Recent developments in reliability engineering has become the most challenging and demanding area of research. Modeling and Simulation, along with System Reliability Engineering has become a greater issue because of high-tech industrial processes, using more complex systems today. This book gives the latest research advances in the field of modeling and simulation, based on analysis in engineering sciences. Features Focuses on the latest research in modeling and simulation based analysis in reliability engineering. Covers performance evaluation of complex engineering systems Identifies and fills the gaps of knowledge pertaining to engineering applications Provides insights on an international and transnational scale Modeling and Simulation Based Analysis in Reliability Engineering aims at providing a reference for applications of mathematics in engineering, offering a theoretical sound background with adequate case studies, and will be of interest to researchers, practitioners, and academics.

Recent developments in reliability engineering has become the most challenging and demanding area of research. Modeling and Simulation, along with System Reliability Engineering has become a greater issue because of high-tech industrial processes, using more complex systems today. This book gives the latest research advances in the field of modeling and simulation, based on analysis in engineering sciences. Features Focuses on the latest research in modeling and simulation based analysis in reliability engineering. Covers performance evaluation of complex engineering systems Identifies and fills the gaps of knowledge pertaining to engineering applications Provides insights on an international and transnational scale Modeling and Simulation Based Analysis in Reliability Engineering aims at providing a reference for applications of mathematics in engineering, offering a theoretical sound background with adequate case studies, and will be of interest to researchers, practitioners, and academics.

This new book will include the original, unpublished research studies and articles from the Modeling and Simulation Reliability 2018 conference organized by the editor. The chapters, along with case studies, will describe the latest research and developments and will be of interest to researchers, pratitioners, and academics.

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.

Gas and Oil Reliability Engineering: Modeling and Analysis, Second Edition, provides the latest tactics and processes that can be used in oil and gas markets to improve reliability knowledge and reduce costs to stay competitive, especially while oil prices are low. Updated with relevant analysis and case studies covering equipment for both onshore and offshore operations, this reference provides the engineer and manager with more information on lifetime data analysis (LDA), safety integrity levels (SILs), and asset management. New chapters on safety, more coverage on the latest software, and techniques such as ReBi (Reliability-Based Inspection), ReGBI (Reliability Growth-Based Inspection), RCM (Reliability Centered Maintenance), and LDA (Lifetime Data Analysis), and asset integrity management, make the book a critical resource that will arm engineers and managers with the basic reliability principles and standard concepts that are necessary to explain their use for reliability assurance for the oil and gas industry. - Provides the latest tactics and processes that can be used in oil and gas markets to improve reliability knowledge and reduce costs - Presents practical knowledge with over 20 new internationally-based case studies covering BOPs, offshore platforms, pipelines, valves, and subsea equipment from various locations, such as Australia, the Middle East, and Asia - Contains expanded explanations of reliability skills with a new chapter on asset integrity management, relevant software, and techniques training, such as THERP, ASEP, RBI, FMEA, and RAMS

The development of new and effective analytical and numerical models is essential to understanding the performance of a variety of structures. As computational methods continue to advance, so too do their applications in structural performance modeling and analysis. Modeling and Simulation Techniques in Structural Engineering presents emerging research on computational techniques and applications within the field of structural engineering. This timely publication features practical applications as well as new research insights and is ideally designed for use by engineers, IT professionals, researchers, and graduate-level students.

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.

In the industry of manufacturing and design, one major constraint has been enhancing operating performance using less time. As technology continues to advance, manufacturers are looking for better methods in predicting the condition and residual lifetime of electronic devices in order to save repair costs and their reputation. Intelligent systems are a solution for predicting the reliability of these components; however, there is a lack of research on the advancements of this smart technology within the manufacturing industry. AI Techniques for Reliability Prediction for Electronic Components provides emerging research exploring the theoretical and practical aspects of prediction methods using artificial intelligence and machine learning in the manufacturing field. Featuring coverage on a broad range of topics such as data collection, fault tolerance, and health prognostics, this book is ideally designed for reliability engineers, electronic engineers, researchers, scientists, students, and faculty members seeking current research on the advancement of reliability analysis using AI.

Safety and Reliability Modeling and Its Applications combines work by leading researchers in engineering, statistics and mathematics who provide innovative methods and solutions for this fast-moving field. Safety and reliability analysis is one of the most multidimensional topics in engineering today. Its rapid development has created many opportunities and challenges for both industrialists and academics, while also completely changing the global design and systems engineering environment. As more modeling tasks can now be undertaken within a computer environment using simulation and virtual reality technologies, this book helps readers understand the number and variety of research studies focusing on this important topic. The book addresses these important recent developments, presenting new theoretical issues that were not previously presented in the literature, along with solutions to important practical problems and case studies that illustrate how to apply the methodology. Uses case studies from industry practice to explain innovative solutions to real world safety and reliability problems Addresses the full interdisciplinary range of topics that influence this complex field Provides brief introductions to important concepts, including stochastic reliability and Bayesian methods