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About 10,000 papers for “Reliability engineering”

Reliability Engineering

M. Lazzaroni, Loredana Cristaldi, Lorenzo Peretto et al. · 2011

Cited by 64

Reliability Engineering

Xabier Larrucea, Fabien Belmonte, Adam Welc et al. · IEEE Software · 2017

Reliability engineering dates back to reliability studies in the 20th century; since then, various models have been defined and used. Software engineering plays a key role from several viewpoints, but the main concern is that we're moving toward a more connected world, including enterprises and mobile devices. The three articles in this special issue illustrate current trends in this domain.

Cited by 12Open access

Reliability Engineering

Vonta Ilia, Ram Mangey · 2018

Cited by 18

Reliability Engineering

Kailash C. Kapur, Michael Pecht · 2014

An Integrated Approach to Product DevelopmentReliability Engineering presents an integrated approach to the design, engineering, and management of reliability activities throughout the life cycle of a product, including concept, research and development, design, manufacturing, assembly, sales, and service. Containing illustrative guides that include worked problems, numerical examples, homework problems, a solutions manual, and class-tested materials, it demonstrates to product development and manufacturing professionals how to distribute key reliability practices throughout an organization.The authors explain how to integrate reliability methods and techniques in the Six Sigma process and Design for Six Sigma (DFSS). They also discuss relationships between warranty and reliability, as well as legal and liability issues. Other topics covered include:Reliability engineering in the 21st CenturyProbability life distributions for reliability analysisProcess control and process capabilityFailure modes, mechanisms, and effects analysisHealth monitoring and prognosticsReliability tests and reliability estimationReliability Engineering provides a comprehensive list of references on the topics covered in each chapter. It is an invaluable resource for those interested in gaining fundamental knowledge of the practical aspects of reliability in design, manufacturing, and testing. In addition, it is useful for implementation and management of reliability programs.

Cited by 0

Reliability Engineering

Karan Sotoodeh · 2023

Cited by 1

Reliability Engineering

Edgar Bradley · 2016

Reliability Engineering – A Life Cycle Approach is based on the author’s knowledge of systems and their problems from multiple industries, from sophisticated, first class installations to less sophisticated plants often operating under severe budget constraints and yet having to deliver first class availability. Taking a practical approach and drawing from the author’s global academic and work experience, the text covers the basics of reliability engineering, from design through to operation and maintenance. Examples and problems are used to embed the theory, and case studies are integrated to convey real engineering experience and to increase the student’s analytical skills. Additional subjects such as failure analysis, the management of the reliability function, systems engineering skills, project management requirements and basic financial management requirements are covered. Linear programming and financial analysis are presented in the context of justifying maintenance budgets and retrofits. The book presents a stand-alone picture of the reliability engineer’s work over all stages of the system life-cycle, and enables readers to: Understand the life-cycle approach to engineering reliability Explore failure analysis techniques and their importance in reliability engineering Learn the skills of linear programming, financial analysis, and budgeting for maintenance Analyze the application of key concepts through realistic Case Studies This text will equip engineering students, engineers and technical managers with the knowledge and skills they need, and the numerous examples and case studies include provide insight to their real-world application. An Instructor’s Manual and Figure Slides are available for instructors.

Cited by 32

Reliability Engineering

Ram, Mangey 1980- · 2019

Cited by 12

“Reliability” engineering

Microelectronics Reliability · 1963

Cited by 0

Reliability Engineering

Youchao Sun, Longbiao Li, Dmytro Tiniakov · 2023

This textbook covers the fundamentals of reliability theory and its application for engineering processes, especially for aircraft units and systems.

Cited by 5Open access

Reliability Engineering

M Tichý · Topics in safety, reliability and quality · 1993

Cited by 22

Reliability Engineering

Lazzaroni, Massimo · CERN Document Server (European Organization for Nuclear Research) · 2012

Cited by 0

Reliability engineering

Markov Chain Monte Carlo · 2011

Estimation of small failure probabilities is one of the most important and challenging computational problems in reliability engineering.The failure probability is usually given by an integral over a highdimensional uncertain parameter space that is difficult to evaluate numerically.This paper focuses on enhancements to Subset Simulation (SS), proposed by Au and Beck, which provides an efficient algorithm based on MCMC (Markov chain Monte Carlo) simulation for computing small failure probabilities for general high-dimensional reliability problems.First, we analyze the Modified Metropolis algorithm (MMA), an MCMC technique, which is used in SS for sampling from high-dimensional conditional distributions.The efficiency and accuracy of SS directly depends on the ergodic properties of the Markov chains generated by MMA, which control how fast the chain explores the parameter space.We present some observations on the optimal scaling of MMA for efficient exploration, and develop an optimal scaling strategy for this algorithm when it is employed within SS.Next, we provide a theoretical basis for the optimal value of the conditional failure probability p 0 , an important parameter one has to choose when using SS.We demonstrate that choosing any p 0 2 [0.1, 0.3] will give similar efficiency as the optimal value of p 0 .Finally, a Bayesian post-processor SS+ for the original SS method is developed where the uncertain failure probability that one is estimating is modeled as a stochastic variable whose possible values belong to the unit interval.Simulated samples from SS are viewed as informative data relevant to the system's reliability.Instead of a single real number as an estimate, SS+ produces the posterior PDF of the failure probability, which takes into account both prior information and the information in the sampled data.This PDF quantifies the uncertainty in the value of the failure probability and it may be further used in risk analyses to incorporate this uncertainty.To demonstrate SS+, we consider its application to two different reliability problems: a linear reliability problem and reliability analysis of an elasto-plastic structure subjected to strong seismic ground motion.The relationship between the original SS and SS+ is also discussed.

Cited by 210