace System (NAS). In recent years, the Federal Aviation Administrations (FAA) safety offices have been researching ways to utilize the many safety databases maintained by the FAA, such as those involving flight recorders, radar tracks, weather, and many other high- volume sensors, in order to monitor this unique and complex system. Although a number of current technologies do monitor the frequency of known safety risks in the NAS, very few methods currently exist that are capable of analyzing large data repositories with the purpose of discovering new and previously unmonitored safety risks. While monitoring the frequency of known events in the NAS enables mitigation of already identified problems, a more proactive approach of finding unidentified issues still needs to be addressed. This is especially important in the proactive identification of new, emergent safety issues that may result from the planned introduction of advanced NextGen air traffic management technologies and procedures. Development of an automated tool that continuously evaluates the NAS to discover both events exhibiting flight characteristics indicative of safety-related concerns as well as operational anomalies will heighten the awareness of such situations in the aviation community and serve to increase the overall safety of the NAS. This paper discusses the extension of previous anomaly detection work to identify operationally significant flights within the highly complex airspace encompassing the New York area of operations, focusing on the major airports of Newark International (EWR), LaGuardia International (LGA), and John F. Kennedy International (JFK). In addition, flight traffic in the vicinity of Denver International (DEN) airport/airspace is also investigated to evaluate the impact on operations due to variances in seasonal weather and airport elevation. From our previous research, subject matter experts determined that some of the identified anomalies were significant, but could not reach conclusive findings without additional supportive data. To advance this research further, causal examination using domain experts is continued along with the integration of air traffic control (ATC) voice data to shed much needed insight into resolving which flight characteristic(s) may be impacting an aircraft's unusual profile. Once a flight characteristic is identified, it could be included in a list of potential safety precursors. This paper also describes a process that has been developed and implemented to automatically identify and produce daily reports on flights of interest from the previous day

Literature review of machine learning techniques to analyse flight data

This paper analyses the increasing trend of using modern machine learning technologies to analyze flight data efficiently. Flight data offers an important insight into the operations of an aircraft. This paper reviews the research undertaken so far on the use of Machine Learning techniques for the analyses of flight data by evaluating various anomaly detection algorithms and the significance of feature selection in Flight Data Monitoring. These algorithms are compared to determine the best class of algorithms for highlighting significant flight anomalies. Furthermore, these algorithms are analyzed for various flight data parameters to determine which class of algorithms is sensitive to continuous parameters and which is sensitive to discrete parameters of flight data. The paper also addresses the ability of each anomaly detection algorithm to be easily adaptable to different datasets and different phases of flight, including take-off and landing.peer-reviewe

Identifying Anomalies in past en-route Trajectories with Clustering and Anomaly Detection Methods

International audienceThis paper presents a framework to identify and characterise anomalies in past en-route Mode S trajectories. The technique builds upon two previous contributions introduced in 2018: it combines a trajectory-clustering method to obtain the main flows in an airspace with autoencoding artificial neural networks to perform anomaly detection in flown trajectories. The combination of these two well-known Machine Learning techniques (ML) provides a useful reading grid associating cluster analysis with quantified level of abnormality. The methodology is applied to a sector of the French Bordeaux Area Control Center (ACC) during its 385 hours of operation over seven months of ADS-B traffic. The results provide a good taxonomy of deconfliction measures and weather-related ATC actions. The application of this work is manyfold, ranging from safety studies estimating risks of midair collision, to complexity and workload assessments of traffic when a sector is operated, or to the constitution of a database of ATC actions ensuring aircraft separation. This database could be used to train further ML techniques aimed at improving the state of the art of deconfliction algorithms

Some Challenges in the Design of Human-Automation Interaction for Safety-Critical Systems

Increasing amounts of automation are being introduced to safety-critical domains. While the introduction of automation has led to an overall increase in reliability and improved safety, it has also introduced a class of failure modes, and new challenges in risk assessment for the new systems, particularly in the assessment of rare events resulting from complex inter-related factors. Designing successful human-automation systems is challenging, and the challenges go beyond good interface development (e.g., Roth, Malin, & Schreckenghost 1997; Christoffersen & Woods, 2002). Human-automation design is particularly challenging when the underlying automation technology generates behavior that is difficult for the user to anticipate or understand. These challenges have been recognized in several safety-critical domains, and have resulted in increased efforts to develop training, procedures, regulations and guidance material (CAST, 2008, IAEA, 2001, FAA, 2013, ICAO, 2012). This paper points to the continuing need for new methods to describe and characterize the operational environment within which new automation concepts are being presented. We will describe challenges to the successful development and evaluation of human-automation systems in safety-critical domains, and describe some approaches that could be used to address these challenges. We will draw from experience with the aviation, spaceflight and nuclear power domains

Investigating the impacts of COVID-19 on aviation safety based on occurrences captured through flight data monitoring

The COVID-19 pandemic led to growing concerns about pilots’ proficiency due to the significant decrease in flight operations. The objective of this research is to provide a proactive approach to mitigate potential risks in flight operations associated with the impact of the COVID-19 pandemic using flight data monitoring (FDM). The results demonstrated significant associations between the pandemic impacts and FDM exceedance categories, flight phases and fleets. Manual flying skill decay, lack of practice effects on use of standard operating procedures and knowledge of flight deck automation should be considered by airlines when preparing for the return to normal operations. An FDM Programme allows prediction of the probability and severity of occurrences for developing an effective SMS within an airline. To mitigate the impacts of the pandemic, tailored training sessions must be implemented, and airlines should strive to avoid additional optional procedures where practicable.Higher Education Innovation Fund (HEIF 2020-2021

Providing Metrics-Based Results To Student Pilots For Critical Phases Of General Aviation Flights

This work details the development of the Critical Phase Analysis Tool (CPAT), a tool for analyzing and grading the quality of approach and landing phases of flight for the National General Aviation Flight Information Database (NGAFID). General Aviation (GA) accounts for the highest accident rates in Civil Aviation, and the approach and landing phases are when a majority of these accidents occur. Since GA aircraft typically lack most of the sophisticated technology that exists within Commercial Aviation, detecting phases of flight can be difficult. Moreover, because of the high variability in GA operations and abilities of the pilot, detecting unsafe flight practices is also not trivial. This thesis details the usefulness of an event-driven approach in analyzing the quality and risk level of an approach and landing. In particular, the application uses several parameters from a flight data recorder (FDR) to detect the phases of flight, detect any safety exceedances during the phases, and assign a metrics-based grade based on the accrued number of risk levels. The goal of this work is to improve the post-flight debriefing process for student pilots and Certified Flight Instructors (CFI) by augmenting the currently limited feedback with metrics and visualizations. By improving the feedback available to students, it is believed that it will help to correct unsafe flying habits quicker, which will also help reduce the GA accident rates in the long-term. The data was collected from a Garmin G1000 FDR glass cockpit display on a Cessna C172 fleet. The developed application is able to successfully detect go-arounds, touch-and-goes, and full-stop landings as either stable or unstable with an accuracy of 98.16%. The CPAT can be used to provide post-flight statistics and user-friendly graphs for educational purposes. It is capable of assisting both new and experienced pilots for the safety of themselves, their organization, and GA as a whole

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

Using machine learning methods in airline flight data monitoring to generate new operational safety knowledge from existing data

The aim of this work is to investigate the possibility of using machine learning (ML) methods in order to generate novel, safety-relevant knowledge from existing flight data. Airlines routinely generate vast amounts of flight data from routine monitoring, but the concept of extracting safety knowledge from this data is still based on detecting exceedances of expert-defined thresholds. This system is conceptually unable to detect novel occurrences for which no such filters exist. ML techniques are able to close this gap. This paper first reviews the literature to select an appropriate ML method. A form of unsupervised learning called “Local Outlier Probability” is selected. Next, an appropriate feature space is developed and implemented in the flight data monitoring system of a supporting airline to generate the dataset. This dataset is cleaned and the outlier calculation performed. The results are statistically analysed. Furthermore, the top outliers are reviewed by the airline’s review pilots in the same way as the traditional exceedance events. Last, the severities and safety relevance of both types of events are compared. This work successfully shows that the chosen approach is able to reduce the number of undetected safety-relevant occurrences by finding novel occurrence types which were undetected by a contemporary and mature flight data monitoring system. This research builds on recent literature by developing a novel method which can be scaled to work in an airline production environment with large datasets, as demonstrated by the efficient analysis of 1.2 million flights

Predição na aviação não regular escrita por Sónia Afonso

A crescente procura na aviação comercial em determinados picos operacionais, as avarias inusitadas e as operações charter Ad-hoc, fazem com que as empresas tenham necessidade de procura de aluguer de aeronaves a operadores de aviação. Por vezes deparam-se com dificuldades em encontrar aeronaves disponíveis para realizar o serviço ou simplesmente para que não haja custos acrescidos de aquisição, manutenção e de certo prejuízo em época baixa de operacionalidade, preferem não fazer aquisição de aeronaves de reserva capazes de fazer cobertura para todas as exigências operacionais, ou simplesmente, porque o seu tipo de negócio não abrange a inclusão de compra de aeronaves e preferem recorrência ao aluguer. As empresas de aviação não regular que conseguem colmatar esta carência necessitam ter uma preparação logística atempada. Assim, com este trabalho pretende-se fazer a predição da próxima tipologia operacional, do modelo de aeronave que será procurado e a consequente tripulação necessária para préstimo de serviço a bordo. A capacidade de preparação com antecedência na resposta operacional ao cliente, adequar o leque de oferta de aviões à procura e a existência de tripulação adequada às necessidades operacionais adjacentes, permite prestar um serviço de qualidade, melhoria da capacidade de resposta e melhoria de organização interna empresarial. Com esta dissertação pretende-se encontrar modelos de predição com auxílio a aprendizagem automática, aprendizagem automática com recurso a séries temporais e RNN – LSTM (Recurrent Neural Network - Long Short Memory Term), encontrando assim entre estes o modelo mais adequado a permitir fazer predição. Para a aplicação destas técnicas, foram utilizados os dados de gestão de tripulação e dados de planeamento de aeronaves, onde foi possível encontrar modelação adequada à predição da tipologia operacional, com ANN de classificação, para a modelação para determinação dos modelos de aeronaves, os melhores resultados obtidos foram com Árvores de Decisão de classificação e de tripulação, foi determinado com algumas dificuldades com ANN de regressão, a escolha recaiu na melhor performance.Growing demand in commercial aviation at certain time of operational peaks, facing maintenance problems as AOG (aircraft on ground) and the procurement for Ad-hoc charter operations, means that companies need to seek aircraft leasing from other aviation operators. Sometimes comercial airlines face some difficulties to find available aircrafts to perform their flights or simply to avoid additional costs of acquisition, maintenance and some losses in low peak operating times, instead they prefer not to purchase but rent aircraft capable of covering all operational requirements, or even simply because their type of business does not include the purchase of aircraft and prefer recurrence to rental. The non-scheduled aviation companies that can fill this gap need to have in advance a logistic preparation, thus, with this work, is intend to predict the next type of operation, the aircraft model to be searched and the convenient crew required for service on board. Pre-operational customer adequate response, matching the range of aircraft model supply to demand, and adequate number of crew to the consequent operational requirements, enables quality service, responsiveness improvement and higher internal business organization. This dissertation aims to find prediction models with the aid of machine learning, machine learning with time series and deep learning RNN - LSTM (Long Term Memory Term), finding amongest them the most suitable model to make predictions. To apply those techniques, crew management data and aircraft planning data were used, where it was possible to find appropriate modeling to predict the operational typology, with ANN classification, to predict the aircraft models, the best results were obtained with Decision Trees classification, and the necessary crew, it was determined with regression ANN, the choice was done having in mind the best performance of each model