This paper presents a framework aimed at monitoring the behavior of aircraft in a given airspace. Trajectories that constitute typical operations are determined and learned using data driven methods. Standard procedures are used by air traffic controllers (ATC) to guide aircraft, ensure the safety of the airspace, and to maximize the runway occupancy. Even though standard procedures are used by ATC, the control of the aircraft remains with the pilots, leading to a large variability in the flight patterns observed. Two methods to identify typical operations and their variability from recorded radar tracks are presented. This knowledge base is then used to monitor the conformance of current operations against operations previously identified as typical. A tool called AirTrajectoryMiner is presented, aiming at monitoring the instantaneous health of the airspace, in real time. The airspace is “healthy” when all aircraft are flying according to the typical operations. A measure of complexity is introduced, measuring the conformance of current flight to typical flight patterns. When an aircraft does not conform, the complexity increases as more attention from ATC is required to ensure a safe separation between aircraft.

The worldwide civilian aviation system is one of the most complex dynamical systems created. Most modern commercial aircraft have onboard flight data recorders that record several hundred discrete and continuous parameters at approximately 1Hz for the entire duration of the flight. These data contain information about the flight control systems, actuators, engines, landing gear, avionics, and pilot commands. In this paper, recent advances in the development of a novel knowledge discovery process consisting of a suite of data mining techniques for identifying precursors to aviation safety incidents are discussed. The data mining techniques include scalable multiple-kernel learning for large-scale distributed anomaly detection. A novel multivariate time-series search algorithm is used to search for signatures of discovered anomalies on massive datasets. The process can identify operationally significant events due to environmental, mechanical, and human factors issues in the high-dimensional flight operations quality assurance data. All discovered anomalies are validated by a team of independent domain experts. This novel automated knowledge discovery process is aimed at complementing the state-of-the-art human-generated exceedance-based analysis that fails to discover previously unknown aviation safety incidents. In this paper, the discovery pipeline, the methods used, and some of the significant anomalies detected on real-world commercial aviation data are discussed.

이륙예보는 항공기가 출발하는 공항에서 이륙 시 예상되는 기상 상태를 예측하여 제공하는 항공기상 정보입니다. 주로 항공기의 안전한 이륙과 초기 상승 단계에 영향을 미칠 수 있는 바람, 시정, 구름, 강수, 뇌우 등의 요소를 중심으로 작성됩니다. 이 예보는 조종사와 항공관제사가 이륙 시의 기상 조건을 사전에 파악하고, 운항 계획이나 항로 선택, 연료 계산 등에 반영할 수 있도록 지원합니다. 또한 갑작스러운 기상 변화로 인한 이륙 지연이나 위험 상황을 예방하는 데에도 중요한 역할을 합니다. 이륙예보는 항공기상관측자료와 예보모델, 현장 분석을 종합하여 공항기상대에서 정기적으로 제공됩니다.

The world-wide aviation system is one of the most complex dynamical systems ever developed and is generating data at an extremely rapid rate. Most modern commercial aircraft record several hundred flight parameters including information from the guidance, navigation, and control systems, the avionics and propulsion systems, and the pilot inputs into the aircraft. These parameters may be continuous measurements or binary or categorical measurements recorded in one second intervals for the duration of the flight. Currently, most approaches to aviation safety are reactive, meaning that they are designed to react to an aviation safety incident or accident. Here, we discuss a novel approach based on the theory of multiple kernel learning to detect potential safety anomalies in very large data bases of discrete and continuous data from world-wide operations of commercial fleets. We pose a general anomaly detection problem which includes both discrete and continuous data streams, where we assume that the discrete streams have a causal influence on the continuous streams. We also assume that atypical sequence of events in the discrete streams can lead to off-nominal system performance. We discuss the application domain, novel algorithms, and also briefly discuss results on synthetic and real-world data sets. Our algorithm uncovers operationally significant events in high dimensional data streams in the aviation industry which are not detectable using state of the art methods.

항공기의 이륙을 지원하기 위해 매 시각 발표하는 국내 공항 이륙예보를 조회하는 서비스 (기상 : 풍향, 풍속, 기온, 기압을 조회하는 서비스)

항공기상관측은 항공기 안전운항에 필요한 기상정보를 생산·제공하기 위하여 공항 내 기상상태를 항공기상관측지침에 의해 측정하는 업무이며, 해당 관측은 당해 공항 내에서 사용하는 보고와 당해 공항 밖으로 통보되는 보고로 구분됩니다. 항공기상관측은 정해진 시간간격을 두고 실시하는 정시관측 및 특정 기준에 해당하는 변화가 있을 때 실시하는 특별관측, 관제기관 등의 요청 및 항공기 사고 시 실시하는 수시관측이 있습니다. - 요 소: 풍향, 풍속, 하늘상태, 시정, 강수량 등 - 지 점: 6개소 공항

Published at 12th AIAA Aviation Technology, Integration, and Operations (ATIO) Conference and 14th AIAA/ISSM 17 - 19 September 2012, Indianapolis, Indiana

항공기의 안전한 운항을 지원하기 위해 전국의 7개소 공항에서 공항기상관측을 실시합니다. 공항기상관측장비(Aerodrome Meteorological Observation System)는 항공기의 안전한 이 · 착륙에 필요한 활주로 부근의 기상실황을 실시간으로 관측하여 제공하는 항공기상관측의 기본 장비입니다. - 요 소: 기온, 강수, 바람, 기압, 습도, 적설, 구름, 시정, 일기현상 - 지 점: 7개소 공항 - 보유기간: 2005년 2월 ∼ 현재(지점별 상이함)

Modern aircraft are producing data at an unprecedented rate with hundreds of parameters being recorded on a second by second basis. The data can be used for studying the condition of the hardware systems of the aircraft and also for studying the complex interactions between the pilot and the aircraft. NASA is developing novel data mining algorithms to detect precursors to aviation safety incidents from these data sources. This talk will cover the theoretical aspects of the algorithms and practical aspects of implementing these techniques to study one of the most complex dynamical systems in the world: the national airspace.