Название: Statistical Reliability Engineering: Methods, Models and ApplicationsАвтор: Hoang Pham
Издательство: Springer
Год: 2022
Страниц: 508
Язык: английский
Формат: pdf (true)
Размер: 10.1 MB
This book presents the state-of-the-art methodology and detailed analytical models and methods used to assess the reliability of complex systems and related applications in statistical reliability engineering. It is a textbook based mainly on the author’s recent research and publications as well as experience of over 30 years in this field.
The book covers a wide range of methods and models in reliability, and their applications, including:
statistical methods and model selection for Machine Learning;
models for maintenance and software reliability;
statistical reliability estimation of complex systems; and
statistical reliability analysis of k out of n systems, standby systems and repairable systems.
The topics covered are organized as follows. Chapter 1 provides a fundamental probability and statistics with which some readers are probably already familiar, but to many others may be not. It also discusses basic concepts and measures in reliability include path sets and cut sets, coherent systems, failure rate, mean time to failure, conditional reliability, and mean residual life. Chapter 2 describes most common discrete and continuous distributions such as binomial, Poisson, geometric, exponential, normal, lognormal, gamma, beta, Rayleigh, Weibull, Vtub-shaped hazard rate, etc. and its applications in reliability engineering and applied statistics. The chapter also describes some related statistical characteristics of reliability measures such as bathtub, Vtub shapes, increasing mean residual life, new better than used, etc.
Chapter 3 discusses statistical inference and common estimation techniques include the maximum likelihood, method of moments, least squared, Bayesian methods, and confidence interval estimates. It also discusses the tolerance limit estimates, goodness-of-fit tests, sequential sampling, and model selection criteria. Chapter 4 discusses the reliability modeling and calculations for various systems including series-parallel, parallel-series, k-out-of-n, standby load-sharing, and degradable systems...
Chapter 8 aims to focus on an emergence trend in recent Big Data era in Industry 4.0 and the high demand of applying some statistical methods to various applications in engineering and Machine Learning. This chapter is devoted to the basic concepts of statistical Machine Learning.
Offering numerous examples and solved problems within each chapter, this comprehensive text provides an introduction to reliability engineering graduate students, a reference for data scientists and reliability engineers, and a thorough guide for researchers and instructors in the field.
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