Within the last decade, progress toward developing a practical electrically-powered transport aircraft has accelerated with improvements in battery technologies and advanced algorithms to control and monitor safety critical processes. In addition, the focus has been placed on developing autonomous vehicles for intra-city short haul flights using vertical takeoff and landing (VTOL) aircraft in effort to facilitate NASA's new Urban Air Mobility (UAM) program. This requires precise knowledge of the current health state of the entire vehicle, and the ability to estimate and predict the state of health over time to make these short missions robust and safe. Online estimation methodologies that reason about faults and component degradation are critical to the safety of the aircraft, its occupants, and the success of its mission. More importantly, it is necessary to understand how degradation at the component level affect the performance of the overall system. We hypothesize that utilizing a holistic approach to system health management will result in a robust framework which can be applied to a number of safety-critical systems. We study the effects of multiple degrading components in the power-train system of the DJI Mavic Pro quad-copter on the entire system and provide a framework for system-level prognostics under multiple sources of degradation and uncertainty.
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