Hiawatha Spacecraft Autonomous System Health (SASH) Management System

Reliability of electronic subsystems is crucial tonbsp;autonomous missions. Initial effects of aging do not result in out-right failures, systems that experience intermittent issues without identification of the root cause are prone to sudden catastrophic failure due to the accumulated degradation of electronics. Nokomis will develop a prototype fault management /Electronics Health Monitoring system for diagnosing the Gateway spacecraft electronic subsystems for autonomous health management. While unoccupied, Gateway is at risk of unexpected events/nbsp;faults may require immediate response, and the ability to detect these conditions prior to loss of functionality enables mitigation or response actions.nbsp; The autonomous nature of the system allows for rapid response following the identification of aging of components likely to lead to failure or reduced functionality to implement mitigation solutions. The system will utilize unintended electromagnetic emissions that emanate from electronic devices to identify conditions such as operational states or conditions that lead to premature aging or sudden failure. Each subcomponent of a device has a unique emissions signature directly associated with the functional state of the device aiding in maintenance and mitigation measures. Metrics extracted by analysis algorithms can differentiate between baseline and stressed system states.nbsp; This effort will demonstrate the autonomous monitoring of critical subassembly health to identify possible critical failures or unsafe states, identify metrics in variation to categorize threat level and potential failure likelihood, communicate with control station to initiate protective behavior or allow for maintenance planning. Nokomis will develop and demonstrate a software module including algorithms approach to detect and categorize differences in emissions data to identify end of life risks to system electronics through the implementation of metric extraction and machine learning methods.