TY - GEN
T1 - Risk Analysis of Discrete Dynamic Event Tree Based on Dynamic Bayesian Network
AU - Fan, Li Ming
AU - Jia, Lu Lu
AU - Ren, Yi
AU - Wang, Kun Sheng
AU - Yang, De Zhen
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/8
Y1 - 2019/8
N2 - With the development of science and technology, systems' complexity has increased sharply because of systems' characteristic of polymorphism and dependence. Although lots of classical methods have been proposed, they show great limitations in dynamic systems' probabilistic risk assessment. To solve this problem, a probabilistic risk assessment method that combines Discrete Dynamic Event Tree (DDET) and Dynamic Bayesian Network (DBN) is proposed in this paper. Compared with other methods, the new method has lots of advantages: (1) Based on DBN's mature mathematical foundation and software support, it provides a useful method for DDET's solution (2) Sensitivity analysis based on DBN helps analysts find high sensitivity events (key events) in system, which provides significant guidance for the development of preventive measures; (3) DDET focuses on the logical combination of events, while Dynamic Bayesian Network focuses on the correlation between different events. As a result, the combination of DDET and DBN helps analysts have a better understanding of risk events in complex systems.
AB - With the development of science and technology, systems' complexity has increased sharply because of systems' characteristic of polymorphism and dependence. Although lots of classical methods have been proposed, they show great limitations in dynamic systems' probabilistic risk assessment. To solve this problem, a probabilistic risk assessment method that combines Discrete Dynamic Event Tree (DDET) and Dynamic Bayesian Network (DBN) is proposed in this paper. Compared with other methods, the new method has lots of advantages: (1) Based on DBN's mature mathematical foundation and software support, it provides a useful method for DDET's solution (2) Sensitivity analysis based on DBN helps analysts find high sensitivity events (key events) in system, which provides significant guidance for the development of preventive measures; (3) DDET focuses on the logical combination of events, while Dynamic Bayesian Network focuses on the correlation between different events. As a result, the combination of DDET and DBN helps analysts have a better understanding of risk events in complex systems.
KW - discrete dynamic event tree
KW - dynamic Bayesian network
KW - risk analysis
UR - https://www.scopus.com/pages/publications/85082389901
U2 - 10.1109/QR2MSE46217.2019.9021120
DO - 10.1109/QR2MSE46217.2019.9021120
M3 - 会议稿件
AN - SCOPUS:85082389901
T3 - Proceedings of 2019 International Conference on Quality, Reliability, Risk, Maintenance, and Safety Engineering, QR2MSE 2019
SP - 141
EP - 147
BT - Proceedings of 2019 International Conference on Quality, Reliability, Risk, Maintenance, and Safety Engineering, QR2MSE 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2019 International Conference on Quality, Reliability, Risk, Maintenance, and Safety Engineering, QR2MSE 2019
Y2 - 6 August 2019 through 9 August 2019
ER -