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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.
This book is intended for students and practitioners who have had a calculus-based statistics course and who have an interest in safety considerations such as reliability, strength, and duration-of-load or service life. Many persons studying statistical science will be employed professionally where the problems encountered are obscure, what should be analyzed is not clear, the appropriate assumptions are equivocal, and data are scant. In this book there is no disclosure with many of the data sets what type of investigation should be made or what assumptions are to be used.
This compact and easy-to-understand text presents the underlying principles and practice of reliability engineering and life testing. It describes the various techniques available for reliability analysis and prediction and explains the statistical methods necessary for reliability modelling, analysis and estimation. The text also discusses in detail the concepts of life testing, its classification and methodologies as well as accelerated life tests, the methodologies and models of stress related failure rates evaluation, and data analysis. Besides, it elaborates on the principles, methods and equipment of highly accelerated life testing and highly accelerated stress screening. Finally, the book concludes with a discussion on the parametric as well as non-parametric methods generally used for reliability estimation, and the recent developments in life testing of engineering components. Key Features The book is up-to-date and very much relevant to the present industrial, research, design, and development scenarios. Provides adequate tools to predict the system reliability at the design stage, to plan and conduct life testing on the products at various stages of development, and to use the life test and field data to estimate the product reliability. Gives sufficiently large number of worked-out examples. Primarily intended as a textbook for the postgraduate students of engineering (M.Tech., Reliability Engineering), the book would also be quite useful for reliability practitioners, professional engineers, and researchers.
As the Lead Reliability Engineer for Ford Motor Company, Guangbin Yang is involved with all aspects of the design and production of complex automotive systems. Focusing on real-world problems and solutions, Life Cycle Reliability Engineering covers the gamut of the techniques used for reliability assurance throughout a product's life cycle. Yang pulls real-world examples from his work and other industries to explain the methods of robust design (designing reliability into a product or system ahead of time), statistical and real product testing, software testing, and ultimately verification and warranting of the final product's reliability
Probabilistic models; Basic statistical inference; The exponential distribution; The weibull distribution; The gamma distribution; Extreme-value distribution; The logistic and other distribution; Goodness-of-fit tests.
This book is intended for students and practitioners who have had a calculus-based statistics course and who have an interest in safety considerations such as reliability, strength, and duration-of-load or service life. Many persons studying statistical science will be employed professionally where the problems encountered are obscure, what should be analyzed is not clear, the appropriate assumptions are equivocal, and data are scant. In this book there is no disclosure with many of the data sets what type of investigation should be made or what assumptions are to be used.
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.
Striking a balance between the use of computer-aided engineering practices and classical life testing, this reference expounds on current theory and methods for designing reliability tests and analyzing resultant data through various examples using Microsoft® Excel, MINITAB, WinSMITH, and ReliaSoft software across multiple industries. The book disc
This textbook reviews the methodologies of reliability prediction as currently used in industries such as electronics, automotive, aircraft, aerospace, off-highway, farm machinery, and others. It then discusses why these are not successful; and, presents methods developed by the authors for obtaining accurate information for successful prediction. The approach is founded on approaches that accurately duplicate the real world use of the product. Their approach is based on two fundamental components needed for successful reliability prediction; first, the methodology necessary; and, second, use of accelerated reliability and durability testing as a source of the necessary data. Applicable to all areas of engineering, this textbook details the newest techniques and tools to achieve successful reliabilityprediction and testing. It demonstrates practical examples of the implementation of the approaches described. This book is a tool for engineers, managers, researchers, in industry, teachers, and students. The reader will learn the importance of the interactions of the influencing factors and the interconnections of safety and human factors in product prediction and testing.