Digital Twin Driven Fault Diagnosis Method for Subsea Control System

被引:4
作者
Ge, Weifeng [1 ]
He, Rui [1 ]
Wu, Qibing [1 ]
Cai, Baoping [2 ]
Yang, Chao [2 ]
Zhang, Fei [3 ]
机构
[1] CNOOC Safety & Technol Serv Co Ltd, CNOOC EnerTech, Safety & Environm Protect Branch, Tianjin 300457, Peoples R China
[2] China Univ Petr, Coll Mech & Elect Engn, Qingdao 266580, Shandong, Peoples R China
[3] China Natl Petr Offshore Engn Co Ltd, Tianjin 300457, Peoples R China
关键词
Fault diagnosis; digital twin; subsea control system; diagnostic verification; Bayesian networks;
D O I
10.1109/ACCESS.2023.3325322
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Digital twin driven fault diagnosis shows good performance in fault diagnosis of subsea control systems. However, the relation between digital twin and fault diagnosis is not clear. This cannot bring substantial improvement to fault diagnosis. A digital twin driven fault diagnosis method for subsea control system is proposed. Simulink is used for building a digital twin model and a fault diagnosis model is established based on Bayesian networks. The diagnosis results are input into the digital twin model to verify them. The results of verification are feedback to fault diagnosis model. Through this method, a framework that improves fault diagnosis by digital twin is proposed and provide a reference for related research. The performance of this method is verified by a redundant control system. The results show that in this framework, the digital twin model can improve diagnostic performance effectively.
引用
收藏
页码:116269 / 116276
页数:8
相关论文
共 20 条
[1]   Remaining Useful Life Estimation of Structure Systems Under the Influence of Multiple Causes: Subsea Pipelines as a Case Study [J].
Cai, Baoping ;
Shao, Xiaoyan ;
Liu, Yonghong ;
Kong, Xiangdi ;
Wang, Haifeng ;
Xu, Hongqi ;
Ge, Weifeng .
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, 2020, 67 (07) :5737-5747
[2]   Bayesian Networks in Fault Diagnosis [J].
Cai, Baoping ;
Huang, Lei ;
Xie, Min .
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, 2017, 13 (05) :2227-2240
[3]   Fatigue reliability evaluation of heavy-haul locomotive car body underframe based on measured strain and virtual strain [J].
Chen, Daoyun ;
Xiao, Qian ;
Mou, Minghui ;
Yang, Wenbin ;
Liu, Xinlong ;
Zeng, Yanjun .
INTERNATIONAL JOURNAL OF FATIGUE, 2023, 172
[4]   Structural damage identification using modified Hilbert-Huang transform and support vector machine [J].
Diao, Yansong ;
Jia, Dantong ;
Liu, Guodong ;
Sun, Zuofeng ;
Xu, Jing .
JOURNAL OF CIVIL STRUCTURAL HEALTH MONITORING, 2021, 11 (04) :1155-1174
[5]   Reliability analysis of subsea blowout preventers with condition-based maintenance using stochastic Petri nets [J].
Elusakin, Tobi ;
Shafiee, Mahmood .
JOURNAL OF LOSS PREVENTION IN THE PROCESS INDUSTRIES, 2020, 63 (63)
[6]   Fault Detection and Classification for Photovoltaic Systems Based on Hierarchical Classification and Machine Learning Technique [J].
Eskandari, Aref ;
Milimonfared, Jafar ;
Aghaei, Mohammadreza .
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, 2021, 68 (12) :12750-12759
[7]   Research on modeling and fault diagnosis of inter-turn short fault of permanent magnet synchronous motor [J].
Hu, Hongqian ;
Shi, Weifeng ;
Venditti, Benjamin .
JOURNAL OF COMPUTATIONAL METHODS IN SCIENCES AND ENGINEERING, 2020, 20 (03) :959-973
[8]   Diagnosing with a hybrid fuzzy-Bayesian inference approach [J].
Koscielny, Jan Maciej ;
Bartys, Michal ;
Sztyber, Anna .
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, 2021, 104
[9]   Machining process-oriented monitoring method based on digital twin via augmented reality [J].
Liu, Shimin ;
Lu, Shanyu ;
Li, Jie ;
Sun, Xuemin ;
Lu, Yuqian ;
Bao, Jinsong .
INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY, 2021, 113 (11-12) :3491-3508
[10]   Risk coupling analysis of subsea blowout accidents based on dynamic Bayesian network and NK model [J].
Liu, Zengkai ;
Ma, Qiang ;
Cai, Baoping ;
Shi, Xuewei ;
Zheng, Chao ;
Liu, Yonghong .
RELIABILITY ENGINEERING & SYSTEM SAFETY, 2022, 218