운항데이터 분석을 통한 롱플레어 이벤트 방지 연구
Research on Preventing Long Flare Events Through Flight Data Analysis
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초록
지난 수십 년 동안 항공 안전이 크게 향상되었다. 1960년대 이후 사고율과 치명적인 사고율이 크게 감소했으며, 이는 기술 발전, 항공기 시스템의 개선, 그리고 엄격한 안전 규제 덕분이다. 최근에는 데이터 분석을 통한 예측적 안전 관리에 중점을 두고 있다. 이는 항공 데이터를 분석하여 이상 패턴과 잠재적 위험을 감지하는 것이다. 본 연구는 활주로 이탈과 관련된 착륙 이벤트인 롱 플레어에 중점을 두고 있다. 이를 위해 실제 운항데이터 (Quick Access Recorders: QAR)를 바탕으로 분산분석, 상관관계 분석, 회귀분석 등의 통계적 방법을 사용하여 롱 플레어 이벤트에 영향을 미치는 주요 요소를 분석하였고 착륙 접근 과정에서 중요한 관리 구간을 식별하고, 착륙 이벤트 예방을 위한 변수들의 영향력을 분석함으로써 비정상적인 접근 패턴을 식별하고 관리하여 안전 운항에 기여하고자 한다.
Abstract
Over the past few decades, aviation safety has significantly improved. Since the 1960s, the rates of accidents and fatal accidents have greatly decreased, thanks to technological advancements, improved aircraft systems, and stringent safety regulations. Recently, there has been a focus on predictive safety management through data analysis, which involves analyzing aviation data to detect abnormal patterns and potential hazards. This study focuses on the long flare, a landing event associated with runway excursions. We analyzed various flight parameters using data from Quick Access Recorders (QAR). Statistical methods such as variance analysis, correlation analysis, and regression analysis were employed to identify the key factors influencing long flare events. The main results show that pitch angle, vertical speed, computed airspeed, and ground speed significantly impact the occurrence of long flares. Environmental factors such as wind speed and direction also play important roles. Precise control and speed management during landing are essential for safety. In conclusion, identifying early warning signals is crucial for accident prevention. This study provides insights into the key factors affecting long flare events, offering a new approach to managing near-event flights and improving pilot training programs. This approach can be effectively integrated with Evidence-Based Training (EBT) programs to enhance flight safety.
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