A Study Of Factors Contributing To Self-reported Anomalies In Civil Aviation

A study investigating what factors are present leading to pilots submitting voluntary anomaly reports regarding their flight performance was conducted. The study employed statistical methods, text mining, clustering, and dimensional reduction techniques in an effort to determine relationships between factors and anomalies. A review of the literature was conducted to determine what factors are contributing to these anomalous incidents, as well as what research exists on human error, its causes, and its management. Data from the NASA Aviation Safety Reporting System (ASRS) was analyzed using traditional statistical methods such as frequencies and multinomial logistic regression. Recently formalized approaches in text mining such as Knowledge Based Discovery (KBD) and Literature Based Discovery (LBD) were employed to create associations between factors and anomalies. These methods were also used to generate predictive models. Finally, advances in dimensional reduction techniques identified concepts or keywords within records, thus creating a framework for an unsupervised document classification system. Findings from this study reinforced established views on contributing factors to civil aviation anomalies. New associations between previously unrelated factors and conditions were also found. Dimensionality reduction also demonstrated the possibility of identifying salient factors from unstructured text records, and was able to classify these records using these identified features

Aircraft Damage Classification by using Machine Learning Methods

Safety is the most significant factor that affected incidents (non-fatal) and accidents (fatal) in civil aviation history related to scheduled flights. In the history of scheduled flights, the total incident and accident number until 2022 is 1988. In this study, 677 of them are taken into consideration since 11 September 2001. The purpose of this study is to reveal the factors that can classify type of aircraft damages such as none, minor and substantial in all-time incidents and accidents. ML algorithms with different configurations are applied for the classification process. The RFE and PCA are used to find the most important factors that are effective on the classification. Four components are found with PCA as zone, weather, time, and history. The results of multinomial logistic regression and ANNs showed that the most important 5 features are latitude, wind speed, wind direction, year, and longitude to classify aircraft damage. Then, temperature, total number of injury passenger, and month factors comes with more than 50% importance. The managerial implication of the study shows that as time passes the number of substantial accidents has decreased due to increasing level of safety precautions in civil aviation

Aerospace medicine and biology: A continuing bibliography with indexes (supplement 359)

This bibliography lists 164 reports, articles and other documents introduced into the NASA Scientific and Technical Information System during Jan. 1992. Subject coverage includes: aerospace medicine and physiology, life support systems and man/system technology, protective clothing, exobiology and extraterrestrial life, planetary biology, and flight crew behavior and performance

Mining Aircraft Telemetry Data With Evolutionary Algorithms

The Ganged Phased Array Radar - Risk Mitigation System (GPAR-RMS) was a mobile ground-based sense-and-avoid system for Unmanned Aircraft System (UAS) operations developed by the University of North Dakota. GPAR-RMS detected proximate aircraft with various sensor systems, including a 2D radar and an Automatic Dependent Surveillance - Broadcast (ADS-B) receiver. Information about those aircraft was then displayed to UAS operators via visualization software developed by the University of North Dakota. The Risk Mitigation (RM) subsystem for GPAR-RMS was designed to estimate the current risk of midair collision, between the Unmanned Aircraft (UA) and a General Aviation (GA) aircraft flying under Visual Flight Rules (VFR) in the surrounding airspace, for UAS operations in Class E airspace (i.e. below 18,000 feet MSL). However, accurate probabilistic models for the behavior of pilots of GA aircraft flying under VFR in Class E airspace were needed before the RM subsystem could be implemented. In this dissertation the author presents the results of data mining an aircraft telemetry data set from a consecutive nine month period in 2011. This aircraft telemetry data set consisted of Flight Data Monitoring (FDM) data obtained from Garmin G1000 devices onboard every Cessna 172 in the University of North Dakota\u27s training fleet. Data from aircraft which were potentially within the controlled airspace surrounding controlled airports were excluded. Also, GA aircraft in the FDM data flying in Class E airspace were assumed to be flying under VFR, which is usually a valid assumption. Complex subpaths were discovered from the aircraft telemetry data set using a novel application of an ant colony algorithm. Then, probabilistic models were data mined from those subpaths using extensions of the Genetic K-Means (GKA) and Expectation- Maximization (EM) algorithms. The results obtained from the subpath discovery and data mining suggest a pilot flying a GA aircraft near to an uncontrolled airport will perform different maneuvers than a pilot flying a GA aircraft far from an uncontrolled airport, irrespective of the altitude of the GA aircraft. However, since only aircraft telemetry data from the University of North Dakota\u27s training fleet were data mined, these results are not likely to be applicable to GA aircraft operating in a non-training environment

Influence of meteorological phenomena on worldwide aircraft accidents in the period 1967-2010

This is the pre-peer reviewed version of the following article: Mazon, J., Rojas, J. I., Lozano, M., Pino, D., Prats, X. and Miglietta, M. M. (2017), Influence of meteorological phenomena on worldwide aircraft accidents, 1967–2010. Met. Apps. doi:10.1002/met.1686, which has been published in final form at http://onlinelibrary.wiley.com/doi/10.1002/met.1686/abstract. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving.Based on the information available in databases from relevant national and international organizations from 1967 to 2010, an Aviation Weather Accidents Database (AWAD) was built. According to the AWAD, the weather is the primary cause in a growing percentage of annual aircraft accidents: from about 40% in 1967 to almost 50% in 2010. While the absolute number of fatalities and injured people due to aircraft accidents has decreased significantly, the percentage of fatalities and injured people in accidents attributed to the weather shows a slight increase in the studied period. The influence of turbulence, clear air turbulence, wind shear, low visibility, rain, icing, snow and storms on aircraft accidents was analysed, considering the different phases of flight, the meteorological seasons of the year and the spatial distribution over four zones of the Earth. These zones were defined following meteorological and climatological criteria, instead of using the typical political criteria. A major part of the accidents and accidents attributed to the weather occur in latitudes between 12º and 38º in both hemispheres. It is concluded that actions aimed at reducing the risk associated with low visibility, rain and turbulence, in this order, should have priority to achieve the most significant improvements in air transport safety.Postprint (author's final draft

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

Human Error and Accident Causation Theories, Frameworks and Analytical Techniques: An Annotated Bibliography

Over the last several decades, humans have played a progressively more important causal role in aviation accidents as aircraft have become more [complex]. Consequently, a growing number of aviation organizations are tasking their safety personnel with developing safety programs to address the highly complex and often nebulous issue of human error. However, there is generally no “off-the-shelf” or standard approach for addressing human error in aviation. Indeed, recent years have seen a proliferation of human error frameworks and accident investigation schemes to the point where there now appears to be as many human error models as there are people interested in the topic. The purpose of the present document is to summarize research and technical articles that either directly present a specific human error or accident analysis system, or use error frameworks in analyzing human performance data within a specific context or task. The hope is that this review of the literature will provide practitioners with a starting point for identifying error analysis and accident investigation schemes that will best suit their individual or organizational needs

An Innovative Approach to Modeling Aviation Safety Incidents

Due to the complexity of aviation safety operations, the number of flight incidents continues to rise. The Aviation Safety Reporting System (ASRS) contains the largest collection of such incidents. Efficient and effective analysis of these incidents remains a challenge. This paper proposes a new approach to analyze aviation safety records using deep learning methods to improve incident classification. The proposed approach, CNN-LSTM, combines the characteristics of convolutional neural network (CNN) and long short-term memory (LSTM) neural network, and a distributed computing method to model aviation safety data. The five machine learning methods Logistic Regression, Naive Bayes, Random Forest, Support Vector Machine, Multi-layer Perceptron were used to compare with CNN-LSTM. The results show that CNN-LSTM model can significantly improve the accuracy rates of classification for aviation safety incident reports using Word2Vec. The distributed platform in Spark with clusters can make full use of computing resources when processing textual data from ASRS, reducing time-consumption greatly when compared with machine learning algorithms running on a standalone computer. Timely and accurate identification of causes of reported incidents is important. The results of this study demonstrate a new approach to improve both accuracy and efficiency in incident cause identification

Classifıcation of Survivor/Non-Survivor Passengers in Fatal Aviation Accidents: A Machine Learning Approach

The safety concept primarily examines the most fatal (resulting in dead passengers) accidents of aviation history in this study. The primary causes of most fatal accidents are; human, technical, and sabotage/terrorism factors. Although the aviation industry started with the first engine flight in 1903, the safety concept has been examined since the 1950s. The safety concept firstly examined the technical factors, and in the late 1970s, human factors started to analyze. Despite these primary causes, there have different factors that affect accidents. So, the study aims to determine the affecting factors of the most fatal accidents to classify the survivor/non-survivor passenger numbers. Logistic regression and discriminant analysis are used as multivariate statistical analyses to compare with the machine learning approaches showing the algorithms’ robustness. In this study, machine learning techniques have better performance than multivariate statistical methods in terms of accuracy, false-positive rate, and false-negative rate. In conclusion, the phase of flight, the primary cause, and total passenger numbers are determined as the most affected factors in machine learning and multivariate statistical models for classifying the accidents’ survivor/non-survivor passenger numbers. Keywords: Machine learning; primary causes; fatal aviation accidents; classification of survivor/non-survivor passengers; multivariate statistical analysis

Learning, technological competition and network structure in the aero-engine industry

This paper provides a novel contribution for specifying the role of demand for technological competition. The focus is on the analysis of the mechanisms of technological learning and spillovers occurring in different structures of networks of vertically-related industries. The paper offers a detailed and original empirical analysis of technological competition among suppliers and structure of the network of two vertically related-industries, namely the commercial jet and turboprop aero-engine and aircraft industries. Technological performances of actors are measured through measures of output of the technological activity.-