Bayes geometric scaling model for common cause failure rates

Athena Zitrou, Tim Bedford, Lesley Walls

Research output: Contribution to journalArticlepeer-review

10 Citations (Scopus)


This paper proposes a mathematical model to associate key operational, managerial and design characteristics of a system with the system's susceptibility towards common cause failure (CCF) events. The model, referred to as the geometric scaling (GS) model, is a mathematical form that allows us to investigate the effect of possible system modifications on risk. As such, the presented methodology results in a CCF model with a strong decision-making character. Based on a Bayesian framework, the GS model allows for the representation of epistemic uncertainty, the update of prior uncertainty in the light of operational data and the coherent use of observations coming from different systems. From a CCF perspective these are particularly useful model features, because CCF events are rare; hence, the operational data available is sparse and is characterised by considerable uncertainty, with databases typically containing events from nominally identical systems from different plants. The GS model also possesses an attractive modelling feature because it significantly decreases the amount of information elicited from experts required for quantification.
Original languageEnglish
Pages (from-to)70-76
Number of pages7
JournalReliability Engineering and System Safety
Issue number2
Publication statusPublished - Feb 2010


  • common cause failures
  • failure rate
  • epistemic uncertainty
  • system defences
  • Bayesian update
  • expert judgment


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