Failure Model Determination in a Knowledge-Based Control System

A technique for determining the most probable failure state of a restructurable control system is presented. The approach is to build a knowledge base that contains and makes use of inference mechanisms to deduce the most likely failures given the symptoms. The analysis is first carried out in a local sense, where only probabilistic information and causality are used to generate failure models, then in a global sense, where the models are grouped and heuristics are used to prune the number of candidate models. Procedures are illustrated using failure patterns of a generic database as well as a fault scenario for a hypothetical helicopter flight control system. It is concluded that the methods are potentially capable of handling generic failures and thus are useful in truly restructable control systems.