Research on deterioration evolution trend of primary loop piping in nuclear power plant based on fusion health index

Abstract

The perennial operation of nuclear power primary loop piping leads to deterioration of the piping and the potential risk of leakage. At present, there is no reliable technology to detect the leakage directly. This paper proposes a method to obtain the piping deterioration evolution trend based on the analysis of sets of data that can reflect real state of the piping. The specific method is to quantitatively construct a fusion health index model based on the improved Mahalanobis distance, which integrates the analytic hierarchy process and the entropy weight method. A prediction model combining convolutional neural networks and long short-term memory neural networks is established to make the trend analysis and prediction. The experimental results show that the method can better reflect the actual health state of the piping and effectively predict the deterioration evolution trend, which provides a specific reference value for ensuring the safe and stable operation of equipment.