AUTHOR=Guohua Wu , Diping Yuan , Jiyao Yin , Yiqing Xiao , Dongxu Ji 

TITLE=A Framework for Monitoring and Fault Diagnosis in Nuclear Power Plants Based on Signed Directed Graph Methods

JOURNAL=Frontiers in Energy Research

VOLUME=Volume 9 - 2021

YEAR=2021

URL=https://www.frontiersin.org/journals/energy-research/articles/10.3389/fenrg.2021.641545

DOI=10.3389/fenrg.2021.641545

ISSN=2296-598X

ABSTRACT=When nuclear power plants (NPPs) are in failure state, it may release radioactive substance into the environment. Thus, the safety of NPPs is put forward a high standard. Online Monitoring, fault detection and diagnosis (FDD) are significant for NPPs to help operator timely know the state of system and provide the online guidance. In the paper, in order to overcome the shortcoming of process monitoring in NPPs, five level threshold, qualitative trend analysis (QTA), and signed directed graph (SDG) inference are combined together to improve the veracity and sensitive of process monitoring and FDD. Firstly, three level threshold is used for process monitoring to ensure the accuracy of alarm signal, and candidate faults are achieved based on SDG backward inference from the alarm parameters, according to the candidate faults, SDG forward inference is applied to obtain candidate parameters. Secondly, five level threshold and QTA are combined together to achieve qualitative trend of candidate parameters which is utilized for FDD. Finally, real faults are acquired by SDG forward inference on the basis of alarm parameters and qualitative trend of candidate parameters. In order to verify the validity of the method, in the paper, the simulation experiments which include loss of coolant accident, steam generator tube rupture, loss of feed water, main steam line break, and station black-out were done, the case shows that the proposed method is superior to the conventional SDG method and can diagnose the faults more accurately and timely.