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Historically, the reliability growth process has been thought of, and treated as, a reactive approach to growing reliability based on failures "discovered" during testing or, most unfortunately, once a system/product has been delivered to a customer. As a result, many reliability growth models are predicated on starting the reliability growth process at test time "zero", with some initial level of reliability (usually in the context of a time-based measure such as Mean Time Between Failure (MTBF)). Time "zero" represents the start of testing, and the initial reliability of the test item is based on its inherent design. The problem with this approach, still predominant today, is that it ignores opportunities to grow reliability during the design of a system or product, i.e., opportunities to go into reliability growth testing with a higher initial inherent reliability at time zero. In addition to the traditional approaches to reliability growth during test, this book explores the activities and opportunities that can be leveraged to promote and achieve reliability growth during the design phase of the overall system life cycle. The ability to do so as part of an integrated, proactive design environment has significant implications for developing and delivering reliable items quickly, on time and within budget. This book offers new definitions of how failures can be characterized, and how those new definitions can be used to develop metrics that will quantify how effective a Design for Reliability (DFR) process is in (1) identifying failure modes and (2) mitigating their root failure causes. Reliability growth can only occur in the presence of both elements.
A high percentage of defense systems fail to meet their reliability requirements. This is a serious problem for the U.S. Department of Defense (DOD), as well as the nation. Those systems are not only less likely to successfully carry out their intended missions, but they also could endanger the lives of the operators. Furthermore, reliability failures discovered after deployment can result in costly and strategic delays and the need for expensive redesign, which often limits the tactical situations in which the system can be used. Finally, systems that fail to meet their reliability requirements are much more likely to need additional scheduled and unscheduled maintenance and to need more spare parts and possibly replacement systems, all of which can substantially increase the life-cycle costs of a system. Beginning in 2008, DOD undertook a concerted effort to raise the priority of reliability through greater use of design for reliability techniques, reliability growth testing, and formal reliability growth modeling, by both the contractors and DOD units. To this end, handbooks, guidances, and formal memoranda were revised or newly issued to reduce the frequency of reliability deficiencies for defense systems in operational testing and the effects of those deficiencies. "Reliability Growth" evaluates these recent changes and, more generally, assesses how current DOD principles and practices could be modified to increase the likelihood that defense systems will satisfy their reliability requirements. This report examines changes to the reliability requirements for proposed systems; defines modern design and testing for reliability; discusses the contractor's role in reliability testing; and summarizes the current state of formal reliability growth modeling. The recommendations of "Reliability Growth" will improve the reliability of defense systems and protect the health of the valuable personnel who operate them.
This book integrates key tools and processes into a comprehensive program for developing more robust and reliable technology-based products. Drawing on their extensive product development experience, the authors present a complete process for ensuring product performance throughout the entire lifecycle, from understanding customers’ needs through manufacturing and post-launch support. The authors begin by presenting broad insights and high-level strategies for improving product quality. Next, they demonstrate how to implement robustness and reliability strategies that complement existing governance and decision processes. A section on tools and methods shows how to institutionalize best practices and apply them consistently. Finally, they tie strategies, decisions, and methods together through a case study project. Product developers will learn how to Understand critical drivers of value in technology products, including reliability and durability Implement a process model and roadmap for improving reliability and robustness Increase robustness early in development, leading to shorter cycle times in later phases Improve the stability of production performance under stress conditions Assess both organizational and process capabilities for delivering robust and reliable products Understand and manage customer-driven requirements Use tools including descriptive and inferential statistics and DOE-based empirical models Managers will understand expectations for Design concepts supported by rigorous analyses of alternatives Products and processes delivering higher value to customers Products with higher reliability and longer useful lives Product processes with lower costs and higher capabilities Development projects having shorter, more predictable cycle times Readers are introduced to many thought leaders whose writings can be sources of further learning. This book is a valuable resource for anyone responsible for delivering reliable, profitable technology products, including general managers, program managers, engineers, scientists, and reliability and quality professionals.
This unique volume presents chapters written on the areas of life-testing and reliability by many well-known researchers who have contributed significantly to these two areas over the years. Chapters cover a wide range of topics such as inference under censoring and truncation, reliability growth models, designs to improve quality, prediction techniques, Bayesian analysis of reliability, multivariate methods, accelerated testing, and more. The book is written in an easy-to-follow style, first presenting the necessary theoretical details and then illustrating the methods with a numerical examples wherever possible. Many tables and graphs that are essential for the use of some of the new methodologies are presented throughout the volume. Numerous examples provide the reader with a clear understanding of the methods presented as well as with insight into the applications of these results.
This book comprises high-quality peer-reviewed research papers presented at the 4th International Symposium on Computer Science, Digital Economy and Intelligent Systems (CSDEIS2022), held in Wuhan, China, from November 11–13, 2022, organized jointly by the Wuhan University of Technology, Hubei University of Technology, Wuhan University of Science and Technology, the Polish Operational and Systems Society, and the International Center of Informatics and Computer Science (ICICS). The topics discussed in the book include state-of-the-art papers in computer science and their technological applications; intelligent systems and intellectual approaches; digital economics and educational approaches. It is an excellent source of references for researchers, graduate students, engineers, management practitioners, and undergraduate students interested in computer science and its applications in engineering and management.
This book provides engineers and researchers knowledge to help them in system reliability analysis using machine learning, artificial intelligence, big data, genetic algorithm, information theory, multi-criteria decision making, and other techniques. It will also be useful to students learning reliability engineering. The book brings readers up to date with how system reliability relates to the latest techniques of AI, big data, genetic algorithm, information theory, and multi-criteria decision making and points toward future developments in the subject.
Learn how to model, predict, and manage system reliability/availability throughout the development life cycle Written by a panel of authors with a wealth of industry experience, the methods and concepts presented here give readers a solid understanding of modeling and managing system and software availability and reliability through the development of real applications and products. The modeling and prediction techniques and tools are customer-focused and data-driven, and are also aligned with industry standards (Telcordia, TL 9000, ISO, etc.). Readers will get a clear understanding about what real-world reliability and availability mean through step-by-step discussions of: System availability Conceptual model of reliability and availability Why availability varies between customers Modeling availability Estimating parameters and availability from field data Estimating input parameters from laboratory data Estimating input parameters in the architecture/design stage Prediction accuracy Connecting the dots This book can be used by system architects, engineers, and developers to better understand and manage the reliability/availability of their products; quality engineers to grasp how software and hardware quality relate to system availability; and engineering students as part of a short course on system availability and software reliability.