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

引用

页数：11

共 50 条

[21] A Hybrid Fault Diagnosis Method Based on Fuzzy Signed Directed Graph and Neighborhood Rough Set
Ma, Xin
Li, Dazi
2017 6TH DATA DRIVEN CONTROL AND LEARNING SYSTEMS (DDCLS), 2017, : 253 - 258
[22] IMPROVEMENT OF THE ACCURACY OF A FAULT-DIAGNOSIS SYSTEM UTILIZING THE SIGNED DIRECTED GRAPH
GUJIMA, F
SHIBATA, B
TSUGE, Y
SHIOZAKI, J
MATSUYAMA, H
OSHIMA, E
KAGAKU KOGAKU RONBUNSHU, 1990, 16 (05) : 899 - 906
[23] Multiscale Principal Component Analysis-Signed Directed Graph Based Process Monitoring and Fault Diagnosis
Ali, Husnain
Maulud, Abdulhalim Shah
Zabiri, Haslinda
Nawaz, Muhammad
Suleman, Humbul
Taqvi, Syed Ali Ammar
ACS OMEGA, 2022, 7 (11): : 9496 - 9512
[24] Signed Directed Graph and Qualitative Trend Analysis-Based Method for Process Monitoring and Fault Diagnosis
Gao Dong
Zhang Beike
Ma Xin
PROGRESS IN SAFETY SCIENCE AND TECHNOLOGY, VOL VII, PTS A AND B, 2008, 7 : 366 - 370
[25] Hierarchy Fault Diagnosis Based on Signed Directed Graphs Model
Xie Gang
Liu Jing
Chen Zehua
PROCEEDINGS OF THE 2012 24TH CHINESE CONTROL AND DECISION CONFERENCE (CCDC), 2012, : 2270 - 2274
[26] Knowledge Fusion Based on D-S Theory and Its Application on Expert System for Software Fault Diagnosis
Yan, Ran
Li, Guoqi
Liu, Bin
2015 PROGNOSTICS AND SYSTEM HEALTH MANAGEMENT CONFERENCE (PHM), 2015,
[27] Signed directed graph-based hierarchical modelling and fault propagation analysis for large-scale systems
Yang, Fan
Xiao, Deyun
Shah, Sirish Lalji
IET CONTROL THEORY AND APPLICATIONS, 2013, 7 (04): : 537 - 550
[28] SIGNED DIRECTED GRAPH AND FUZZY-LOGIC BASED FAULT-DIAGNOSIS FOR TITANIUM-DIOXIDE KILN
VENKATESWARLU, C
GANGIAH, K
INDIAN JOURNAL OF TECHNOLOGY, 1993, 31 (02): : 91 - 97
[29] Model-Based Process Diagnosis: Bond Graph and Signed Directed Graph Tools
Smaili, Rahma
El Harabi, Rafika
Abdelkrim, Mohamed Naceur
2013 INTERNATIONAL CONFERENCE ON CONTROL, DECISION AND INFORMATION TECHNOLOGIES (CODIT), 2013, : 782 - 787
[30] Missile Fault Detection and Localization Based on HBOS and Hierarchical Signed Directed Graph
Hu, Hengsong
Cheng, Yuehua
Jiang, Bin
Li, Wenzhuo
Guo, Kun
AEROSPACE, 2024, 11 (08)

← 1 2 3 4 5 →