The safety of mechanical equipment is more important, it directly determines the safety of nuclear power plant operation, and even nuclear safety. So it is necessary to monitor the operating state of NPP system and mechanical equipment in real time by inspecting operating parameters. However, the key technology is real-time fault diagnosis of the mechanical equipment in NPP. Traditional fault diagnosis method based on analytic model is difficult to diagnose relevant and superimposed fault because of model error, disturbance and noise. This paper studies the application of fault diagnosis method based on BP neural network in NPP, and proposes an improved method for neural BP network method. For the feed-water system in the variable load operation process, we select the normal operation, the single feed-water valve fault, feed-water pump and feed-water valve superimposed fault as the analysis objects. One hundred points of data are extracted as BP algorithm training elements in these three processes averagely. The normal and abnormal conditions (including single fault and superimposed fault) can be accurately judged, but the single fault and superimposed failure would produce miscarriage of justice, about 2.4% of the single fault is diagnosed as superimposed fault, the diagnosis time delay is less than 1 second. These results meet the accuracy and real-time requirements. Then we study the application of support vector machine (SVM), which can make up for the deficiency of BP neural network. The results of this paper are useful for the real-time and reliable fault diagnosis of NPP.

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