The Monte Carlo Simulation Method For System Reliability And Risk Analysis

Monte Carlo simulation is one of the best tools for performing realistic analysis of complex systems as it allows most of the limiting assumptions on system behavior to be relaxed. The Monte Carlo Simulation Method for System Reliability and Risk Analysis comprehensively illustrates the Monte Carlo simulation method and its application to reliability and system engineering. Readers are given a sound understanding of the fundamentals of Monte Carlo sampling and simulation and its application for realistic system modeling. Whilst many of the topics rely on a high-level understanding of calculus, probability and statistics, simple academic examples will be provided in support to the explanation of the theoretical foundations to facilitate comprehension of the subject matter. Case studies will be introduced to provide the practical value of the most advanced techniques. This detailed approach makes The Monte Carlo Simulation Method for System Reliability and Risk Analysis a key reference for senior undergraduate and graduate students as well as researchers and practitioners. It provides a powerful tool for all those involved in system analysis for reliability, maintenance and risk evaluations.

This book illustrates a number of modelling and computational techniques for addressing relevant issues in reliability and risk analysis. In particular, it provides: i) a basic illustration of some methods used in reliability and risk analysis for modelling the stochastic failure and repair behaviour of systems, e.g. the Markov and Monte Carlo simulation methods; ii) an introduction to Genetic Algorithms, tailored to their application for RAMS (Reliability, Availability, Maintainability and Safety) optimization; iii) an introduction to key issues of system reliability and risk analysis, like dependent failures and importance measures; and iv) a presentation of the issue of uncertainty and of the techniques of sensitivity and uncertainty analysis used in support of reliability and risk analysis. The book provides a technical basis for senior undergraduate or graduate courses and a reference for researchers and practitioners in the field of reliability and risk analysis. Several practical examples are included to demonstrate the application of the concepts and techniques in practice. This book is also available as a set with An Introduction to the Basics of Reliability and Risk Analysis and Basics of Reliability and Risk Analysis. Foreword Foreword (64k) Sample Chapter(s) Chapter 1: Markov Reliability and Availability Analysis (1,181k) Request Inspection Copy

This book offers a comprehensive overview of recently developed methods for assessing and optimizing system reliability. It consists of two main parts, for treating assessment methods and optimization methods, respectively. The first part covers methods of multi-state system reliability modelling and evaluation, Markov processes, Monte Carlo simulation and uncertainty analysis. The methods considered range from piecewise-deterministic Markov processes to belief function analysis. The second part covers optimization methods of mathematical programming and evolutionary algorithms, and problems of multi-objective optimization and optimization under uncertainty. The methods of this part range from non-dominated sorting genetic algorithm to robust optimization. The book also includes the application of the assessment and optimization methods considered on real case studies, particularly with respect to the reliability assessment and optimization of renewable energy systems, and bridges the gap between theoretical method development and engineering practice.

This book is a comprehensive overview of the recently developed methods for assessing and optimizing system reliability and safety. It consists of two main parts, for assessment and optimization methods, respectively. The former covers multi-state system modelling and reliability evaluation, Markov processes, Monte Carlo simulation and uncertainty treatments under poor knowledge. The reviewed methods range from piecewise-deterministic Markov process to belief functions. The latter covers mathematical programs, evolutionary algorithms, multi-objective optimization and optimization under uncertainty. The reviewed methods range from non-dominated sorting genetic algorithm to robust optimization. This book also includes the applications of the assessment and optimization method on real world cases, particularly for the reliability and safety of renewable energy systems. From this point of view, the book bridges the gap between theoretical development and engineering practice.

Analysis of reliability and risk is an important and integral part of planning, construction and operation of all technical systems. To be able to perform such analyses systematically and scientifically, there is usually a need for special methods and models. This book presents the most important of these. Particular emphasis has been placed on the ideas and the motivation for the use of the various methods and models. It has been an objective to compile a book which provides practising engineers and engineering graduates with the concepts and basic techniques for evaluating reliability and risk. It is hoped that the material presented will make them so familiar with the subject that they can carry out various types of analyses themselves and understand and make use of the more detailed applications and additional material which is available in the journals and publications associated with their own discipline. It has also been an objective to put reliability and risk analyses in context - how such analyses should be used in design and operation of components and systems. The material presented is modern and a large part of the book is at research level. The book focuses on analysis of repairable systems, not only non-repairable systems which have traditionally been given most attention in textbooks on reliability theory. Since most real-life systems are repairable, methods for analysing repairable systems are an important area of research. The book presents general methods, with most applications taken from offshore petro leum activities.

Simulation Methods for Reliability and Availability of Complex Systems discusses the use of computer simulation-based techniques and algorithms to determine reliability and availability (R and A) levels in complex systems. The book: shares theoretical or applied models and decision support systems that make use of simulation to estimate and to improve system R and A levels, forecasts emerging technologies and trends in the use of computer simulation for R and A and proposes hybrid approaches to the development of efficient methodologies designed to solve R and A-related problems in real-life systems. Dealing with practical issues, Simulation Methods for Reliability and Availability of Complex Systems is designed to support managers and system engineers in the improvement of R and A, as well as providing a thorough exploration of the techniques and algorithms available for researchers, and for advanced undergraduate and postgraduate students.

The need for a comprehensive book on probabilistic structural mechanics that brings together the many analytical and computational methods developed over the years and their applications in a wide spectrum of industries-from residential buildings to nuclear power plants, from bridges to pressure vessels, from steel structures to ceramic structures-became evident from the many discussions the editor had with practising engineers, researchers and professors. Because no single individual has the expertise to write a book with such a di.verse scope, a group of 39 authors from universities, research laboratories, and industries from six countries in three continents was invited to write 30 chapters covering the various aspects of probabilistic structural mechanics. The editor and the authors believe that this handbook will serve as a reference text to practicing engineers, teachers, students and researchers. It may also be used as a textbook for graduate-level courses in probabilistic structural mechanics. The editor wishes to thank the chapter authors for their contributions. This handbook would not have been a reality without their collaboration.