Abstract
Flight safety event reasoning is an important task both in QAR (quick access recorder) data exceedence and statistical analyses. Situation awareness is booming recently as a very useful tool. It perceives, understands environments both inside and outside pilot's compartment, and then helps pilot think ahead about potential threats, risks toward targets (like operation during taking off or landing, etc.) and how to overcome them. QAR monitors the whole situation awareness process for subsequent analysis. Through QAR big data digging, we re-appear this process and evaluate pilot's performance. Risk situations/scenarios cause events for certain reasons. Correction methods for managing risk situations are obtained from opinion leaders and also included into our simulation system. This is a requisite step to introduce Artificial intelligence and machine learning algorithms later to fully automatically classify pilot behaviors into classes which will be studied later. We incorporate QAR as the situational awareness into Bowtie model to study the sequence of the event reasoning. Hard landing and long flare distance are studied through multiple flights QAR data and the achieve the Bowtie reasoning model.
Published Version
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