Expert knowledge modelling software design based on Signed Directed Graph with the application for PWR fault diagnosis

被引：1
|
作者：
Ma, Zhanguo [1 ,2 ]
Deng, Shiguang [3 ]
Zhou, Zhuoran [2 ]
Ai, Xin [2 ]
Zhang, Jing [3 ]
Liu, Yongkuo [2 ]
Peng, Minjun [2 ]
Cui, Jing [1 ]
机构：
[1] Harbin Univ Sci & Technol, Sch Automat, Harbin 150080, Peoples R China
[2] Harbin Engn Univ, Coll Nucl Sci & Technol, Fundamental Sci Nucl Safety & Simulat Technol Lab, Harbin 150001, Heilongjiang, Peoples R China
[3] CNNC China Nucl Power Engn Co Ltd, Beijing 100840, Peoples R China
来源：
ANNALS OF NUCLEAR ENERGY | 2024年 / 196卷
关键词：
Signed directed graph; Fault detection and diagnosis; Expert knowledge models; SDG; SAFETY;
D O I：
10.1016/j.anucene.2023.110206
中图分类号：
TL [原子能技术]; O571 [原子核物理学];
学科分类号：
0827 ; 082701 ;
摘要：
Online monitoring and Fault Diagnosis and Detection (FDD) can help the operator to enhance the situation awareness. The Signed Directed Graph (SDG) has the significant advantages that SDG-based FDD not only infers the incipient faults and reveals fault propagation paths but also comprehensively explains causes of failure. In this study, an expert knowledge modelling software is designed and developed based on SDG method. Firstly, the SDG-based process monitoring and FDD algorithms are derived by coupling the threshold method and the quality trend analysis method. Secondly, the expert knowledge modelling software is designed and developed not only considering the convenience of the engineer but also the effectiveness of the SDG-based algorithms. Finally, a Pressurized Water Reactor (PWR) is applied to demonstrate the SDG modelling and verify the SDG models. The case study demonstrates that the developed expert knowledge modelling software can effectively diagnose the incipient faults and infer variable impact as the fault propagation paths
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页数：11
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