A State-Based Approach to Trend Recognition and Failure Prediction for the Space Station Freedom

A state-based reasoning approach to trend recognition and failure prediction for the Altitude Determination, and Control System (ADCS) of the Space Station Freedom (SSF) is described. The problem domain is characterized by features (e.g., trends and impending failures) that develop over a variety of time spans, anywhere from several minutes to several years. Our state-based reasoning approach, coupled with intelligent data screening, allows features to be tracked as they develop in a time-dependent manner. That is, each state machine has the ability to encode a time frame for the feature it detects. As features are detected, they are recorded and can be used as input to other state machines, creating a hierarchical feature recognition scheme. Furthermore, each machine can operate independently of the others, allowing simultaneous tracking of features. State-based reasoning was implemented in the trend recognition and the prognostic modules of a prototype Space Station Freedom Maintenance and Diagnostic System (SSFMDS) developed at Honeywell's Systems and Research Center.