The Fault Diagnosis using Neural Networks for Nuclear Power Plants

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Nuclear power generations have been developed gradually since 1950. Nowadays, 440 nuclear power generations are taking charge of 16% of electric power production in the world. The most important factor to operate the nuclear power generations is safety. It is not easy way to control nuclear power generations with safety because nuclear power generations are very complicated systems. In the main control room of the nuclear power generations, about 4000 numbers of alarms and monitoring devices are equipped to handle the signals corresponding to operating equipments. Thus, operators have to deal with massive information and to grasp the situation immediately. If they could not achieve these task, then they should make big problem in the power generations Owing to too many variables, operators could be also in the uncontrolled situation. So in this paper, automatic systems to diagnose the fault are constructed using 2 steps neural networks. This diagnosis method is based on the pattern of the principal variables which could represent the type and severity of faults.