Overview
- Defines and studies selected aging intensity functions that are used to gauge various aspects of aging trends
- Reviews the research on Lifetime Analysis by Aging Intensity Functions carried out in the past several years
- Offers a valuable reference guide for reliability researchers and practitioners alike
- Presents a number of basic continuous and discrete, univariate and bivariate lifetime distributions
Part of the book series: Studies in Systems, Decision and Control (SSDC, volume 196)
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Table of contents (8 chapters)
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Front Matter
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Classic Aging Intensity Functions
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Front Matter
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Generalized Aging Intensity Functions
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Front Matter
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Back Matter
Keywords
About this book
This book addresses a range of aging intensity functions, which make it possible to measure and compare aging trends for lifetime random variables. Moreover, they can be used for the characterization of lifetime distributions, also with bounded support. Stochastic orders based on the aging intensities, and their connections with some other orders, are also discussed.
To demonstrate the applicability of aging intensity in reliability practice, the book analyzes both real and generated data. The estimated, properly chosen, aging intensity function is mainly recommended to identify data’s lifetime distribution, and secondly, to estimate some of the parameters of the identified distribution. Both reliability researchers and practitioners will find the book a valuable guide and source of inspiration.Authors and Affiliations
Bibliographic Information
Book Title: Lifetime Analysis by Aging Intensity Functions
Authors: Magdalena Szymkowiak
Series Title: Studies in Systems, Decision and Control
DOI: https://doi.org/10.1007/978-3-030-12107-5
Publisher: Springer Cham
eBook Packages: Intelligent Technologies and Robotics, Intelligent Technologies and Robotics (R0)
Copyright Information: Springer Nature Switzerland AG 2020
Hardcover ISBN: 978-3-030-12106-8Published: 25 February 2019
Softcover ISBN: 978-3-030-12109-9Published: 15 August 2020
eBook ISBN: 978-3-030-12107-5Published: 01 February 2019
Series ISSN: 2198-4182
Series E-ISSN: 2198-4190
Edition Number: 1
Number of Pages: XV, 215
Number of Illustrations: 24 b/w illustrations
Topics: Computational Intelligence, Data Engineering, Engineering Mathematics