A KNOWLEDGE-BASED APPROACH TO INSTRUMENT FAULT-DETECTION AND ISOLATION

A knowledge-based instrument fault detection and isolation (IFDI) tech nique is proposed and described. It is based on the ''duplication'' of measurement devices by means of suitable mathematical relationships a nd is implemented on an expert system. The latter allows a mathematica l model to be substituted by integrating qualitative models with empir ical knowledge, thereby reducing overall computer effort without any c orresponding decrease-in diagnostic capabilities. This technique is pa rticularly efficient when applied to detect and isolate faults in meas urement systems integrated in control architectures. It is applied to an automatic measurement station for induction motor testing to illust rate its characteristics and performance. The experimental results are also reported, illustrating IFDI performance mainly in terms of promp tness, sensitivity, and selectivity.