Safety enhancement is a major goal of the aviation industry owing to the predicted increase in air travel. There is also the need to prevent fatalities, increase reliability and reduce monetary costs suffered as a result of delays and accidents that still occur. Accidents today are complex as a result of many causal factors acting alone but more often as a combination with other contributing factors. In tackling this trend, proactive measures have been put in place to find hazardous combinations that occur during flights in order to mitigate them before accidents occur. Flight Anomaly Detection (AD) methods are aimed at highlighting abnormal occurrences of a flight, that are different from the norm. As an improvement on the current state-of-the-art method, previous works have proposed different AD techniques for detection of previously unknown flight risks such as component faults, aircraft operational inefficiencies and some abnormal crew behaviour. However, current AD methods individually have limitations that prevent them from detecting certain significant anomalies in flight data. This paper surveys current flight AD approaches, their strengths and limitations as well as brings to light the benefits of a hybrid AD method to extend previous work and find safety-critical events, particularly those related to abnormal crew activity: a class of events known to amount for a substantial number of accidents/incidents today. It also highlights another emerging AD application opportunity, its challenges and how AD is beneficial in addressing them.
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Anomaly Detection, Data Analytics, Flight Data Analysis
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