A New Diagnosis Method Using Alarm Annunciation for Nuclear Power Plants.

Abstract

We discuss the methodology diversity for diagnosis reasoning in an autonomous operation system, and propose a new diagnosis method using an alarm annunciation system. The combination of annunciated alarms is expected to be peculiar to the anomalous phenomenon or accident. Moreover, as the state of affairs is developing, each appearance of the pattern is changing with time peculiarly to each anomaly or accident. The matter is utilized for the new diagnosis method. The patterns of annunciated alarms with progress of the events are prepared in advance under the condition of the anomalies or accidents by use of a plant simulator. The diagnostic reasoning can be done by comparing the obtained combination of annunciated alarms with the reference templates by using pattern matching method. On the other hand, we have another method, called COBWEB used for conceptual classification in cognitive science, to reason for diagnosis. We have carried out the experiments using the loop type LMFBR plant simulator to obtain the various combinations of annunciated alarms with progress of the events under the conditions of anomalies and accidents. The examined cases were related to the anomalies and accidents in the water/steam system of the LMFBR power plant. The simulation examination showed that each change of the pattern of annunciated alarms is specific to each anomaly or accident, and we have applied the pattern matching technique and COBWEB methods into the diagnostic reasoning using annunciated alarms. We could show the capability of these two methods to reason and focus among various candidates of causes of anomalies with gradually improved conviction degree as time passes from the occurrence of anomalies. It was also confirmed that these methods are effective in diagnosis reasoning as a way the operators are doing the diagnosis reasoning in existing plants.