Predicting reliability in design of complex systems with common-cause failures and time-varying failure rates

One of the challenges in designing complex systems is determining the reliability benefit of redundant and back-up components. Traditional reliability theory assumes that component failures are independent, which tends to over-predict the improvement provided by redundancy. Various “Common Cause Failure” approaches have been developed to model the actual dependency between nominally independent components. However, these approaches rely on the simplifying assumption that component failure rates and common-cause event rates are constant. This assumption may not be accurate; however, a closed form solution may not otherwise be achievable. An alternative approach using discrete event simulation can evaluate the reliability of complex systems when closed form solutions aren´t achievable; in particular under the condition that individual component failure rates and common cause event rates are time-dependent. This research develops an approach to modeling common cause failures that can accommodate time-varying failure rates.