Practical Applications of Bayesian Reliability

Practical Applications of Bayesian Reliability

Author: Yan Liu

Publisher: John Wiley & Sons

Published: 2019-05-28

Total Pages: 324

ISBN-13: 1119287979

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Demonstrates how to solve reliability problems using practical applications of Bayesian models This self-contained reference provides fundamental knowledge of Bayesian reliability and utilizes numerous examples to show how Bayesian models can solve real life reliability problems. It teaches engineers and scientists exactly what Bayesian analysis is, what its benefits are, and how they can apply the methods to solve their own problems. To help readers get started quickly, the book presents many Bayesian models that use JAGS and which require fewer than 10 lines of command. It also offers a number of short R scripts consisting of simple functions to help them become familiar with R coding. Practical Applications of Bayesian Reliability starts by introducing basic concepts of reliability engineering, including random variables, discrete and continuous probability distributions, hazard function, and censored data. Basic concepts of Bayesian statistics, models, reasons, and theory are presented in the following chapter. Coverage of Bayesian computation, Metropolis-Hastings algorithm, and Gibbs Sampling comes next. The book then goes on to teach the concepts of design capability and design for reliability; introduce Bayesian models for estimating system reliability; discuss Bayesian Hierarchical Models and their applications; present linear and logistic regression models in Bayesian Perspective; and more. Provides a step-by-step approach for developing advanced reliability models to solve complex problems, and does not require in-depth understanding of statistical methodology Educates managers on the potential of Bayesian reliability models and associated impact Introduces commonly used predictive reliability models and advanced Bayesian models based on real life applications Includes practical guidelines to construct Bayesian reliability models along with computer codes for all of the case studies JAGS and R codes are provided on an accompanying website to enable practitioners to easily copy them and tailor them to their own applications Practical Applications of Bayesian Reliability is a helpful book for industry practitioners such as reliability engineers, mechanical engineers, electrical engineers, product engineers, system engineers, and materials scientists whose work includes predicting design or product performance.


Bayesian Reliability

Bayesian Reliability

Author: Michael S. Hamada

Publisher: Springer Science & Business Media

Published: 2008-08-15

Total Pages: 445

ISBN-13: 0387779507

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Bayesian Reliability presents modern methods and techniques for analyzing reliability data from a Bayesian perspective. The adoption and application of Bayesian methods in virtually all branches of science and engineering have significantly increased over the past few decades. This increase is largely due to advances in simulation-based computational tools for implementing Bayesian methods. The authors extensively use such tools throughout this book, focusing on assessing the reliability of components and systems with particular attention to hierarchical models and models incorporating explanatory variables. Such models include failure time regression models, accelerated testing models, and degradation models. The authors pay special attention to Bayesian goodness-of-fit testing, model validation, reliability test design, and assurance test planning. Throughout the book, the authors use Markov chain Monte Carlo (MCMC) algorithms for implementing Bayesian analyses -- algorithms that make the Bayesian approach to reliability computationally feasible and conceptually straightforward. This book is primarily a reference collection of modern Bayesian methods in reliability for use by reliability practitioners. There are more than 70 illustrative examples, most of which utilize real-world data. This book can also be used as a textbook for a course in reliability and contains more than 160 exercises. Noteworthy highlights of the book include Bayesian approaches for the following: Goodness-of-fit and model selection methods Hierarchical models for reliability estimation Fault tree analysis methodology that supports data acquisition at all levels in the tree Bayesian networks in reliability analysis Analysis of failure count and failure time data collected from repairable systems, and the assessment of various related performance criteria Analysis of nondestructive and destructive degradation data Optimal design of reliability experiments Hierarchical reliability assurance testing


Frontiers in Reliability

Frontiers in Reliability

Author: Asit P. Basu

Publisher: World Scientific

Published: 1998

Total Pages: 512

ISBN-13: 9789810233600

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This volume presents recent results in reliability theory by leading experts in the world. It will prove valuable for researchers, and users of reliability theory. It consists of refereed invited papers on a broad spectrum of topics in reliability. The subjects covered include Bayesian reliability, Bayesian reliability modeling, confounding in a series system, DF tests, Edgeworth approximation to reliability, estimation under random censoring, fault tree reduction for reliability, inference about changes in hazard rates, information theory and reliability, mixture experiment, mixture of Weibull distributions, queuing network approach in reliability theory, reliability estimation, reliability modeling, repairable systems, residual life function, software spare allocation systems, stochastic comparisons, stress-strength models, system-based component test plans, and TTT-transform.


Software Reliability Modelling

Software Reliability Modelling

Author: M. Xie

Publisher: World Scientific

Published: 1991

Total Pages: 232

ISBN-13: 9789810206406

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This book summarizes the recent advances in software reliability modelling. Almost all the existing models are classified and the most interesting models are described in detail.Because of the application of software in many industrial, military and commercial systems, software reliability has become an important research area. Although there are many models and results appeared in different journals and conference proceedings, there is a lack of systematic publications on this subject. The aim of this book is to provide an overview of this area and provide software reliability researchers and analysts with a systematic study of the existing results. This book can also be used as a reference book for other software engineers and reliability theoreticians interested in this area.


Bayesian Networks for Reliability Engineering

Bayesian Networks for Reliability Engineering

Author: Baoping Cai

Publisher: Springer

Published: 2019-02-28

Total Pages: 257

ISBN-13: 9811365164

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This book presents a bibliographical review of the use of Bayesian networks in reliability over the last decade. Bayesian network (BN) is considered to be one of the most powerful models in probabilistic knowledge representation and inference, and it is increasingly used in the field of reliability. After focusing on the engineering systems, the book subsequently discusses twelve important issues in the BN-based reliability methodologies, such as BN structure modeling, BN parameter modeling, BN inference, validation, and verification. As such, it is a valuable resource for researchers and practitioners in the field of reliability engineering.


Mathematical and Statistical Models and Methods in Reliability

Mathematical and Statistical Models and Methods in Reliability

Author: V.V. Rykov

Publisher: Springer Science & Business Media

Published: 2010-11-02

Total Pages: 465

ISBN-13: 0817649719

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The book is a selection of invited chapters, all of which deal with various aspects of mathematical and statistical models and methods in reliability. Written by renowned experts in the field of reliability, the contributions cover a wide range of applications, reflecting recent developments in areas such as survival analysis, aging, lifetime data analysis, artificial intelligence, medicine, carcinogenesis studies, nuclear power, financial modeling, aircraft engineering, quality control, and transportation. Mathematical and Statistical Models and Methods in Reliability is an excellent reference text for researchers and practitioners in applied probability and statistics, industrial statistics, engineering, medicine, finance, transportation, the oil and gas industry, and artificial intelligence.


Safety and Reliability Modeling and Its Applications

Safety and Reliability Modeling and Its Applications

Author: Mangey Ram

Publisher: Elsevier

Published: 2021-08-15

Total Pages: 433

ISBN-13: 0128233249

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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


Modern Statistical and Mathematical Methods in Reliability

Modern Statistical and Mathematical Methods in Reliability

Author: Alyson G. Wilson

Publisher: World Scientific

Published: 2005

Total Pages: 430

ISBN-13: 9812563563

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This volume contains extended versions of 28 carefully selected and reviewed papers presented at The Fourth International Conference on Mathematical Methods in Reliability in Santa Fe, New Mexico, June 21-25, 2004, the leading conference in reliability research. A broad overview of current research activities in reliability theory and its applications is provided with coverage on reliability modeling, network and system reliability, Bayesian methods, survival analysis, degradation and maintenance modeling, and software reliability. The contributors are all leading experts in the field and include the plenary session speakers, Tim Bedford, Thierry Duchesne, Henry Wynn, Vicki Bier, Edsel Pena, Michael Hamada, and Todd Graves.