Common Cause Failure Analysis - Improved Approach for Determining the Beta Value in the PDS Method
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This master thesis investigates definitions and classifications of the common cause failure from various industries. From the investigation, four core aspects of the definition for the common cause failure are extracted, and improved classifications for root causes and coupling factors of the common cause failure are suggested.This thesis also explores a framework for the inclusion of the impact of the common cause failure in risk and reliability evaluations. From the investigation of the framework, the relationship among definition of the common cause failure, causes of the common cause failure, common cause failure parametric model, parameter estimation and system model is provided. Especially, this thesis focuses on the common cause failure parametric model that is used to quantify common cause failure effect on the risk assessment. The thesis investigates popular common cause failure parametric models (Beta Factor model, Alpha Factor model, Multiple Greek Letter model, and Multiple Beta Factor model.), and discusses their features. After the investigation of common cause failure parametric model, this thesis focuses on the Beta Factor Model and its parameter estimation. For the parameter estimation, two conventional approaches (data based and checklist based) and a relatively new approach (PDS method) are presented. The main contribution of this thesis is ⅰ) to identify challenges of the PDS estimator in the PDS method and ⅱ) to suggest improved approach for determining the value of PDS estimator. This thesis identifies underlying assumptions and constraints in PDS estimators, and proposes improved approach to determine CCF group size. Case study is conducted to apply this improved approach. This new approach is realized as a computer program using Microsoft Excel and its embedded Visual Basic, so that the industries can easily use the new approach.