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The take-off of an aircraft is one of the most dangerous flight phases, as failures and adverse environmental conditions can lead to a catastrophe. Should abnormal events occur during the roll phase, the crew or the flight computer has to make the decision if the take-off can be safely rejected and the aircraft can brake and come to a standstill on the runway, or if the take-off has to be attempted in any case. This decision has to be made instantaneously upon the estimate of the current state of the aircraft, using available sensor data under noise and potential failure conditions. In order to do so, at any time during the roll phase, a prediction has to be made, if the aircraft can come to a safe stop within the boundaries of the runway. In this paper, we formulate this decision making task as an online prognostics problem and develop a model-based architecture that allows us to perform a probabilistic prediction of the aircraft’s braking distance given the current aircraft state. We are using particle filter and Monte-Carlo based prediction algorithms. Because this task has to be performed in real-time on the on-board flight computer, computational resources are very restricted. We therefore propose several models of increasing fidelity, which have substantially different computational footprints and exhibit different levels of accuracy that can impose severe restrictions on the handling of uncertainties and on the failures that can be modeled.

Safety Monitoring and Prognostics for Automatic Aircraft Take-off