RPV Sealing Reliability Estimating Using a New Inconsistent Knowledge Fused Bayesian Network and Weighted Loss Function

Abstract

The sealing system performance of a nuclear reactor pressure vessel (RPV) under different working circumstances is vital to the safe operation of the whole nuclear process; however, the mechanism, and especially the relationship among highly related variables for it, has remained unclear. Therefore, a new inconsistent knowledge fused Bayesian network and weighted loss function (iBWL) method was proposed to identify key variables and estimate the reliability of an RPV sealing system. In this method, a new inconsistent knowledge fusion method was proposed to make good use of available priceless field knowledge by considering its reliability and inconsistency seriously. The key variables identified by the Bayesian network structure were then used by a new weighted loss function to estimate the reliability of the RPV sealing system by comprehensively quantifying the deviations of them from their corresponding expected values. It is not only the quantified reliability of RPV sealing that can provide solid information for its operation status but also the weighted loss function can provide clues for how to tune the corresponding parameters to make sure RPV operating has an acceptable status. The application performed on the simulation samples based on the RPV of Liaoning Hongyanhe Nuclear Power Plant and another two RPV units in service strongly proved the outstanding performance of this advanced iBWL method.