Autonomous Unmanned Aerial Vehicles (UAVs) operate under uncertain environmental conditions and can have to face unexpected obstacles, weather changes and sensor or hardware/software component failures. In such situations, the UAV must be able to detect and locate the failure and to take adequate recovery actions. In this paper, we focus on the Health Management of the system depending on the context of the mission. The task of this Health Management is to monitor the status of the system components based on observations from sensors and appearance contexts, and it is designed by means of Bayesian Networks arising from the Failure Mode and Effects Analysis. We jointly introduce a framework to generate embedded software and hardware implementations for online and real-time observations, which are demonstrated on a Hybrid CPU/FPGA Zynq platform.