The book focuses on system dependability modeling and calculation, considering the impact of s-dependency and uncertainty. The best suited approaches for practical system dependability modeling and calculation, (1) the minimal cut approach, (2) the Markov process approach, and (3) the Markov minimal cut approach as a combination of (1) and (2) are described in detail and applied to several examples. The stringently used Boolean logic during the whole development process of the approaches is the key for the combination of the approaches on a common basis. For large and complex systems, efficient approximation approaches, e.g. the probable Markov path approach, have been developed, which can take into account s-dependencies be-tween components of complex system structures. A comprehensive analysis of aleatory uncertainty (due to randomness) and epistemic uncertainty (due to lack of knowledge), and their combination, developed on the basis of basic reliability indices and evaluated with the Monte Carlo simulation method, has been carried out. The uncertainty impact on system dependability is investigated and discussed using several examples with different levels of difficulty. The applications cover a wide variety of large and complex (real-world) systems. Actual state-of-the-art definitions of terms of the IEC 60050-192:2015 standard, as well as the dependability indices, are used uniformly in all six chapters of the book.

This book presents state-of-the-art probabilistic methods for the reliability analysis and design of engineering products and processes. It seeks to facilitate practical application of probabilistic analysis and design by providing an authoritative, in-depth, and practical description of what probabilistic analysis and design is and how it can be implemented. The text is packed with many practical engineering examples (e.g., electric power transmission systems, aircraft power generating systems, and mechanical transmission systems) and exercise problems. It is an up-to-date, fully illustrated reference suitable for both undergraduate and graduate engineering students, researchers, and professional engineers who are interested in exploring the fundamentals, implementation, and applications of probabilistic analysis and design methods.

The application of quantitative reliability evaluation in electric power sys tems has now evolved to the point at which most utilities use these techniques in one or more areas of their planning, design, and operation. Most of the techniques in use are based on analytical models and resulting analytical evaluation procedures. Improvements in and availability of high-speed digi tal computers have created the opportunity to analyze many of these prob lems using stochastic simulation methods and over the last decade there has been increased interest in and use made of Monte Carlo simulation in quantitative power system reliability assessment. Monte Carlo simulation is not a new concept and recorded applications have existed for at least 50 yr. However, localized high-speed computers with large-capacity storage have made Monte Carlo simulation an available and sometimes preferable option for many power system reliability applications. Monte Carlo simulation is also an integral part of a modern undergrad uate or graduate course on reliability evaluation of general engineering systems or specialized areas such as electric power systems. It is hoped that this textbook will help formalize the many existing applications of Monte Carlo simulation and assist in their integration in teaching programs. This book presents the basic concepts associated with Monte Carlo simulation.

This book provides structural reliability and design students with fundamental knowledge in structural reliability, as well as an overview of the latest developments in the field of reliability engineering. It addresses the mathematical formulation of analytical tools for structural reliability assessment. This book offers an accessible introduction to structural reliability assessment and a solid foundation for problem-solving. It introduces the topic and background, before dealing with probability models for random variables. It then explores simulation techniques for single random variables, random vectors consisting of different variables, and stochastic processes. The book addresses analytical approaches for structural reliability assessment, including the reliability models for a single structure and those for multiple structures, as well as discussing the approaches for structural time-dependent reliability assessment in the presence of discrete and continuous load processes. This book delivers a timely and pedagogical textbook, including over 170 worked-through examples, detailed solutions, and analytical tools, making it of interest to a wide range of graduate students, researchers, and practitioners in the field of reliability engineering.

A reader-friendly introduction to reliability analysis and its power systems applications The subset of probability theory known as reliability theory analyzes the likelihood of failure in a given component or system under given conditions. It is a critical aspect of engineering as it concerns systems of all kinds, not least modern power systems, with their essential role in sustaining the technologies on which modern life relies. Reliability Analysis of Modern Power Systems is a thorough, accessible book introducing the core concepts of reliability theory as they apply to power systems engineering, as well as the advanced technologies currently driving new frontiers in reliability analysis. It is a must-own for anyone looking to understand and improve the systems that power our world. Readers will also find: Detailed discussion of reliability modeling and simulation of composite systems using Typhoon HIL 404 Reliability assessment of generation systems, transmission systems, distribution systems, and more Information on renewable energy integration for more sustainable power grids Reliability Analysis of Modern Power Systems is ideal for professionals, engineers, and researchers in power system design and reliability engineering, as well as for advanced undergraduate and graduate students in these and related subjects.

Safety and Reliability of Complex Engineered Systems contains the Proceedings of the 25th European Safety and Reliability Conference, ESREL 2015, held 7-10 September 2015 in Zurich, Switzerland. It includes about 570 papers accepted for presentation at the conference. These contributions focus on theories and methods in the area of risk, safety and

Time-Dependent Reliability Theory and Its Applications introduces the theory of time-dependent reliability and presents methods to determine the reliability of structures over the lifespan of their services. The book contains state-of-the-art solutions to first passage probability derived from the theory of stochastic processes with different types of probability distribution functions, including Gaussian and non-Gaussian distributions and stationary and non-stationary processes. In addition, it provides various methods to determine the probability of failure over time, considering different failure modes and a methodology to predict the service life of structures. Sections also cover the applications of time-dependent reliability to prediction of service life and development of risk cost-optimized maintenance strategy for existing structures. This new book is for those who wants to know how to predict the service life of a structure (buildings, bridges, aircraft structures, etc.) and how to develop a risk-cost, optimized maintenance strategy for these structures. - Presents the basic knowledge required to predict service life and develop a maintenance strategy for infrastructure - Explains how to predict the remaining safe life of the infrastructure during its lifespan of operation - Describes how to carry out maintenance for an infrastructure to ensure its safe and serviceable operation during the designed service life

Although tremendous progress has been made in Artificial Intelligence (AI), it entails new challenges. The growing complexity of learning tasks requires more complex AI components, which increasingly exhibit unreliable behaviour. In this book, we present a model-driven approach to model architectural safeguards for AI components and analyse their effect on the overall system reliability.

This open access book provides an introduction to uncertainty quantification in engineering. Starting with preliminaries on Bayesian statistics and Monte Carlo methods, followed by material on imprecise probabilities, it then focuses on reliability theory and simulation methods for complex systems. The final two chapters discuss various aspects of aerospace engineering, considering stochastic model updating from an imprecise Bayesian perspective, and uncertainty quantification for aerospace flight modelling. Written by experts in the subject, and based on lectures given at the Second Training School of the European Research and Training Network UTOPIAE (Uncertainty Treatment and Optimization in Aerospace Engineering), which took place at Durham University (United Kingdom) from 2 to 6 July 2018, the book offers an essential resource for students as well as scientists and practitioners.