Reliability modelling of redundant safety systems without automatic diagnostics incorporating common cause failures and process demand

Sriramula’s work within the Lloyd’s Register Foundation Centre for Safety and Reliability Engineering at the University of Aberdeen is supported by Lloyd’s Register Foundation. The Foundation helps to protect life and property by supporting engineering-related education, public engagement and the application of re-search.Peer reviewedPostprin

Impact of common cause failure on reliability performance of redundant safety related systems subject to process demand

Acknowledgments The authors would like to thank the anonymous reviewers for their constructive comments and feedback.Peer reviewedPostprin

Probabilistic Assessment of Common Cause Failures in Nuclear Power Plants

Common cause failures (CCF) are a significant contributor to risk in complex technological systems, such as nuclear power plants. Many probabilistic parametric models have been developed to quantify the systems subject to the CCF. Existing models include the beta factor model, the multiple Greek letter model, the basic parameter model, the alpha factor model and the binomial failure rate model. These models are often only capable of providing a point estimate, when there are limited CCF data available. Some recent studies have proposed a Bayesian approach to quantify the uncertainties in CCF modeling, but they are limited in addressing the uncertainty in the common failure factors only. This thesis presents a multivariate Poisson model for CCF modeling, which combines the modeling of individual and common cause failures into one process. The key idea of the approach is that failures in a common cause component group of n components are decomposed into superposition of k (>n) independent Poisson processes. Empirical Bayes method is utilized for simultaneously estimating the independent and common cause failure rates which are mutually exclusive. In addition, the conventional CCF parameters can be evaluated using the outcomes of the new approach. Moreover, the uncertainties in the CCF modeling can also be addressed in an integrated manner. The failure rate is estimated as the mean value of the posterior density function while the variance of the posterior represents the variation of the estimate. A MATLAB program of the Monte Carlo simulation was developed to check the behavior of the proposed multivariate Poisson (MVP) model. Superiority over the traditional CCF models has been illustrated. Furthermore, due to the rarity of the CCF data observed at one nuclear power plant, data of the target plant alone are insufficient to produce reliable estimates of the failure rates. Data mapping has been developed to make use of the data from source plants of different sizes. In this thesis, data mapping is combined with EB approach to partially assimilate information from source plants and also respect the data of the target plant. Two case studies are presented using different database. The results are compared to the empirical values provided by the United States Nuclear Regulatory Commission (USNRC)

A General Cause Based Methodology for Analysis of Dependent Failures in System Risk and Reliability Assessments

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

Time-Based Risk-Informed Safety Margins: Concepts and Application to Heterogeneous Systems

A model to quantify the temporal failure probability for a nuclear power station’s fleet of multiple, redundant, emergency diesel generators (EDGs) is developed and demonstrated in this thesis. The initiating event for this problem is Loss of Offsite Power (LOOP). This model calculates the probability that the load on the system overcomes (LOOP duration) the capacity of the system (time until the EDGs fail), as a means to quantify system safety margin; this concept comes from The United States Department of Energy (DOE), the Idaho National Laboratory (INL) and the Electric Power Research Institute (EPRI) collaboration on the “Risk-Informed Safety Margin Characterization” (RISMC) approach. The ultimate application of this model is to quantify improved safety margin for an originally two-EDG system that has been upgraded with an additional, reinforced, FLEX diesel generator (DG). Some unique features of the Non-Recovery Integral (NRI) (main model of this thesis) are that it can account for dynamic timing of the EDG failures, model both hot and cold standby EDG arrangements, and accept time-dependent hazard function inputs for hot standby cases (when the hazard functions meet certain conditions). Nuclear industry and Standardized Plant Analysis Risk (SPAR) model data are used as inputs to the NRI to create six specific system model cases. The results from these cases are compared to see how different EDG arrangements affect the overall system reliability. The three main conclusions drawn from the various result comparisons are the following: (1) adding a FLEX DG to an originally two-EDG system makes the system three times less likely to fail for LOOP durations of 24 hours (further improvement in system reliability is seen for longer LOOP durations); (2) the specific model of load placed on the system has a major impact on the system failure probability quantification; and (3) the most effective way to increase safety margin (for the most likely LOOP duration scenarios) is to reduce the likelihood of common-cause failure events

Utilizaçãoda metodologia "RAMS" na análise de barreiras de segurança de instalações industriais de risco elevado

Tese de mestrado integrado. Engenharia Electrotécnica e de Computadores. Faculdade de Engenharia. Universidade do Porto. 201