Conventional reliability analysis methods assume that a simulation model is able to represent the real physics accurately. However, this assumption may not always hold as the simulation model could be biased due to simplifications and idealizations. Simulation models are approximate mathematical representations of real-world systems and thus cannot exactly imitate the real-world systems. The accuracy of a simulation model is especially critical when it is used for the reliability calculation. Therefore, a simulation model should be validated using prototype testing results for reliability analysis. However, in practical engineering situation, experimental output data for the purpose of model validation is limited due to the significant cost of a large number of physical testing. Thus, the model validation needs to be carried out to account for the uncertainty induced by insufficient experimental output data as well as the inherent variability existing in the physical system and hence in the experimental test results. Therefore, in this study, a confidence-based model validation method that captures the variability and the uncertainty, and that corrects model bias at a user-specified target confidence level, has been developed. Reliability assessment using the confidence-based model validation can provide conservative estimation of the reliability of a system with confidence when only insufficient experimental output data are available. Without confidence-based model validation, the designed product obtained using the conventional reliability-based design optimization (RBDO) optimum could either not satisfy the target reliability or be overly conservative. Therefore, simulation model validation is necessary to obtain a reliable optimum product using the RBDO process. In this study, the developed confidence-based model validation is integrated in the RBDO process to provide truly confident RBDO optimum design. The developed confidence-based model validation will provide a conservative RBDO optimum design at the target confidence level. However, it is challenging to obtain steady convergence in the RBDO process with confidence-based model validation because the feasible domain changes as the design moves (i.e., a moving-target problem). To resolve this issue, a practical optimization procedure, which terminates the RBDO process once the target reliability is satisfied, is proposed. In addition, the efficiency is achieved by carrying out deterministic design optimization (DDO) and RBDO without model validation, followed by RBDO with the confidence-based model validation. Numerical examples are presented to demonstrate that the proposed RBDO approach obtains a conservative and practical optimum design that satisfies the target reliability of designed product given a limited number of experimental output data. Thus far, while the simulation model might be biased, it is assumed that we have correct distribution models for input variables and parameters. However, in real practical applications, only limited numbers of test data are available (parameter uncertainty) for modeling input distributions of material properties, manufacturing tolerances, operational loads, etc. Also, as before, only a limited number of output test data is used. Therefore, a reliability needs to be estimated by considering parameter uncertainty as well as biased simulation model. Computational methods and a process are developed to obtain confidence-based reliability assessment. The insufficient input and output test data induce uncertainties in input distribution models and output distributions, respectively. These uncertainties, which arise from lack of knowledge – the insufficient test data, are different from the inherent input distributions and corresponding output variabilities, which are natural randomness of the physical system.

A new reliability-based probabilistic model is developed for the estimation of liquefaction-induced lateral ground spreading, taking into consideration the uncertainties within the model functional form and the descriptive variables as well. The new model is also introduced as performance-based probabilistic engineering tool.

Recent major electrical disturbances highlight the extent to which modern societies depend on a reliable power infrastructure and the impact of these undesirable events on the economy and society. Numerous blackout models have been developed in the last decades that capture effectively the cascade mechanism leading to a partial or complete blackout. These models usually consider only the state of the electrical part of the system and investigate how failures or limitations in this system affect the probability and severity of a blackout.However, an analysis of the major disturbances that occurred during the last decade, such as the North America blackout of 2003 and the UCTE system disturbance of 2006, shows that failures or inadequacies in the Information and Communication Technology (ICT) infrastructure and also human errors had a significant impact on most of these blackouts.The aim of this thesis is to evaluate the contribution of these non-electrical events to the risk of power system blackouts. As the nature of these events is probabilistic and not deterministic, different probabilistic techniques have been developed to evaluate their impact on power systems reliability and operation.In particular, a method based on Monte Carlo simulation is proposed to assess the impact of an ICT failure on the operators’ situation awareness and consequently on their performance during an emergency. This thesis also describes a generic framework using Markov modeling for quantifying the impact of insufficient situation awareness on the probability of cascading electrical outages leading to a blackout. A procedure based on Markov modeling and fault tree analysis is also proposed for assessing the impact of ICT failures and human errors on the reliable operation of fast automatic protection actions, which are used to provide protection against fast-spreading electrical incidents. The impact of undesirable interactions and the uncoordinated operation of these protection schemes on power system reliability is also assessed in this thesis.The simulation results of these probabilistic methods show that a deterioration in the state of the ICT infrastructure and human errors affect significantly the probability and severity of power system blackouts. The conclusion of the work undertaken in this research is that failures in all the components of the power system, and not just the “heavy electrical” ones, must be considered when assessing the reliability of the electrical supply.

The diploma thesis deals with the assessment of the reliability of processes at the packaging center in the company CommScope Czech Republic s.r.o. The first part of the work is research about options for quality management using ISO 9000 standards, but also TQM approaches. At the same time, methods and analytical techniques are discussed, which will be further used in the practical part of this thesis. The following section introduces CommScope Inc. and its main product - optical fiber. The practical part contains an introduction to the packaging center and its processes, it also describes the analysis of the reliability of the original state and proposals for corrective measures that were designed within the Kaizen event. The rest of the work is then devoted to the implementation of the proposed changes and their economic assessment. Finally, an evaluation of the methods used, the results achieved and other recommendations are given.

In this paper, a new methodology is presented to estimate the lifetime impact of design changes, called Failure Likelihood Assessment (FLA). The discussion in this paper is on the fatigue lifetime prediction of axial piston units, especially after a design change. The demonstration object is an axial piston pump due to extreme environmental conditions and high specification demands, where the FLA is applied to a manufacturing change in an existing product and delivers an effect on the unit reliability. The resulted reliability imp rovement, if combined with typical calculation methods like Weibull analysis, delivers an increase in predicted lifetime considering the intended modification. As demonstration subje ct, a change of the manufacturing process of the cylinder block hub in an axial piston pump is used. The effect to the lifetime is predicted via the FLA-method and the results are calculated with test data and compared to theoretical results. The paper shows that the methodology delivers highly accurate results providing that the FLA is a powerful tool to analyze design changes as weil as new designs in regard to reliability and lifetime. The benefit for the user of this methodology will hence be more reliable products with optimized designs tobest fulfil customer's performance requirements.

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Разработан способ и программа для оценки исполнительных геодезических схем в камеральных условиях с целью обоснования принятия решения о необходимости проведения инструментального контроля планово-высотного положения строительных конструкций. / A method and program have been developed for evaluating executive geodetic schemes in office conditions in order to justify the decision on the need for instrumental control of the planned-altitude position of building structures.

[pt] Fontes renováveis são importantes recursos a serem agregados aos sistemas de energia elétrica em prol da descentralização da geração. Discussões acerca dos efeitos ambientais direcionam os estudos em busca de alternativas que possibilitem minimizar a emissão de gases poluentes e diversifiquem a matriz elétrica. Nesse contexto, a geração distribuída (GD) de natureza renovável vem se mostrando cada vez mais presente, alterando a estrutura clássica do sistema e conferindo um maior protagonismo do consumidor. Assim, torna-se essencial avaliar o desempenho dessas novas redes de distribuição no atendimento à demanda, de modo a estabelecer padrões adequados e monitorá-los através das agências reguladoras. Há uma diversidade de métodos de avaliação do desempenho dessas redes, principalmente através dos conceitos de confiabilidade, para lidar com as falhas de equipamentos e os efeitos decorrentes. A presente tese tem como objetivo avaliar índices de confiabilidade de sistemas de distribuição na presença de GD. Para isso, são apresentadas três técnicas baseadas em simulação Monte Carlo (SMC). Uma clássica, denominada SMC sequencial, tem como objetivo modelar a natureza cronológica do problema bem como as incertezas provenientes da intermitência de fontes de GD. A segunda, baseada na SMC quase sequencial, caracteriza-se por sua simplicidade e capacidade em manter a flexibilidade da SMC sequencial, porém, com melhor desempenho em termos de precisão e tempo de processamento. Por último, uma SMC baseada nos conceitos de transição de estado do sistema de forma cronológica assegura também precisão e flexibilidade à técnica. Novas funções teste são propostas para alcançar tal objetivo. Dois sistemas com inserção de GD são utilizados para avaliar o desempenho dos conceitos e técnicas propostas: IEEE RBTS, rede acadêmica padrão amplamente utilizada na área de confiabilidade; e uma rede real. A discussão exaustiva dos resultados confirma que as propostas cumprem os objetivos estabelecidos. / [en] Renewable sources are important resources to be added to electrical energy systems in favor of decentralized generation. Discussions about environmental effects drive the studies in search of alternatives that make it possible to minimize the emission of polluting gases and diversify the electrical matrix. In this context, distributed generation (DG) of a renewable nature has been increasingly present, changing the classic structure of the system and giving greater engagement to the consumer. Therefore, it is essential to evaluate the performance of these new distribution grids in meeting the power demand, in order to establish appropriate standards and monitor them through the regulatory agencies. There is a diversity of methods for evaluating the performance of these networks, mainly through reliability concepts, to deal with equipment failures and the resulting effects. This thesis aims to evaluate reliability indices of distribution systems in the presence of DG. To this end, three techniques based on Monte Carlo simulation (MCS) are presented. A classic one, called sequential MCS, aims at modeling the chronological nature of the problem as well as the uncertainties arising from the intermittency of the DG sources. The second one, based on a quasi-sequential MCS, is characterized by its simplicity and ability to maintain the flexibility of sequential MCS, but with better performance in terms of precision and processing time. Finally, an MCS based on the concepts of chronological system state transition, also provides precision and flexibility to the reliability assessment. New test functions are proposed to achieve this goal. Two systems with DG insertion are used to evaluate the performance of the proposed concepts and techniques: IEEE RBTS, a standard academic network widely used in the reliability area; and another real network. The exhaustive discussion of the results confirms that both proposals meet the established objectives.

Abstract The goal of this research is to advance the state of the art in bulk power system reliability assessment. Bulk power system reliability assessment is an important procedure at both power system planning and operating stages to assure reliable and acceptable electricity service to customers. With the increase in the complexity of modern power systems and advances in the power industry toward restructuring, the system models and algorithms of traditional reliability assessment techniques are becoming obsolete as they suffer from nonrealistic system models and slow convergence (even non-convergence) when multi-level contingencies are considered and the system is overstressed. To allow more rigor in system modeling and higher computational efficiency in reliability evaluation procedures, this research proposes an analytically-based security-constrained adequacy evaluation (SCAE) methodology that performs bulk power system reliability assessment. The SCAE methodology adopts a single-phase quadratized power flow (SPQPF) model as a basis and encompasses three main steps: (1) critical contingency selection, (2) effects analysis, and (3) reliability index computations. In the critical contingency selection, an improved contingency selection method is developed using a wind-chime contingency enumeration scheme and a performance index approach based on the system state linearization technique, which can rank critical contingencies with high accuracy and efficiency. In the effects analysis for selected critical contingencies, a non-divergent optimal quadratized power flow (NDOQPF) algorithm is developed to (1) incorporate major system operating practices, security constraints, and remedial actions in a constrained optimization problem and (2) guarantee convergence and provide a solution under all conditions. This algorithm is also capable of efficiently solving the ISO/RTO operational mode in deregulated power systems. Based on the results of the effects analysis, reliability indices that provide a quantitative indication of the system reliability level are computed. In addition, this research extends the proposed SCAE framework to include the effects of protection system hidden failures on bulk power system reliability. The overall SCAE methodology is implemented and applied to IEEE reliability test systems, and evaluation results demonstrate the expected features of proposed advanced techniques. Finally, the contributions of this research are summarized and recommendations for future research are proposed.

'Independent component analysis' (ICA) ist ein Werkzeug der statistischen Datenanalyse und Signalverarbeitung, welches multivariate Signale in ihre Quellkomponenten zerlegen kann. Obwohl das klassische ICA Modell sehr nützlich ist, gibt es viele Anwendungen, die Erweiterungen von ICA erfordern. In dieser Dissertation präsentieren wir neue Verfahren, die die Funktionalität von ICA erweitern: (1) Zuverlässigkeitsanalyse und Gruppierung von unabhängigen Komponenten durch Hinzufügen von Rauschen, (2) robuste und überbestimmte ('over-complete') ICA durch Ausreissererkennung, und (3) nichtlineare ICA mit Kernmethoden. / Independent component analysis (ICA) is a tool for statistical data analysis and signal processing that is able to decompose multivariate signals into their underlying source components. Although the classical ICA model is highly useful, there are many real-world applications that require powerful extensions of ICA. This thesis presents new methods that extend the functionality of ICA: (1) reliability and grouping of independent components with noise injection, (2) robust and overcomplete ICA with inlier detection, and (3) nonlinear ICA with kernel methods.