This thesis deals with a common cause failure analysis (CCF) for space devices. This analysis belongs among dependability analyses, which have not been sufficiently developed in a field of space industry in corresponding technical and normative documents. Therefore, we focused on devising a new procedure of a qualitative and quantitative common cause failure analysis for the space applications herein. This new procedure of the qualitative and quantitative CCF analysis was applied on redundant systems of a special space device microaccelerometer (ACC), which was developed in VZLÚ. Performance of the qualitative CCF analysis can lead to recommendations to change design of the system, making the system less susceptible to the common cause failures. Performance of the quantitative CCF analysis and its inclusion into the computation of the system reliability can lead to a more accurate estimation of the reliability (in most cases it leads to decreasing the system reliability). During the development of the ACC there were not defined any requirements to perform the CCF analysis within general dependability requirements (defined by the customer and by ECSS standards). Hence, we compared computations of the ACC reliability with and without considering the CCFs. When the CCFs were considered, the reliability of the ACC was decreased according to our assumption. On our example of the ACC we showed advantages of the performance of the CCF analysis within the dependability analyses during development of the space devices.

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).

Within the nuclear industry there is an extensive need for evaluation of the safety of the plant. In such evaluations there is one phenomenon requiring some particular treatment, namely common cause failure (CCF). This involves the occurrences of components failing dependently, meaning failures that can overcome the applied redundancy or diversity. The impact of CCF is relatively large, but unfortunately the process of CCF analysis is complicated by the complex nature of CCF events and a very sparse availability of CCF data. Today, there are a number of methods for CCF analysis available with different characteristics, especially concerning their qualitative and quantitative features. The most common working procedure for CCF treatment is to divide the analysis in a qualitative and a quantitative part, but unfortunately the development of tools for the qualitative part has to a certain extent got behindhand. This subject is further explored in a comparative study focused on two totally different approaches for CCF analysis, the impact vector method and the unified partial method. Based on insights from this study an integrated impact vector and ‘Relations of Defences, Root causes and Coupling factors’ (RDRC) methodology is suggested to be further explored for progress towards a methodology incorporating both qualitative and quantitative aspects.

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).

Adverse weather such as hurricanes can significantly affect the reliability of composite power systems. Predicting the impact of hurricanes can help utilities for better preparedness and make appropriate restoration arrangements. In this dissertation, the impact of hurricanes on the reliability of composite power systems is investigated. Firstly, the impact of adverse weather on the long-term reliability of composite power systems is investigated by using Markov cut-set method. The Algorithms for the implementation is developed. Here, two-state weather model is used. An algorithm for sequential simulation is also developed to achieve the same goal. The results obtained by using the two methods are compared. The comparison shows that the analytical method can obtain comparable results and meantime it can be faster than the simulation method. Secondly, the impact of hurricanes on the short-term reliability of composite power systems is investigated. A fuzzy inference system is used to assess the failure rate increment of system components. Here, different methods are used to build two types of fuzzy inference systems. Considering the fact that hurricanes usually last only a few days, short-term minimal cut-set method is proposed to compute the time-specific system and nodal reliability indices of composite power systems. The implementation demonstrates that the proposed methodology is effective and efficient and is flexible in its applications. Thirdly, the impact of hurricanes on the short-term reliability of composite power systems including common-cause failures is investigated. Here, two methods are proposed to archive this goal. One of them uses a Bayesian network to alleviate the dimensionality problem of conditional probability method. Another method extends minimal cut-set method to accommodate common-cause failures. The implementation results obtained by using the two methods are compared and their discrepancy is analyzed. Finally, the proposed methods in this dissertation are also applicable to other applications in power systems.

Triple modular redundancy (TMR) with repair is commonly used to improve the reliability of systems. TMR is often employed for circuits implemented on field programmable gate arrays (FPGAs) to mitigate the radiation effects of single event upsets (SEUs). This has proven to be an effective technique by improving a circuit's sensitive cross-section by up to 100x. However, testing has shown that the improvement offered by TMR is limited by upsets in single configuration bits that cause TMR to fail.This work proposes a variety of mitigation techniques that improve the effectiveness of TMR on FPGAs. These mitigation techniques can alter the circuit's netlist and how the circuit is placed and routed on the FPGA. TMR with repair showed a neutron cross-section improvement of 100x while the best mitigation technique proposed in this work showed an improvement of 700x.This work demonstrates both some causes behind single bit SEU failures for TMR circuits on FPGAs and mitigation techniques to address these failures. In addition to these findings, this work also shows that the majority of radiation failures in these circuits are caused by multiple cell upsets, laying the path for future work to further enhance the effectiveness of TMR on FPGAs.