Enhance GO methodology for reliability analysis of the closed-loop system using Cyclic Bayesian Networks

Abstract GO methodology is a success-oriented method for reliability analysis of complex safety critical system, which existing in various fields, such as aircraft carrier, nuclear plant, petrochemical plant, etc. However, the traditional GO methodology is insufficient in modeling the system with feedback signals, which are common in those systems. The challenge lies in modeling the closed-loop feedback process and its algorithms. To address this issue, an approach based on Cyclic Bayesian Networks (CBNs) is presented in this paper to enhance its capability of feedback modeling. In the approach, Type 9 operator is the key element to be introduced to simulate a component with feedback signal, and then the GO model can be cyclic to represent a system with feedback loops; furthermore, we compare the decision capability of the GO and BN methodologies in dynamic structure and uncertainty handling. Considering the complexity of the analysis of the GO method, the cyclic GO model is mapped to its corresponding CBNs according to some mapping rules. And leveraging matured algorithms and toolkit of BNs, we can not only obtain the probability of each node in each state via failure propagation, but also identify critical events given the event occurrence via backward reasoning. Eventually, a case of chemical treatment tank liquid level controlling minus-feedback system is analyzed to demonstrate the approach’s feasibility, and by enhancing the capability with loop structured system modeling, the approach makes GO methodology more practical in more modern complex engineering systems with feedback loops.