Neuromorphic Spacecraft Fault Monitor

The goal of this work is to develop a low power machine learning anomaly detector. The low power comes from the type of machine learning (Spiking Neural Network (SNN)) and the hardware the neuromorphic anomaly detectornbsp;runs on. The ability to detect and react to anomalies in sensor readings on board resource constrained spacecraft is essential, now more than ever, as enormous satellite constellations are launched and humans push out again beyond low Earth orbit to the Moon and beyond.Spacecraft are autonomous systems operating in dynamic environments. When monitored parameters exceed limitsnbsp;or watchdog timers are not reset, spacecraft can automatically enter a #39;safe#39; 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 and the spacecraft design to determine the root cause and what corrective action to take. This is a difficult and time consuming task for humans, but can be accomplished faster, in real-time, by machine learning.As humans travel away from Earth, light travel time delays increase, lengthening the time it takes for ground crews to respond to a safe mode event. Thenbsp;few astronauts onboard will have a hard time replacing the brain power and experience of a team of experts on the ground. Therefore, a new approach is needed that augments existing capabilities to help the astronauts in key decision moments.We provide a new machine learning approach that recognizes nominal and faulty behavior, by learning during integration, test, and on-orbit checkout. This knowledge is stored and used for anomaly detection in a low power neuromorphic chip and continuously updated through regular operations. Anomalies are detected and context is provided in real-time, enabling both astronauts onboard, and ground crews on Earth, to take action and avoid potential faults or safe mode events.