Explaining Aviation Safety Incidents Using Deep Temporal Multiple Instance Learning

Although aviation accidents are rare, safety incidents occur more frequently and require a careful analysis to detect and mitigate risks in a timely manner. Analyzing safety incidents using operational data and producing event-based explanations is invaluable to airline companies as well as to governing organizations such as the Federal Aviation Administration (FAA) in the United States. However, this task is challenging because of the complexity involved in mining multi-dimensional heterogeneous time series data, the lack of time-step-wise annotation of events in a flight, and the lack of scalable tools to perform analysis over a large number of events. In this work, we propose a precursor mining algorithm that identifies events in the multidimensional time series that are correlated with the safety incident. Precursors are valuable to systems health and safety monitoring and in explaining and forecasting safety incidents. Current methods suffer from poor scalability to high dimensional time series data and are inefficient in capturing temporal behavior. We propose an approach by combining multiple-instance learning (MIL) and deep recurrent neural networks (DRNN) to take advantage of MIL's ability to learn using weakly supervised data and DRNN's ability to model temporal behavior. We describe the algorithm, the data, the intuition behind taking a MIL approach, and a comparative analysis of the proposed algorithm with baseline models. We also discuss the application to a real-world aviation safety problem using data from a commercial airline company and discuss the model's abilities and shortcomings, with some final remarks about possible deployment directions

Aerospace medicine and biology: A continuing bibliography with indexes (supplement 349)

This bibliography lists 149 reports, articles and other documents introduced into the NASA Scientific and Technical Information System during April, 1991. Subject coverage includes: aerospace medicine and psychology, life support systems and controlled environments, safety equipment, exobiology and extraterrestrial life, and flight crew behavior and performance

FDM Machine Learning: An investigation into the utility of neural networks as a predictive analytic tool for go around decision making

Loss-of-control events during the approach-to-landing phase of flight account for a large share of fatalities in general aviation. During this critical transition towards the runway it is essential that an aircraft is stabilized. Pilot discretion and judgment is used to determine if an aircraft is suited to either land or go-around, based on an assessment of approach conditions. Many landing incidents and accidents could be prevented with improved go-around decisions. The purpose of this research is to investigate the utility of neural networks in modeling those decisions using historic aircraft flight data. Data collected from nearly 2,000 hours of training flights is used to create a snapshot of an aircraft’s flight parameters at 200’ above ground level on approach. Each approach is then categorized as a landing event or go-around; using this data set a neural network is trained to predict approach outcomes. The network is then tested with an unfamiliar data set. Low error rates with testing data indicate the success of the network in predicting go-around events

Intelligent flight control systems

The capabilities of flight control systems can be enhanced by designing them to emulate functions of natural intelligence. Intelligent control functions fall in three categories. Declarative actions involve decision-making, providing models for system monitoring, goal planning, and system/scenario identification. Procedural actions concern skilled behavior and have parallels in guidance, navigation, and adaptation. Reflexive actions are spontaneous, inner-loop responses for control and estimation. Intelligent flight control systems learn knowledge of the aircraft and its mission and adapt to changes in the flight environment. Cognitive models form an efficient basis for integrating 'outer-loop/inner-loop' control functions and for developing robust parallel-processing algorithms

Multi-sensor data fusion techniques for RPAS detect, track and avoid

Accurate and robust tracking of objects is of growing interest amongst the computer vision scientific community. The ability of a multi-sensor system to detect and track objects, and accurately predict their future trajectory is critical in the context of mission- and safety-critical applications. Remotely Piloted Aircraft System (RPAS) are currently not equipped to routinely access all classes of airspace since certified Detect-and-Avoid (DAA) systems are yet to be developed. Such capabilities can be achieved by incorporating both cooperative and non-cooperative DAA functions, as well as providing enhanced communications, navigation and surveillance (CNS) services. DAA is highly dependent on the performance of CNS systems for Detection, Tacking and avoiding (DTA) tasks and maneuvers. In order to perform an effective detection of objects, a number of high performance, reliable and accurate avionics sensors and systems are adopted including non-cooperative sensors (visual and thermal cameras, Laser radar (LIDAR) and acoustic sensors) and cooperative systems (Automatic Dependent Surveillance-Broadcast (ADS-B) and Traffic Collision Avoidance System (TCAS)). In this paper the sensors and system information candidates are fully exploited in a Multi-Sensor Data Fusion (MSDF) architecture. An Unscented Kalman Filter (UKF) and a more advanced Particle Filter (PF) are adopted to estimate the state vector of the objects based for maneuvering and non-maneuvering DTA tasks. Furthermore, an artificial neural network is conceptualised/adopted to exploit the use of statistical learning methods, which acts to combined information obtained from the UKF and PF. After describing the MSDF architecture, the key mathematical models for data fusion are presented. Conceptual studies are carried out on visual and thermal image fusion architectures

Vision Science and Technology at NASA: Results of a Workshop

A broad review is given of vision science and technology within NASA. The subject is defined and its applications in both NASA and the nation at large are noted. A survey of current NASA efforts is given, noting strengths and weaknesses of the NASA program

A multi-level predictive methodology for terminal area air traffic flow

Over the past few decades, the air transportation system has grown significantly. In particular, the number of passengers using air transportation has greatly increased. As the demand for air travel expands, airport departure/arrival demand almost reaches its capacity. In consequence, the level of delays increases since the system capacity cannot manage the increased demand. With this trend, the national airspace system (NAS) will be saturated, and the congestion at the airport will become even more severe. As a result of congestion, a considerable number of flights experience delays. According to the Bureau of Transportation Statistics (BTS), over 1 million flights are operated in a year, and about twenty percent of all scheduled commercial flights are delayed more than 15 minutes. These delays cost billions of dollars annually for airlines, passengers, and the US economy. Therefore, this study seeks to find out why the delays occur and to analyze patterns in which the delays occurred. Analysis of airport operations generally falls into a macro or micro perspective. At the macro point of view, very few details are considered, and delays are aggregated at the airport level. Especially, shortfalls in airport capacity and a capacity-demand imbalance are the primary causes of delays in this respect. In the micro perspective, each aircraft is modeled individually, and the causes of delays are reproduced as precisely as possible. Micro reasons for air traffic delays include inclement weather, mechanics problems, operation issues. In this regard, this research proposes a methodology that can efficiently and practically predict macro and micro-level air traffic flow in the terminal area. For a macro-level analysis of delays, artificial neural networks models are proposed to predict the hourly airport capacity. Multi-layer perceptron (MLP), recurrent neural network (RNN), and long short-term memory (LSTM) are trained with historical weather and airport capacity data of Hartsfield-Jackson Atlanta airport (ATL). In the performance evaluation, the models have presented decent predictive performance and successfully predicted the test data as well as the training data. On the other hand, Random Forests and AdaBoost are implemented in the micro-level modeling of the air traffic. The micro-level models trained with on-time flight performance data and corresponding weather data focus on a classification of the individual flight delays. The model provides interpretability and imbalanced data handling while the accuracy is as good as the existing methods. Lastly, the predictive model for individual flight delays is refined using the cost-proportionate rejection sampling (costing) method. Along with the integration of the costing method, general machine learning algorithms have been converted to cost-sensitive classifiers. The cost-sensitive classifiers were able to account for asymmetric misclassification costs without losing their diagnostic functionality as binary classifiers. This study presents a data-driven approach to air traffic flow management that can effectively utilize air traffic data accumulated over decades. Through data analysis from the macro and micro perspective, an integrated methodology for terminal air traffic flow prediction is provided. An accurate prediction of the airport capacity and individual flight delays will assist stakeholders in taking more informed decisions.Ph.D