Multi State System Reliability Analysis And Optimization

Multi-state System Reliability Analysis and Optimization for Engineers and Industrial Managers presents a comprehensive, up-to-date description of multi-state system (MSS) reliability as a natural extension of classical binary-state reliability. It presents all essential theoretical achievements in the field, but is also practically oriented. New theoretical issues are described, including: • combined Markov and semi-Markov processes methods, and universal generating function techniques; • statistical data processing for MSSs; • reliability analysis of aging MSSs; • methods for cost-reliability and cost-availability analysis of MSSs; and • main definitions and concepts of fuzzy MSS. Multi-state System Reliability Analysis and Optimization for Engineers and Industrial Managers also discusses life cycle cost analysis and practical optimal decision making for real world MSSs. Numerous examples are included in each section in order to illustrate mathematical tools. Besides these examples, real world MSSs (such as power generating and transmission systems, air-conditioning systems, production systems, etc.) are considered as case studies. Multi-state System Reliability Analysis and Optimization for Engineers and Industrial Managers also describes basic concepts of MSS, MSS reliability measures and tools for MSS reliability assessment and optimization. It is a self-contained study resource and does not require prior knowledge from its readers, making the book attractive for researchers as well as for practical engineers and industrial managers.

Most books on reliability theory are devoted to traditional binary reliability models allowing for only two possible states for a system and its components: perfect functionality and complete failure. However, many real-world systems are composed of multi-state components, which have different performance levels and several failure modes with various effects on the entire system performance (degradation). Such systems are called Multi-State Systems (MSS). The examples of MSS are power systems where the component performance is characterized by the generating capacity, computer systems where the component performance is characterized by the data processing speed, communication systems, etc.This book is the first to be devoted to Multi-State System (MSS) reliability analysis and optimization. It provides a historical overview of the field, presents basic concepts of MSS, defines MSS reliability measures, and systematically describes the tools for MSS reliability assessment and optimization. Basic methods for MSS reliability assessment, such as a Boolean methods extension, basic random process methods (both Markov and semi-Markov) and universal generating function models, are systematically studied. A universal genetic algorithm optimization technique and all details of its application are described. All the methods are illustrated by numerical examples. The book also contains many examples of application of reliability assessment and optimization methods to real engineering problems.The aim of this book is to give a comprehensive, up-to-date presentation of MSS reliability theory based on modern advances in this field and provide a theoretical summary and examples of engineering applications to a variety of technical problems. From this point of view the book bridges the gap between theoretical advances and practical reliability engineering.

Many real systems are composed of multi-state components with different performance levels and several failure modes. These affect the whole system's performance. Most books on reliability theory cover binary models that allow a system only to function perfectly or fail completely. "The Universal Generating Function in Reliability Analysis and Optimization" is the first book that gives a comprehensive description of the universal generating function technique and its applications in binary and multi-state system reliability analysis. Features: - an introduction to basic tools of multi-state system reliability and optimization; - applications of the universal generating function in widely used multi-state systems; - examples of the adaptation of the universal generating function to different systems in mechanical, industrial and software engineering. This monograph will be of value to anyone interested in system reliability, performance analysis and optimization in industrial, electrical and nuclear engineering.

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.

Recent Advances in System Reliability discusses developments in modern reliability theory such as signatures, multi-state systems and statistical inference. It describes the latest achievements in these fields, and covers the application of these achievements to reliability engineering practice. The chapters cover a wide range of new theoretical subjects and have been written by leading experts in reliability theory and its applications. The topics include: concepts and different definitions of signatures (D-spectra), their properties and applications to reliability of coherent systems and network-type structures; Lz-transform of Markov stochastic process and its application to multi-state system reliability analysis; methods for cost-reliability and cost-availability analysis of multi-state systems; optimal replacement and protection strategy; and statistical inference. Recent Advances in System Reliability presents many examples to illustrate the theoretical results. Real world multi-state systems, such as power generation and transmission, refrigeration, and production systems, are considered in the form of case studies, making the book a useful resource for researchers and postgraduate students.

As our modern information-age society grows in complexity both in terms of embedded systems and applications, the problems and challenges in reliability become ever more complex. Bringing together many of the leading experts in the field, this volume presents a broad picture of current research on system modeling and optimization in reliability and its applications.The book comprises twenty-three chapters organized into four parts: Reliability Modeling, Software Quality Engineering, Software Reliability, and Maintenance and Inspection Policies. These sections cover a wide range of important topics, including system reliability modeling, optimization, software reliability and quality, maintenance theory and inspection, reliability failure analysis, sampling plans and schemes, software development processes and improvement, stochastic process modeling, statistical distributions and analysis, fault-tolerant performance, software measurements and cost effectiveness, queueing theory and applications, system availability, reliability of repairable systems, testing sampling inspection, software capability maturity model, accelerated life modeling, statistical control, and HALT testing.

This book presents original studies describing the latest research and developments in the area of reliability and systems engineering. It helps the reader identifying gaps in the current knowledge and presents fruitful areas for further research in the field. Among others, this book covers reliability measures, reliability assessment of multi-state systems, optimization of multi-state systems, continuous multi-state systems, new computational techniques applied to multi-state systems and probabilistic and non-probabilistic safety assessment.

This book addresses a modern topic in reliability: multi-state and continuous-state system reliability, which has been intensively developed in recent years. It offers an up-to-date overview of the latest developments in reliability theory for multi-state systems, engineering applications to a variety of technical problems, and case studies that will be of interest to reliability engineers and industrial managers. It also covers corresponding theoretical issues, as well as case studies illustrating the applications of the corresponding theoretical advances. The book is divided into two parts: Modern Mathematical Methods for Multi-state System Reliability Analysis (Part 1), and Applications and Case Studies (Part 2), which examines real-world multi-state systems. It will greatly benefit scientists and researchers working in reliability, as well as practitioners and managers with an interest in reliability and performability analysis. It can also be used as a textbook or as a supporting text for postgraduate courses in Industrial Engineering, Electrical Engineering, Mechanical Engineering, Applied Mathematics, and Operations Research.

Complex System Reliability presents a state-of-the-art treatment of complex multi-channel system reliability assessment and provides the requisite tools, techniques and algorithms required for designing, evaluating and optimizing ultra-reliable redundant systems. Critical topics that make Complex System Reliability a unique and definitive resource include: • redundant system analysis for k-out-of-n systems (including complex systems with embedded k-out-of-n structures) involving both perfect and imperfect fault coverage; • imperfect fault coverage analysis techniques, including algorithms for assessing the reliability of redundant systems in which each element is subject to a given coverage value (element level coverage) or in which the system uses voting to avoid the effects of a failed element (fault level coverage); and • state-of-the-art binary decision diagram analysis techniques, including the latest and most efficient algorithms for the reliability assessment of large, complex redundant systems. This practical presentation includes numerous fully worked examples that provide detailed explanations of both the underlying design principles and the techniques (such as combinatorial, recursive and binary decision diagram algorithms) used to obtain quantitative results. Many of the worked examples are based on the design of modern digital fly-by-wire control system technology. Complex System Reliability provides in-depth coverage of systems subject to either perfect or imperfect fault coverage and also the most recent techniques for correctly assessing the reliability of redundant systems that use mid-value-select voting as their primary means of redundancy management. It is a valuable resource for those involved in the design and reliability assessment of highly reliable systems, particularly in the aerospace and automotive sectors.