Advances in Reliability Science – serie

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

Reliability Analysis and Asset Management of Engineering Systems explains methods that can be used to evaluate reliability and availability of complex systems, including simulation-based methods.

The increasing digitization of mechanical processes driven by Industry 4.0 increases the interaction between machines and monitoring and control systems, leading to increases in system complexity. For those systems the reliability and availability analyses are increasingly challenging, as the interaction between machines has become more complex, and the analysis of the flexibility of the production systems to respond to machinery failure may require advanced simulation techniques.

This book fills a gap on how to deal with such complex systems by linking the concepts of systems reliability and asset management, and then making these solutions more accessible to industry by explaining the availability analysis of complex systems based on simulation methods that emphasise Petri nets.

Explains how to use a monitoring database to perform important tasks including an update of complex systems reliability Shows how to diagnose probable machinery-based causes of system performance degradation by using a monitoring database and reliability estimates in an integrated way Describes practical techniques for the application of AI and machine learning methods to fault detection and diagnosis problems

Reliability and Maintenance of Multi-indenture Systems analyzes and discusses the optimization problems of the multi-indenture system, providing theoretical and practical insights for reliability and maintenance engineers and managers. The book combines computer simulation, operational research, reliability engineering and numerical optimization to solve practical reliability problems. After a thorough introduction to multi-indenture systems and the reliability problems associated with them, the general framework of the simulation system is proposed and explained, along with more detailed explanations of the applications.Complex hierarchical system structures have become more common in applications such as rail, aircraft and weaponry due to increasing requirements for flexibility and functionality. As a consequence, it has become increasingly difficult to use analytical methods to calculate and improve system reliability and availability of these large complex systems. However, recent research has provided proven methodologies for the application of simulation systems to tackle problems including redundancy optimization in the design phase, optimal maintenance, and spare parts provisioning in the operation phase, hence this book is an ideal contribution to the conversation.

Reliable and Resilient Logistic Systems signposts the risks associated with common logistics systems and provides cutting-edge preventive and rapid response strategies to counter them. Today’s dynamically changing market conditions create an unstable and unpredictable environment for enterprises to operate in, which is not catered to by traditional approaches to logistics planning and organization. In response, this ground-breaking book explains how to create resilient logistic networks that can adapt quickly to changes in operational conditions, thus leading to more reliable supply. Adopting a process orientation, the authors consider a range of issues related to continuous improvement and sidestep the organizational boundaries that can stand in the way of process optimization. Their interdisciplinary approach addresses both technical and management aspects of logistics system performance, thus reflecting the complexity of the challenges targeted.Describes each logistics system comprehensively with a relevant industry case studyCombines management and risk and reliability approaches to provide an interdisciplinary view of complex logistics problemsDescribed a practical framework for logistics decision-makers

Engineering Reliability and Risk Assessment explains how to improve the performance of a system using the latest risk and reliability models. Against a backdrop of increasing availability of industrial data, and ever-increasing global commercial competition, the standards for optimal efficiency with minimum hazards keep improving. Topics explained include Effective strategies for the maintenance of the mechanical components of a system, How to schedule necessary interventions throughout the product life cycle, How to understand the structure and cost of complex systems, Planning a schedule to improve the reliability and life of the system, software, system safety and risk informed asset management, and more. Uses case studies from industry practice to explain innovative solutions to real world risk assessment problems Addresses the full interdisciplinary range of topics that influence this complex field Provides brief introductions to important concepts, including risk and reliability analysis and fuzzy reliability

Reliability Modeling with Industry 4.0 explores the emerging theoretical and practical developments in reliability engineering in highly digitized industries, including power, computer systems, railway systems, and robotics. Drawing on leading research from around the globe, as well as the latest in industry practice, this book provides cutting edge advice on how to integrate a fully digitized industry 4.0 system for enhanced reliability and reduced maintenance cost. Technologies such as big data, artificial intelligence, and the industrial internet of things are addressed in the context of reliability engineering, providing practical advice on applications. Provides innovative reliability modeling tools related to the application of Industry 4.0 technologies Includes case studies from industries such as rail, energy, and computer systems Describes techniques for the successful digital transformation of industries for sophisticated reliability systems

Modern systems have become increasingly complex to design and build, while the demand for reliability and cost-effective enhancement continues. Robust international competition has further intensified the need for all designers, managers, practitioners, scientists, and engineers to ensure a level of reliability of their products and processes before release at the lowest cost.Developments in Reliability Engineering equips its audience with the necessary information to keep up with the latest original research and state-of-the-art advances in reliability engineering. The volume offers an excursus from historical theories and methods to the present-world practical utility of these concepts with worked-out examples.

Guides readers through reliability topics from an historical perspective to new research results, advancements, and latest developmentsDraws on the authors’ experience of reliability analysis in a range of industries and disciplines, showing the need for reliability from the product design stage right through to aftercareProvides methods throughout, making this title a good source of actionable information

Reliability and Resilience in the Internet of Things explains the latest advances in reliability and resilience modeling, analysis, and design techniques for Internet of Things (IoT) systems. Over the last decade, IoT has developed rapidly, and it now spans diverse application domains such as healthcare, home automation, smart manufacturing, smart ocean, and smart agriculture. Due to the critical nature of these applications, it is imperative that the IoT systems operate reliably throughout the intended mission time. This timely book provides state-of the-art coverage on IoT reliability and resilience modeling, analysis, and design methods and solutions to help prevent costly malfunctions. From the viewpoint of engineers, researchers, and developers, reliability analysis and resilience design are key to the deployment and operation of IoT systems in critical applications, and this book contains the best advice on the subject available. It is a valuable resource for researchers in academia and industry interested in smart IoT systems and Industry 4.0.

Addresses several IoT applications with case studiesExplores solutions in the contexts of IoT-layered architecture as well as cross-layer interactions and dependenciesExplains fundamentals of IoT technology in terms of reliability and resilience

Dynamic Modelling of Time-to-Event Processes covers an alternative dynamic modelling approach for studying time-to-event processes. This innovative approach covers some key elements, including the Development of continuous-time state of dynamic time-to-event processes, an Introduction of an idea of discrete-time dynamic intervention processes, Treating a time-to-event process operating/functioning under multiple time-scales formulation of continuous and discrete-time interconnected dynamic system as hybrid dynamic time-to-event process, Utilizing Euler-type discretized schemes, developing theoretical dynamic algorithms, and more.Additional elements of this process include an Introduction of conceptual and computational state and parameter estimation procedures, Developing multistage a robust mean square suboptimal criterion for state and parameter estimation, and Extending the idea conceptual computational simulation process and applying real datasets.

Presents a dynamic approach which does not require a closed-form survival/reliability distributionProvides updates that are independent of existing Maximum likelihood, Bayesian, and Nonparametric methodsApplies to nonlinear and non-stationary interconnected large-scale dynamic systemsIncludes frailty and other models in survival analysis as case studies

Reliability Assessment and Optimization of Complex Systems delves into a range of tools and techniques for designing optimized complex systems. Each chapter explores system modeling and the implementation of various metaheuristics for optimization purposes. This book provides readers in the domain of applied mathematics with a comprehensive understanding of system reliability analysis and improvement, thereby offering substantial value to their knowledge and expertise. System reliability has become the paramount attribute of any production unit. The process of maximizing system reliability while adhering to multiple constraints is referred to as reliability optimization.There are two primary approaches to enhancing a system's performance and reliability: developing a product with reduced failures (failure avoidance) or incorporating resilience to ensure the system continues functioning even in the event of a failure (fault tolerance).Explains the process and application of reliability-based design optimizationCovers many metaheuristic approaches such as reliability, cost, and the MTTF of the systemProvides the workings and applications of multi-objective optimizations

Risk, Reliability and Resilience in Operations Management examines measurement tools and techniques and their real-world application. The book provides a resource that is needed to help solve complex business operations and global supply chains and their important requirements for the accurate measurement of risk, reliability, and resilience to inform decisions and reduce risk. In addition, the book discusses advancements in technology and data analytics, with final sections covering the COVID-19 pandemic and how it has put greater emphasis on the importance of risk, reliability, and resilience in business operations.This book provides a timely overview of measurement techniques and their application in operations management, offering insights into future directions in this field.

Provides a comprehensive overview on the measurement of risk, reliability, and resilience in operations managementDelves into the practical application of risk, reliability, and resilience management techniques in real-world scenarios, providing case studies and examples that demonstrate how businesses can effectively measure and manage these factors to make informed decisionsExplores emerging trends, technological advancements, and potential developments that may impact risk measurement, reliability, and resilience

Stochastic Modeling and Statistical Methods: Advances and Applications is the practical guide to the latest developments in data analysis and research methods. The book explores the significant research progress that has been seen in recent decades, offering vital tools for analyzing modern applications and real data. Topics covered include Dynamic Reliability, Stochastic Modeling, System Maintainability, and Parametric, Semi-Parametric, and Nonparametric Statistical Inference. Readers will find the latest advancements in these areas, making it an essential resource for researchers and practitioners who want to explore these evolving fields and stay updated on cutting-edge research.Presents the latest breakthroughs in Reliability Engineering, along with current perspectives on the fieldIncludes shared, practical knowledge of contemporary statistical modeling techniques, thus enhancing analytical skillsCovers the probabilistic methods used to investigate various applications in Reliability Engineering

Reliability Analysis and Modeling for Complex Systems is a crucial resource for engineers and technologists grappling with modern challenges. As technology advances and safety concerns mount, the complexity of systems like autonomous vehicles and critical infrastructure demands innovative reliability assessment methods. This book bridges theory and practice, offering practical solutions for professionals navigating the intricate world of reliability engineering. Through real-world case studies and interdisciplinary insights, it equips readers to address the multifaceted challenges of ensuring dependability in today's interconnected technological landscape.Dives deeply into advanced probabilistic modeling and analysis techniques tailored for complex systems. This content addresses the needs of reliability engineers and researchers who seek to apply cutting-edge methods to their projectsIncludes interdisciplinary perspectives, such as human factors and cyber-physical systems, which is essential for addressing the real-world challenges the target audience faces. This approach will help readers tackle complex system reliability from multiple anglesReal-world case studies that bridge theory and practice, helping practitioners and academics understand how to apply reliability analysis to complex systems in various domains

Reliability Assessment of Flow Networks and Industrial Systems: Bridging Theory and Applications explores real-life flow networks and industrial systems, employing various methods, including both exact and approximation techniques, to assess the reliability of these systems. Through numerous examples, the book explains algorithms, ensuring readers gain a deep understanding of the discussed approaches. The complexity results for these algorithms are computed and compared with alternative methods. Furthermore, the book presents experimental results tied to specific methods, providing a comprehensive and practical understanding throughout.Explores the modeling of diverse real-world systems through binary/multistate flow networksIntroduces the fundamentals of binary network reliability, providing a comprehensive understanding and exposure to various assessment methodsDelves into the intricacies of stochastic/multistate flow networks, emphasizing reliability concepts and offering familiarity with multiple assessment methods

Reliability in Green Engineering: Ensuring Environmental Compliance is a comprehensive guide that helps engineers, environmental professionals, and policymakers navigate the complexities of sustainability and regulatory compliance in engineering practice. This book begins by laying out the foundational concepts of green engineering principles and the importance of reliability in achieving environmental goals. It then delves into topics such as renewable energy systems, eco-friendly design methodologies, life cycle analysis, and green technologies. It equips readers with the knowledge and tools to effectively integrate reliability and environmental compliance into their engineering practices.Provides a thorough exploration of key concepts in green engineering, reliability, and environmental compliance, offering readers a solid understanding of the principles underpinning sustainable engineering practicesProvides practical insights and strategies for integrating reliability into green engineering projects, including case studies and real-world examples that illustrate successful approaches to ensuring environmental complianceProvides guidance on navigating complex regulatory frameworks and ensuring compliance with environmental standards, helping readers mitigate legal risks and ensure the long-term viability of their projects

In an era where global economic development demands innovation, reliability engineering stands at the forefront of modern industrial challenges. Over the past 50 years, profound advancements in reliability analysis have transformed how engineers and academics approach system performance, offering significant economic benefits and improving quality of life worldwide. K-out-of-n Structures: Theories, Methods and Applications delves into the extensive research surrounding k-out-of-n structures, crucial for modeling diverse engineering systems-from telecom microwave stations to oil pipelines and electron accelerator vacuum systems. This comprehensive volume is designed for professionals, students and researchers eager to deepen their understanding of reliability engineering. Authored by leading scientists, mathematicians, and engineers, this book brings together current research and novel approaches.Focuses on the latest research in the field of k-out-of-n system reliability engineering applicationsInsights on an international and transnational scaleIdentifies the knowledge gaps in k-out-of-n systems applicationsIncludes recent research findings that are occurring in this field in different institutions

Soft Computing and Artificial Intelligence in System Safety and Reliability Analysis: Recent Advances and Perspectives provides comprehensive and easy-to-understand information in three main fields of safety and reliability including “AI in reliability and safety science”, “soft computing Applications in safety analysis” and “Operation and Maintenance”. The book's content thoroughly and succinctly addresses a variety of safety and dependability-related topics. The book's contents will be easily understood by everyone, even those with little prior knowledge of the subject, due to its straightforward writing style. Every topic addressed in the book is complemented by illustrations, which aid with conceptual understanding. Using this book, all audiences will learn about different types of recent theoretical and methodological advances in ensemble AI-driven metaheuristic optimization algorithms and their applications in all aspects of safety and reliability, different types of soft computing, as well as hybridization of metaheuristics with other soft computing and artificial intelligence tools, and would be able to perform operations like designing, identifying, choosing the best AI-driven algorithms for the safety and reliability analysis, and maintenance optimization. The main advantage of this book is that all the required and recent knowledge of safety and reliability is provided in one place without the need to read several books and papers which have been written professionally and are therefore hard to understand especially for those who are interested in increasing the knowledge on safety and reliability but have no prior background in this field. Moreover, this book informs the reader about each aspect of heuristic and/or metaheuristic techniques and the applications thereof in the context of the most recent advances in science and engineering and to provide insight into possible future developments in the field of safety and reliability.Comprehensive information on soft computing and artificial intelligence in different aspects of safety, reliability, and maintenanceIntroductory knowledge on safety, reliability, and maintenance, then applications of soft computing, and artificial intelligence in safety, and reliability, and finally maintenance optimizationAll the required information on safety, reliability, and maintenance in one place in a simple, comprehensive, and compact way

Advanced Queueing and Reliability Engineering: Theory, Optimization, and Applications combines queueing theory, reliability engineering, and optimization techniques, offering a balanced view of both theory and practical application. It focuses on engineering applications, real-world case studies, and computational tools, making it relevant for academics and industry practitioners alike. By incorporating interdisciplinary approaches like AI-driven reliability modeling and uncertainty analysis, the book not only presents existing solutions but also outlines future research avenues. Structured for progressive learning, it begins with fundamental concepts and advances to sophisticated techniques, ensuring accessibility for graduate students and early-career researchers while providing depth for seasoned scholars. Key topics include Markovian and non-Markovian systems, breakdown modeling, and reliability engineering, with chapters supported by case studies from various sectors, including telecommunications, healthcare, and aviation. This book emphasizes optimization techniques, including metaheuristics and classical methods such as dynamic programming, to enhance system performance. Additionally, it includes an introductory chapter on probability, statistics, and optimization to equip readers with essential knowledge. Overall, this resource is invaluable for those looking to navigate the complexities of queueing and reliability models in modern engineering contexts.Balanced coverage of advanced queueing and reliability models, carefully selecting key methods while avoiding unnecessary complexity. The book maintains depth while avoiding unnecessary complexityFocus on practical applications, with case studies from telecommunications, healthcare, manufacturing, and IT demonstrating how queueing and reliability models solve real-world challenges such as network congestion, predictive maintenance, and service optimizationCarefully structured content that balances breadth and depth, ensuring a focused and coherent exploration of key topics without overwhelming the readerReal-world case studies with step-by-step implementation guidance, industry best practices, and decision-making insights relevant to professionals in telecommunications, healthcare, manufacturing, and service industries