Theincreasingcomplexityofspacevehiclessuchassatellites,
andthecostreduction measures that have affected satellite operators are
increasingly driving the need for more autonomy in satellite diagnostics
and control systems. Current methods for detecting and correcting
anomalies onboard the spacecraft as well as on the ground are primarily
manual and labor intensive, and therefore, tend to be slow. Operators
inspect telemetry data to determine the current satellite health. They
use various statisticaltechniques andmodels, buttheanalysisandevaluation
ofthelargevolume of data still require extensive human intervention and
expertise that is prone to error. Furthermore, for spacecraft and most
of these satellites, there can be potentially unduly long delays in
round-trip communications between the ground station and the satellite.
In this context, it is desirable to have onboard fault-diagnosis system
that is capable of detecting, isolating, identifying or classifying
faults in the system
withouttheinvolvementandinterventionofoperators.Towardthisend,
theprinciple goal here is to improve the ef?ciency, accuracy, and
reliability of the trend analysis and diagnostics techniques through
utilization of intelligent-based and hybrid-based methodologi