A cascade intelligent fault diagnostic technique for nuclear power plants

Safe operation of nuclear power plant is one of the most important tasks in nuclear power development. This justifies the variety of methods that have been proposed to support the operators in the task of plant condition monitoring, fault detection, and diagnosis. A number of hybrid fault detection and diagnosis methods have also been proposed, with their attendant weaknesses. This work proposes the hybrid of principal component analysis (PCA), signed directed graph (SDG), and Elman Neural Network (ENN) for fault detection, fault isolation, and severity estimation, respectively. The proposed hybrid method is verified with the data derived from Personal Computer Transient Analyzer (PCTRAN) simulation. The verification result shows that the PCA-based fault detection methodology realized timely detection of anomaly in the simulated nuclear power plants system, the SDG-based fault recognition method was able to isolate the system abnormality and identify the root causes, and the ENN-based fault severity estimation method presents the failure fraction of fault, representing the severity. With this integrated hybrid method, more fault information is provided for the operators, which serves as a good foundation for further decision-making and interventions.