Zitrou, Athena and Bedford, Tim and Walls, Lesley (2010) Bayes geometric scaling model for common cause failure rates. Reliability Engineering and System Safety, 95 (2). pp. 70-76. ISSN 0951-8320Full text not available in this repository. (Request a copy from the Strathclyde author)
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.
|Keywords:||common cause failures, failure rate, epistemic uncertainty, system defences, Bayesian update, expert judgment, Management. Industrial Management, Applied Mathematics, Industrial and Manufacturing Engineering, Safety, Risk, Reliability and Quality|
|Subjects:||Social Sciences > Industries. Land use. Labor > Management. Industrial Management|
|Department:||Strathclyde Business School > Management Science|
|Depositing user:||Mrs Caroline Sisi|
|Date Deposited:||22 Jan 2010 17:51|
|Last modified:||22 Mar 2017 10:29|