Traditional parametric Common Cause Failure (CCF) models quantify the soft dependencies between component failures through the use of empirical ratio relationships. Furthermore CCF modeling has been essentially restricted to identical components in redundant formations. While this has been advantageous in allowing the prediction of system reliability with little or no data, it has been prohibitive in other applications such as modeling the characteristics of a system design or including the characteristics of failure when assessing the risk significance of a failure or degraded performance event (known as an event assessment).

This dissertation extends the traditional definition of CCF to model soft dependencies between like and non-like components. It does this through the explicit modeling of soft dependencies between systems (coupling factors) such as sharing a maintenance team or sharing a manufacturer. By modeling the soft dependencies explicitly these relationships can be individually quantified based on the specific design of the system and allows for more accurate event assessment given knowledge of the failure cause.

Since the most data informed model in use is the Alpha Factor Model (AFM), it has been used as the baseline for the proposed solutions. This dissertation analyzes the US Nuclear Regulatory Commission's Common Cause Failure Database event data to determine the suitability of the data and failure taxonomy for use in the proposed cause-based models. Recognizing that CCF events are characterized by full or partial presence of "root cause" and "coupling factor" a refined failure taxonomy is proposed which provides a direct link between the failure cause category and the coupling factors.

This dissertation proposes two CCF models (a) Partial Alpha Factor Model (PAFM) that accounts for the relevant coupling factors based on system design and provide event assessment with knowledge of the failure cause, and (b)General Dependency Model (GDM),which uses Bayesian Network to model the soft dependencies between components. This is done through the introduction of three parameters for each failure cause that relate to component fragility, failure cause rate, and failure cause propagation probability.