Anomaly Detection via Topological Feature Map (ADTM)
Our system, Anomaly Detection via Topological feature Map (ADTM), uses a combination of case-based reasoning (CBR), neural-network based clustering, and supervised classification techniques to predict, detect, and explain anomalies, and guide users in implementing effective mitigations.
Autonomous Fault Diagnosis and Recovery for Advanced Life Support Systems
Stottler Henke developed a diagnosis and recovery planning system for the Intelligent Systems Branch, Automation and Robotics Division at Johnson Space Center. This work focused on the deliberative elements of our health maintenance architecture for subsystems of advanced life support (ALS) systems.
Autonomous Spacecraft Subsystem Fault Detection, Isolation, Diagnosis, and Recovery
Our MAESTRO (Management through intelligent, AdaptivE, autonomouS, faulT identification and diagnosis, Reconfiguration/replanning/rescheduling Optimization) architecture has been applied to the xEMU Portable Life Support System (PLSS), Gateway PPE Electrical Power System (EPS), MSU Cubesat EPSs, Mars Transit Vehicle, and International Space Station (ISS) experiment.
Satellite Unknown Anomaly Resolution Using Cased-Based and Model-Based Reasoning
The UARS employs both case-based reasoning (CBR) and model-based reasoning (MBR) in concert to diagnose and resolve unknown anomalies in engineering telemetry from geosynchronous communication satellites.