Reliability evaluation of auxiliary feedwater system by mapping GO-FLOW models into Bayesian networks.
Bayesian network (BN) is a widely used formalism for representing uncertainty in probabilistic systems and it has become a popular tool in reliability engineering. The GO-FLOW method is a success-oriented system analysis technique and capable of evaluating system reliability and risk. To overcome the limitations of GO-FLOW method and add new method for BN model development, this paper presents a novel approach on constructing a BN from GO-FLOW model. GO-FLOW model involves with several discrete time points and some signals change at different time points. But it is a static system at one time point, which can be described with BN. Therefore, the developed BN with the proposed method in this paper is equivalent to GO-FLOW model at one time point. The equivalent BNs of the fourteen basic operators in the GO-FLOW methodology are developed. Then, the existing GO-FLOW models can be mapped into equivalent BNs on basis of the developed BNs of operators. A case of auxiliary feedwater system of a pressurized water reactor is used to illustrate the method. The results demonstrate that the GO-FLOW chart can be successfully mapped into equivalent BNs.
College of Mechanical and Electrical Engineering, China University of Petroleum, Qingdao 266580, China.
Zengkai
Z
Liu
College of Mechanical and Electrical Engineering, China University of Petroleum, Qingdao 266580, China. Electronic address: liuyhup@163.com.
Yonghong
Y
Liu
College of Mechanical and Electrical Engineering, China University of Petroleum, Qingdao 266580, China.
Xinlei
X
Wu
College of Mechanical and Electrical Engineering, China University of Petroleum, Qingdao 266580, China.
Dongwei
D
Yang
College of Mechanical and Electrical Engineering, China University of Petroleum, Qingdao 266580, China.
Baoping
B
Cai
College of Mechanical and Electrical Engineering, China University of Petroleum, Qingdao 266580, China.
Chao
C
Zheng
Auxiliary feedwater system; Bayesian network; GO-FLOW methodology; Reliability