Nuclear Power Reactors (NPRs) are large in scale and complex, so the information from local fields is excessive, and therefore plant operators cannot properly process it. When a plant malfunction occurs, there are data influxes, so the cause of the malfunction cannot be easily and promptly identified. A typical NPR may have around 2,000 alarms in the Main Control Room (MCR) in addition to the display of analog data [1--4]. During plant transients, mode changes and component trips, hundreds of alarms may be activated in a short time. Hence, to increase the plant safety, this article proposes the operator support systems based on neurofuzzy assisted alarming and diagnosis system. Throughout this framework the neurofuzzy fault diagnosis system is employed to fault diagnosis of nuclear reactors. To overcome the weak points of both linguistic and neuro learning based approaches an integration between the neural networks and fuzzy logic
has been applied by which the integrated system will inherit the strengths of both approaches.