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The tool developed through this work merges information from the electric propulsion system design phase with diagnostic tools. Information from the failure mode and effect analysis (FMEA) from the system design phase is embedded within a Bayesian network (BN). Each node in the network can represent either a fault, failure mode, root cause or effect, and the causal relationships between different elements are described through the connecting edges. This novel approach can help Fault Detection and Isolation (FDI), producing a framework capable of isolating the cause of sub-system level fault and degradation. This system: Identifies and quantifies the effects of the identified hazards, the severity and probability of their effects, their root cause, and the likelihood of each cause; Uses a Bayesian framework for fault detection and isolation (FDI); Based on the FDI output, estimates health of the faulty component and predicts the remaining useful life (RUL) by also performing uncertainty quantification (UQ); Identifies potential electric powertrain hazards and performs a functional hazard analysis (FHA) for unmanned aerial vehicles (UAVs)/Urban Air Mobility (UAM) vehicles. Despite being developed for and demonstrated with an application to an electric UAV, the methodology is generalized and can be implemented in other domains, ranging from manufacturing facilities to various autonomous vehicles.

The MAchine learning ESTimations for uRban Operations (MAESTRO) system is a novel approach that couples commodity sensors with advanced algorithms to provide real-time onboard local wind and kinematics estimations to a vehicle's guidance and navigation system. Sensors and computations are integrated in a novel way to predict local winds and promote safe operations in dynamic urban regions where Global Positioning System/Global Navigation Satellite System (GPS/GNSS) and other network communications may be unavailable or are difficult to obtain when surrounded by tall buildings due to multi-path reflections and signal diffusion. The system can be implemented onboard an Unmanned Aerial Systems (UAS) and once airborne, the system does not require communication with an external data source or the GPS/GNSS. Estimations of the local winds (speed and direction) are created using inputs from onboard sensors that scan the local building environment. This information can then be used by the onboard guidance and navigation system to determine safe and energy-efficient trajectories for operations in urban and suburban settings. The technology is robust to dynamic environments, input noise, missing data, and other uncertainties, and has been demonstrated successfully in lab experiments and computer simulations.

The traditional scissor spring design for hand controllers has been improved upon with a circumferential spring controller mechanism that facilitates easy customization, enhanced durability, and optimum controller feedback. These advantages are partially facilitated by locating the spring to the outside of the mechanism which allows for easier spring replacement to adjust the deflection force or for maintenance. The new mechanism is comprised of two rounded blades, or cams, that pivot forward and back under operation and meet to form a circle. An expansion spring is looped around the blade perimeter and resides in a channel, providing the restoring force that returns the control stick to a neutral position. Due to the use of a longer circumferential spring, the proportion of spring expansion is smaller for a given distance of deflection, so the forces associated with the deflection remain on a more linear portion of the force deflection curve. The Circumferential Scissor Spring for Controllers is at technology readiness level (TRL) 8 (actual system completed and flight qualified through test and demonstration) and is available for patent licensing. Please note that NASA does not manufacture products itself for commercial sale.