Anomaly Detection in Health and Status Telemetry Data

Northrop Grumman Electronic Systems, Space Systems Division Computer Science, 2003-04

Liaison(s): Craig Snow
Advisor(s): Robert Keller
Students(s): Erika Rice (PM), Daniel Marley, Gabriel Neer, Jesse Ruderman

Detection of anomalies in satellite health and status data requires real-time processing capabilities in order to reduce the ill effects of equipment malfunctions and other undesirable events. The team designed and implemented an extensible software architecture that enables anomalies to be detected and displayed in visual form. In addition to preset monitoring capabilities, our system provides learning capabilities based upon techniques from adaptive signal processing and adaptive resonance theory.