Automatic Reliability Analysis in the Presence of Probabilistic Common Cause Failures

Common cause failures (CCFs) are simultaneous failures of multiple components in a system and must be considered for accurate and realistic reliability analysis. Traditional CCF analysis techniques typically assume deterministic failures of the affected components. However, CCFs are usually probabilistic, i.e., when a common cause occurs, the affected components fail with different probabilities. Existing techniques that consider probabilistic CCFs (PCCFs) introduce significant execution time and memory overheads to the underlying reliability analysis—limiting their application to small systems only. This paper proposes a fast and automatic PCCF analysis that is based on i) deriving the mutually exclusive success paths of the system using binary decision diagrams (BDDs), and ii) analyzing each path considering PCCFs using explicit and implicit methods. Moreover, an alternative stochastic logic-based technique is presented that compromises analysis accuracy for execution time, and can be used when BDD-based techniques are prohibitive due to their memory overheads. Experimental results show that compared to the state of the art, our methods calculate the system's reliability between 1.1 
$\times$
and 43.4 
$\times$
faster while requiring up to 99.94 % less memory.