Diagnosis of Abnormal Timing for a Sequence Control System : Reducing Methods of the Amount of Calculation for a Reverse Problem in Model Based Reasoning

Abstract

Although many diagnostic expert systems have been developed, only a few of them became of practical use mainly because of "knowledge acquisition bottleneck". We developed a plant diagnosis system using model based reasoning (MBR). The system contains a model of sequence control logic which can be automatically derived from design data, thus resolving the knowledge acquisition bottleneck. A plant has a huge number of sensors which are prone to failure. The system starts with abnormal observed values, traces through the sequence control model backward, and identifies failed sensors. The reverse tracing involves combinatorial explosion, especially the data is time series as in our system. In order to make the system practical, we proposed the following methods : pruning branches based on observation values, focusing a time interval of time series data, and selecting a physically plausible solution among logical candidates. Experimental results showed that the system could detect accurate solutions within reasonable time.