Construction of fault diagnosis system for control rod drive mechanism based on knowledge graph and Bayesian inference

• Published in: Nuclear science and techniques Vol. 34; no. 2; pp. 58 - 75
• Main Authors: Jiang, Xue-Jun, Zhou, Wen, Hou, Jie
• Format: Journal Article
• Language: English
• Published: Singapore Springer Nature Singapore 01.02.2023; Shanghai Institute of Applied Physics,Chinese Academy of Sciences,Shanghai 201800,China; University of Chinese Academy of Sciences,Beijing 100049,China%Shanghai Institute of Applied Physics,Chinese Academy of Sciences,Shanghai 201800,China
• Subjects: Beam Physics; Nuclear Energy; Particle Acceleration and Detection; Particle and Nuclear Physics; Physics; Physics and Astronomy; Fault diagnosis; Web interface; RBT3-TextCNN; Bayesian inference; CRDM; Knowledge graph
• ISSN: 1001-8042; 2210-3147
• DOI: 10.1007/s41365-023-01173-8

Knowledge graph technology has distinct advantages in terms of fault diagnosis. In this study, the control rod drive mechanism (CRDM) of the liquid fuel thorium molten salt reactor (TMSR-LF1) was taken as the research object, and a fault diagnosis system was proposed based on knowledge graph. The subject–relation–object triples are defined based on CRDM unstructured data, including design specification, operation and maintenance manual, alarm list, and other forms of expert experience. In this study, we constructed a fault event ontology model to label the entity and relationship involved in the corpus of CRDM fault events. A three-layer robustly optimized bidirectional encoder representation from transformers (RBT3) pre-training approach combined with a text convolutional neural network (TextCNN) was introduced to facilitate the application of the constructed CRDM fault diagnosis graph database for fault query. The RBT3-TextCNN model along with the Jieba tool is proposed for extracting entities and recognizing the fault query intent simultaneously. Experiments on the dataset collected from TMSR-LF1 CRDM fault diagnosis unstructured data demonstrate that this model has the potential to improve the effect of intent recognition and entity extraction. Additionally, a fault alarm monitoring module was developed based on WebSocket protocol to deliver detailed information about the appeared fault to the operator automatically. Furthermore, the Bayesian inference method combined with the variable elimination algorithm was proposed to enable the development of a relatively intelligent and reliable fault diagnosis system. Finally, a CRDM fault diagnosis Web interface integrated with graph data visualization was constructed, making the CRDM fault diagnosis process intuitive and effective.