EXPLORATION INSTITUTE LLC — National Aeronautics and Space Administration SBIR Phase I: H6

Description

nbsp;Spacecraft are incredibly complicated, interdependent, somewhat autonomous systems operating in dynamic environments. When parameter limits are exceedednbsp;or watchdog timers are not reset, spacecraft automatically enter a ldquo;saferdquo; mode where primary functionality is reduced or stopped completely. During safe mode the primary mission is put on hold while teams on the ground examine dozens to hundreds of parameters and compare them to archived historical data andnbsp;the spacecraft design to determine the root cause and what corrective action to take. This is a difficult and time consuming task.As human travel away from Earth, light travel time delays increase, increasing the time it takes for ground crews to respond to a safe event. A few astronauts will have a hard time replacing the brain power and experience of a team of experts on the ground. Therefore, a new approach is needednbsp;that augments existing capabilities to help the astronauts in key decision moments.This proposal will develop a fault protection monitor that will better understand current faults to help diagnostic efforts, with the ultimate goal of presentingnbsp;suggested corrective action and relevant collated information to onboard astronauts ornbsp;predicting upcoming faults and deciding on corrective action.This monitor enables a new kind of awareness within the spacecraft. This awareness is trained on past faults so that the sum total of understanding of the spacecraftrsquo;s test and development program is kept on board. This knowledge is stored in a low power neuromorphic chip whose architecture is modelled on the neural pathways of a neocortex to enable advanced pattern recognition and comparison to known patterns with minimal power consumption.Phase I will demonstrate the simplest use of this fault monitor system, which willnbsp;be to train a Spiked Neural Network (architected so it can work on currently available neuromorphic chips) to detect and report faults, learning from the environment and improvingnbsp;operations.