Extend GO Methodology to Support Common-Cause Failures Modeling Explicitly by Means of Bayesian Networks

Abstract

As a success-oriented system reliability and safety-analysis technique, the GO methodology has been applied in a variety of real-world safety-critical industrial fields. Common-cause failure (CCF) is the simultaneous failure of multicomponents within a system due to the same root cause. An enhancement approach for the original GO methodology is proposed in this paper to support CCF modeling and calculation both in graphical modeling aspect and algorithm aspect. First, a new concise and formalized GO operator (named CCO) is introduced to represent complicated CCF event, which makes the CCF modeling process intuitive and concise for analysts. In algorithm aspect, the mapping rule is given and demonstrated to transform new CCO operator with impacted multiple operators to the corresponding Bayesian network (BN) fragment. Second, general mapping programmable process is presented on transforming any CCF enhanced GO model to the corresponding BN. Furthermore, using BN's inference capability, the enhanced GO model with CCF can be calculated efficiently. Nevertheless, the diagnosis process can be performed to investigate the weak points of the modeled system. Finally, a case study is performed to demonstrate the modeling process by means of CCF enhanced GO model. The calculation result shows that CCF has significant influence on the system reliability. Using diagnosis analysis, the CCF event can be confirmed as the major cause leading to system failure.