A diagnosis method with functions of the anomaly cause identification and thefailure effect prediction has been developed in order to support the operators of a nuclear power plant. The method consists of a plant event data base and a diagnosis part. The former is a data base of nuclear power plant knowledge which contains relationships represented by binary values computed from measurable process data under anomalous plant conditions. The latter consists of a status predictor which provides

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... predicted binary values, a supervisor Which controls the diagnosis mode, an anomaly cause identifier which identifies the cause of an anomaly and a failure effect predictor which predicts the effects of component failure on the nuclear power plant. The following studies were made: (1) the procedure of concentrating plant knowledge; (2) the structure of the Plant event data base; (3) the configuration of the diagnosis part. The method was applied to the main subsystems of a PWR plant, and the performance was evaluated by use of a plant simulator. The results show that the method possesses high reliability, applicability to a real time system, the ability to track plant operation and applicability to plant transients. These are the most important requirements in putting the method to practical use.