This book addresses reliability, maintenance, risk, and safety issues of industrial systems with applications of the latest decision-making techniques. Thus, this book presents chapters that apply advanced tools, techniques, and computing models for optimizing the performance of industrial and manufacturing systems, along with other complex engineering equipment. Computing techniques like data analytics, failure mode and effects analysis, fuzzy set theory, petri-net, multi-criteria decision-making (MCDM), and soft computing are used for solving problems of reliability, risk, and safety related issues.
The necessity of expertise for tackling the complicated and multidisciplinary issues of safety and risk has slowly permeated into all engineering applications so that risk analysis and management has gained a relevant role, both as a tool in support of plant design and as an indispensable means for emergency planning in accidental situations. This entails the acquisition of appropriate reliability modeling and risk analysis tools to complement the basic and specific engineering knowledge for the technological area of application.Aimed at providing an organic view of the subject, this book provides an introduction to the principal concepts and issues related to the safety of modern industrial activities. It also illustrates the classical techniques for reliability analysis and risk assessment used in current practice.
This book addresses reliability, maintenance, risk, and safety issues of industrial systems with applications of the latest decision-making techniques. Thus, this book presents chapters that apply advanced tools, techniques, and computing models for optimizing the performance of industrial and manufacturing systems, along with other complex engineering equipment. Computing techniques like data analytics, failure mode and effects analysis, fuzzy set theory, petri-net, multi-criteria decision-making (MCDM), and soft computing are used for solving problems of reliability, risk, and safety related issues. Discusses reliability, risk, and safety issues of industrial systems with the application of the latest reliability and risk based modelling; Includes a wide range of applications related to reliability and risk modeling of engineering systems; Presents a collection of a diverse range of case studies and uses methods to let readers apply cutting-edge decision making methods to their own projects.
This undergraduate and graduate textbook provides a practical and comprehensive overview of reliability and risk analysis techniques. Written for engineering students and practicing engineers, the book is multi-disciplinary in scope. The new edition has new topics in classical confidence interval estimation; Bayesian uncertainty analysis; models for physics-of-failure approach to life estimation; extended discussions on the generalized renewal process and optimal maintenance; and further modifications, updates, and discussions. The book includes examples to clarify technical subjects and many end of chapter exercises. PowerPoint slides and a Solutions Manual are also available.
“Failure Rate Modeling for Reliability and Risk” focuses on reliability theory, and to the failure rate (hazard rate, force of mortality) modeling and its generalizations to systems operating in a random environment and to repairable systems. The failure rate is one of the crucial probabilistic characteristics for a number of disciplines; including reliability, survival analysis, risk analysis and demography. The book presents a systematic study of the failure rate and related indices, and covers a number of important applications where the failure rate plays the major role. Applications in engineering systems are studied, together with some actuarial, biological and demographic examples. The book provides a survey of this broad and interdisciplinary subject which will be invaluable to researchers and advanced students in reliability engineering and applied statistics, as well as to demographers, econometricians, actuaries and many other mathematically oriented researchers.
This graduate textbook imparts the fundamentals of reliability and risk that can be connected mathematically and applied to problems in engineering and medical science and practice. The book is divided into eight chapters, the first three of which deal with basic fundamentals of probability theory and reliability methods. The fourth chapter illustrates simulation methods needed to solve complex problems. Chapters 5-7 explain reliability codes and system reliability (which uses the component reliabilities discussed in previous chapters). The book concludes in chapter 8 with an examination of applications of reliability within engineering and medical fields. Presenting a highly relevant competency for graduates entering product research and development, or facilities operations sectors, this text includes many examples and end of chapter study questions to maximize student comprehension. Explains concepts of reliability and risk estimation techniques in the context of medicine and engineering; Elucidates the interplay between reliability and risk from design to operation phases; Uses real world examples from engineering structures and medical devices and protocols; Adopts a lucid yet rigorous presentation of reliability and risk calculations; Reinforces students understanding of concepts covered with end-of-chapter exercises.
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.
Tools to Proactively Predict Failure The prediction of failures involves uncertainty, and problems associated with failures are inherently probabilistic. Their solution requires optimal tools to analyze strength of evidence and understand failure events and processes to gauge confidence in a design’s reliability. Reliability Engineering and Risk Analysis: A Practical Guide, Second Edition has already introduced a generation of engineers to the practical methods and techniques used in reliability and risk studies applicable to numerous disciplines. Written for both practicing professionals and engineering students, this comprehensive overview of reliability and risk analysis techniques has been fully updated, expanded, and revised to meet current needs. It concentrates on reliability analysis of complex systems and their components and also presents basic risk analysis techniques. Since reliability analysis is a multi-disciplinary subject, the scope of this book applies to most engineering disciplines, and its content is primarily based on the materials used in undergraduate and graduate-level courses at the University of Maryland. This book has greatly benefited from its authors' industrial experience. It balances a mixture of basic theory and applications and presents a large number of examples to illustrate various technical subjects. A proven educational tool, this bestselling classic will serve anyone working on real-life failure analysis and prediction problems.
This complete resource on the theory and applications of reliability engineering, probabilistic models and risk analysis consolidates all the latest research, presenting the most up-to-date developments in this field. With comprehensive coverage of the theoretical and practical issues of both classic and modern topics, it also provides a unique commemoration to the centennial of the birth of Boris Gnedenko, one of the most prominent reliability scientists of the twentieth century. Key features include: expert treatment of probabilistic models and statistical inference from leading scientists, researchers and practitioners in their respective reliability fields detailed coverage of multi-state system reliability, maintenance models, statistical inference in reliability, systemability, physics of failures and reliability demonstration many examples and engineering case studies to illustrate the theoretical results and their practical applications in industry Applied Reliability Engineering and Risk Analysis is one of the first works to treat the important areas of degradation analysis, multi-state system reliability, networks and large-scale systems in one comprehensive volume. It is an essential reference for engineers and scientists involved in reliability analysis, applied probability and statistics, reliability engineering and maintenance, logistics, and quality control. It is also a useful resource for graduate students specialising in reliability analysis and applied probability and statistics. Dedicated to the Centennial of the birth of Boris Gnedenko, renowned Russian mathematician and reliability theorist
Reliability technology plays an important role in the present era of industrial growth, optimal efficiency, and reducing hazards. This book provides insights into current advances and developments in reliability engineering, and the research presented is spread across all branches. It discusses interdisciplinary solutions to complex problems using different approaches to save money, time, and manpower. It presents methodologies of coping with uncertainty in reliability optimization through the usage of various techniques such as soft computing, fuzzy optimization, uncertainty, and maintenance scheduling. Case studies and real-world examples are presented along with applications that can be used in practice. This book will be useful to researchers, academicians, and practitioners working in the area of reliability and systems assurance engineering. Provides current advances and developments across different branches of engineering. Reviews and analyses case studies and real-world examples. Presents applications to be used in practice. Includes numerous examples to illustrate theoretical results.
