DEVELOPMENT OF AN ONLINE FUZZY EXPERT-SYSTEM FOR INTEGRATED ALARM PROCESSING IN NUCLEAR-POWER-PLANTS

Abstract

An on-line fuzzy expert system, called alarm filtering and diagnostic system (AFDS), was developed to provide the operator with clean alarm pictures and system-wide failure information during abnormal states through alarm filtering and diagnosis, In addition, it carries out alarm prognosis to warn the operator of process abnormalities. Clean alarm pictures that have no information overlapping are generated from multiple activated alarms at the alarm filtering stage. The meta rules for dynamic filtering were established on the basis of the alarm relationship network. In the case of alarm diagnosis, the relations between alarms and abnormal states are represented by means of fuzzy relations, and the compositional inference rule of fuzzy logic is utilized to infer abnormal states from the fuzzy relations. The AFDS offers the operator related operating procedures as well as diagnostic results, At the stage of alarm prognosis, the future values of some important critical safety parameters are predicted by means of Levinson algorithm selected from the comparative experiments, and the global trends of these parameters are estimated using data smoothing and fuzzy membership. This information enables early failure detection and is also used to supplement diagnostic symptoms. The AFDS has been validated and demonstrated using the full-scope simulator for Yonggwang Units 1, 2. From the validation results, it can be concluded that the AFDS is able to aid the operator to terminate early and mitigate plant abnormalities.