Fault detection and diagnosis (FDD) provides safety alarms and diagnostic functions for a nuclear power plant (NPP), which comprises large and complex systems. NPP has a large number of parameters which make it difficult achieve FDD. Now many diagnosis methods have lack of better explanation for faults and quantitative analysis. Therefore, to overcome the “black box” of FDD based on data-driven methods, this paper adopts signed directed graph (SDG) in knowledge graph for FDD. It can express the cause and effect of accidents through knowledge maps. At same time, this paper uses correlation analysis to conduct a quantitative analysis between parameters and faults.
It this paper, SDG is used to explain the reason of faults. In order to quickly achieve FDD, this paper introduces a quantitative analysis method. It combines expert system and correlation analysis method to analyze the weight of each parameter. On this basis, matrix reasoning is used to achieve the FDD, and the reason is shown in SDG model inference. This paper takes loss of coolant accident as the case study, the case shows that the proposed method is superior to the conventional SDG method and can diagnose the faults timely.