Current fault detection and correction techniques require either an operator-in-the-loop to identify and respond to on-board satellite anomalies or a hard-coded, rules-based fault response tree to algorithmically respond to triggers and perform corrections or escalate the fault. Both processes consume time and resources from system engineers or ground operators and are unlikely to identify novel patterns in onboard data and telemetry that signify a fault event. Orbit Logic proposes the Fault Learning Agent for Prediction, Protection, and Early Response (FLAPPER) solution, to be implemented as a pair of Specialized Autonomy Planning Agents (SAPAs) that expand our onboard Autonomous Planning System (APS) architecture to include machine learning capable of detecting, isolating, and mitigating anomalies in real- or near-real-time with minimal ground intervention. FLAPPER will analyze a subset of onboard spacecraft health and safety data and telemetry to train against and later autonomously detect and categorize spacecraft faults. Categorized faults will then be mapped to acceptable corrective action responses to be carried out autonomously or with expert oversight from operators given the inferred data. Transitioning the fault detection and correction capabilities to an autonomous and onboard application will benefit the mission’s success by reducing the time the spacecraft spends in anomalous states.
The FLAPPER solution has the potential to be applied to human exploration, deep space, un-manned exploration, and any additional spacecraft missions. As long as the spacecraft supports hardware/software, the spacecraft can benefit from the proposed Fault Management technology.
The FLAPPER solution has the potential for application aboard any non-NASA spacecraft, watercraft, factories, data centers, automobiles and even at home (A/C units, hot water heaters, etc.). Benefits can be seen in all machinery or monitored components to mitigate faults as well as warn the user if a system is degrading and may require maintenance.