A textbook for a graduate course in reliability engineering, designed to be used after Kececioglu's Reliability Engineering Handbook and Reliability and Life Testing Handbook. The first of two volumes presenting a full spectrum of preventive maintenance strategies for industrial equipment, along with the analytical tools for choosing the most appropriate ones. No bibliography. Annotation copyright by Book News, Inc., Portland, OR
Preventive maintenance engineering can significantly contribute to productivity and cost-reduction in any industry dependent upon machinery and equipment. This handbook provides a comprehensive guide to advanced strategies and procedures for this vital function.
This handbook studies the combination of various methods of designing for reliability, availability, maintainability and safety, as well as the latest techniques in probability and possibility modeling, mathematical algorithmic modeling, evolutionary algorithmic modeling, symbolic logic modeling, artificial intelligence modeling and object-oriented computer modeling.
Expanding on the coverage provided in Volume 1, this volume covers the prediction of equipment and system reliability for the series, parallel, standby, and conditional function configuration cases and discusses the prediction of the reliability of complex components, equipment, and systems with multimode function and logic, among others.
Practical Approaches to Reliability Theory in Cutting-Edge Applications Probabilistic Reliability Models helps readers understand and properly use statistical methods and optimal resource allocation to solve engineering problems. The author supplies engineers with a deeper understanding of mathematical models while also equipping mathematically oriented readers with a fundamental knowledge of the engineeringrelated applications at the center of model building. The book showcases the use of probability theory and mathematical statistics to solve common, real-world reliability problems. Following an introduction to the topic, subsequent chapters explore key systems and models including: • Unrecoverable objects and recoverable systems • Methods of direct enumeration • Markov models and heuristic models • Performance effectiveness • Time redundancy • System survivability • Aging units and their related systems • Multistate systems Detailed case studies illustrate the relevance of the discussed methods to real-world technical projects including software failure avalanches, gas pipelines with underground storage, and intercontinental ballistic missile (ICBM) control systems. Numerical examples and detailed explanations accompany each topic, and exercises throughout allow readers to test their comprehension of the presented material. Probabilistic Reliability Models is an excellent book for statistics, engineering, and operations research courses on applied probability at the upper-undergraduate and graduate levels. The book is also a valuable reference for professionals and researchers working in industry who would like a mathematical review of reliability models and the relevant applications.
A fine blend of the three disciplines, viz. quality, reliability and maintainability, this book provides a clear understanding of the concepts and discusses their applications using statistical tools and techniques. The concepts are critically assessed and explained to enable their use for management decision-making. The book describes many current topics such as six sigma, capability maturity model integration (CMMI), process data management, reliability system models, repairable system models, maintainability assessment and design and testing concepts. It is intended as a textbook for the undergraduate students of Mechanical Engineering and Production and Industrial Engineering. The book will also be useful to the postgraduate students of Applied Statistics, Quality and Reliability, and Quality and Productivity Management as well as to the management and engineering professionals. KEY FEATURES : Provides charts and plots to explain the concepts discussed. Gives an account of most recent developments. Gives illustrations of practical situations where tools can be applied immediately. Interspersed with plenty of worked-out examples to reinforce the concepts. Includes chapter-end exercises to drill the students in self-study.
Includes the binomial tests of comparison and information on Accept-Reject Tests, the Sequential Probability Ratio Test, Bayesian MTBF and Reliability Demonstration Tests, as well as other important accelerated tests such as Arrhenius, Eyriing, Bazovsky, and Inverse Power Law.
