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Advances in computing hardware and algorithms have dramatically improved the ability to simulate complex processes computationally. Today's simulation capabilities offer the prospect of addressing questions that in the past could be addressed only by resource-intensive experimentation, if at all. Assessing the Reliability of Complex Models recognizes the ubiquity of uncertainty in computational estimates of reality and the necessity for its quantification. As computational science and engineering have matured, the process of quantifying or bounding uncertainties in a computational estimate of a physical quality of interest has evolved into a small set of interdependent tasks: verification, validation, and uncertainty of quantification (VVUQ). In recognition of the increasing importance of computational simulation and the increasing need to assess uncertainties in computational results, the National Research Council was asked to study the mathematical foundations of VVUQ and to recommend steps that will ultimately lead to improved processes. Assessing the Reliability of Complex Models discusses changes in education of professionals and dissemination of information that should enhance the ability of future VVUQ practitioners to improve and properly apply VVUQ methodologies to difficult problems, enhance the ability of VVUQ customers to understand VVUQ results and use them to make informed decisions, and enhance the ability of all VVUQ stakeholders to communicate with each other. This report is an essential resource for all decision and policy makers in the field, students, stakeholders, UQ experts, and VVUQ educators and practitioners.
As computer and communications systems become more complex it becomes increasingly more difficult to analyze their hardware reliability, because simple models may fail to adequately capture subtle but important model features. This paper describes a number of ways we have addressed this problem for analyses based upon White's SURE theorem. We point out how reliability analysis based on SURE mathematics can be extracted from a general C language description of the model behavior, how it can attack very large problems by accepting recomputation in order to reduce memory usage, how such analysis can be parallelized both on multiprocessors and on networks of ordinary workstations, and observe excellent performance gains by doing so. We also discuss how the SURE theorem supports efficient Monte Carlo based estimation of reliability, and show the advantages of the method. Reliability, Markov modeling.
This book covers reliability assessment and prediction of new technologies such as next generation networks that use cloud computing, Network Function Virtualization (NVF), Software Defined Network (SDN), Next Generation Transport, Evolving Wireless Systems, Digital VoIP Telephony, and Reliability Testing techniques specific to Next Generation Networks (NGN). This book introduces the technology to the reader first, followed by advanced reliability techniques applicable to both hardware and software reliability analysis. The book covers methodologies that can predict reliability using component failure rates to system level downtimes. The book’s goal is to familiarize the reader with analytical techniques, tools and methods necessary for analyzing very complex networks using very different technologies. The book lets readers quickly learn technologies behind currently evolving NGN and apply advanced Markov modeling and Software Reliability Engineering (SRE) techniques for assessing their operational reliability. Covers reliability analysis of advanced networks and provides basic mathematical tools and analysis techniques and methodology for reliability and quality assessment; Develops Markov and Software Engineering Models to predict reliability; Covers both hardware and software reliability for next generation technologies.
Complex high-technology devices are in growing use in industry, service sectors, and everyday life. Their reliability and maintenance is of utmost importance in view of their cost and critical functions. This book focuses on this theme and is intended to serve as a graduate-level textbook and reference book for scientists and academics in the field. The chapters are grouped into five complementary parts that cover the most important aspects of reliability and maintenance: stochastic models of reliability and maintenance, decision models involving optimal replacement and repair, stochastic methods in software engineering, computational methods and simulation, and maintenance management systems. This wide range of topics provides the reader with a complete picture in a self-contained volume.
This book highlights recent findings in industrial, manufacturing and mechanical engineering, and provides an overview of the state of the art in these fields, mainly in Russia and Eastern Europe. A broad range of topics and issues in modern engineering are discussed, including the dynamics of machines and working processes, friction, wear and lubrication in machines, surface transport and technological machines, manufacturing engineering of industrial facilities, materials engineering, metallurgy, control systems and their industrial applications, industrial mechatronics, automation and robotics. The book gathers selected papers presented at the 5th International Conference on Industrial Engineering (ICIE), held in Sochi, Russia in March 2019. The authors are experts in various fields of engineering, and all papers have been carefully reviewed. Given its scope, the book will be of interest to a wide readership, including mechanical and production engineers, lecturers in engineering disciplines, and engineering graduates.
The book is a selection of invited chapters, all of which deal with various aspects of mathematical and statistical models and methods in reliability. Written by renowned experts in the field of reliability, the contributions cover a wide range of applications, reflecting recent developments in areas such as survival analysis, aging, lifetime data analysis, artificial intelligence, medicine, carcinogenesis studies, nuclear power, financial modeling, aircraft engineering, quality control, and transportation. Mathematical and Statistical Models and Methods in Reliability is an excellent reference text for researchers and practitioners in applied probability and statistics, industrial statistics, engineering, medicine, finance, transportation, the oil and gas industry, and artificial intelligence.
Reliability is an essential concept in mathematics, computing, research, and all disciplines of engineering, and reliability as a characteristic is, in fact, a probability. Therefore, in this book, the author uses the statistical approach to reliability modelling along with the MINITAB software package to provide a comprehensive treatment of modelling, from the basics through advanced modelling techniques. The book begins by presenting a thorough grounding in the elements of modelling the lifetime of a single, non-repairable unit. Assuming no prior knowledge of the subject, the author includes a guide to all the fundamentals of probability theory, defines the various measures associated with reliability, then describes and discusses the more common lifetime models: the exponential, Weibull, normal, lognormal and gamma distributions. She concludes the groundwork by looking at ways of choosing and fitting the most appropriate model to a given data set, paying particular attention to two critical points: the effect of censored data and estimating lifetimes in the tail of the distribution. The focus then shifts to topics somewhat more difficult: the difference in the analysis of lifetimes for repairable versus non-repairable systems and whether repair truly "renews" the system methods for dealing with system with reliability characteristic specified for more than one component or subsystem the effect of different types of maintenance strategies the analysis of life test data The final chapter provides snapshot introductions to a range of advanced models and presents two case studies that illustrate various ideas from throughout the book.
"Markov modeling has long been accepted as a fundamental and powerful technique for the fault tolerance analysis of mission-critical applications. However, the elaborate computations required have often made Markov modeling too time-consuming to be of practical use on these complex systems. With this hands-on tool, designers can use the Markov modeling technique to analyze safety, reliability, maintainability, and cost-effectiveness factors in the full range of complex systems in use today. Featuring ground-breaking simulation software and a comprehensive reference manual, MARKOV MODELING FOR RELIABILITY ANALYSIS helps system designers surmount the mathematical computations that have previously prevented effective reliability analysis. The text and software compose a valuable self-study tool that is complete with detailed explanations, examples, and a library of Markov models that can be used for experiments and as derivations for new simulation models. The book details how these analyses are conducted, while providing hands-on instruction on how to develop reliability models for the full range of system configurations. Computer-Aided Rate Modeling and Simulation (CARMS) software is an integrated modeling tool that includes a diagram-based environment for model setup, a spreadsheet like interface for data entry, an expert system link for automatic model construction, and an interactive graphic interface for displaying simulation results."
The papers in this volume present theoretical insights and report practical applications both for neural networks, genetic algorithms and evolutionary computation. In the field of natural computing, swarm optimization, bioinformatics and computational biology contributions are no less compelling. A wide selection of contributions report applications of neural networks to process engineering, robotics and control. Contributions also abound in the field of evolutionary computation particularly in combinatorial and optimization problems. Many papers are dedicated to machine learning and heuristics, hybrid intelligent systems and soft computing applications. Some papers are devoted to quantum computation. In addition, kernel based algorithms, able to solve tasks other than classification, represent a revolution in pattern recognition bridging existing gaps. Further topics are intelligent signal processing and computer vision.