Probability Models In Reliability Analysis


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Probability Models in Reliability Analysis


Probability Models in Reliability Analysis

Author: S. P. Mukherjee

language: en

Publisher:

Release Date: 2025-06-11


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Introduction to Reliability Analysis


Introduction to Reliability Analysis

Author: Shelemyahu Zacks

language: en

Publisher: Springer Science & Business Media

Release Date: 2012-12-06


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Reliability analysis is concerned with the analysis of devices and systems whose individual components are prone to failure. This textbook presents an introduction to reliability analysis of repairable and non-repairable systems. It is based on courses given to both undergraduate and graduate students of engineering and statistics as well as in workshops for professional engineers and scientists. As aresult, the book concentrates on the methodology of the subject and on understanding theoretical results rather than on its theoretical development. An intrinsic aspect of reliability analysis is that the failure of components is best modelled using techniques drawn from probability and statistics. Professor Zacks covers all the basic concepts required from these subjects and covers the main modern reliability analysis techniques thoroughly. These include: the graphical analysis of life data, maximum likelihood estimation and bayesian likelihood estimation. Throughout the emphasis is on the practicalities of the subject with numerous examples drawn from industrial and engineering settings.

Probability Distributions Used in Reliability Engineering


Probability Distributions Used in Reliability Engineering

Author: Andrew N O'Connor

language: en

Publisher: RIAC

Release Date: 2011


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The book provides details on 22 probability distributions. Each distribution section provides a graphical visualization and formulas for distribution parameters, along with distribution formulas. Common statistics such as moments and percentile formulas are followed by likelihood functions and in many cases the derivation of maximum likelihood estimates. Bayesian non-informative and conjugate priors are provided followed by a discussion on the distribution characteristics and applications in reliability engineering.