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Statistical Reliability Engineering

  • ID: 2220074
  • Book
  • May 1999
  • Region: Global
  • 528 Pages
  • John Wiley and Sons Ltd
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Proven statistical reliability analysis methods–available for the first time to engineers in the West While probabilistic methods of system reliability analysis have reached an unparalleled degree of refinement, Russian engineers have concentrated on developing more advanced statistical methods. Over the past several decades, their efforts have yielded highly evolved statistical models that have proven to be especially valuable in the estimation of reliability based upon tests of individual units of systems. Now Statistical Reliability Engineering affords engineers a unique opportunity to learn both the theory behind and applications of those statistical methods. Written by three leading innovators in the field, Statistical Reliability Engineering:

∗ Covers all mathematical models for statistical reliability analysis, including Bayesian estimation, accelerated testing, and Monte Carlo simulation

∗ Focuses on the estimation of various measures of system reliability based on the testing of individual units

∗ Contains new theoretical results available for the first time in print

∗ Features numerous examples demonstrating practical applications of the theory presented

Statistical Reliability Engineering is an important professional resource for reliability and design engineers, especially those in the telecommunications and electronics industries. It is also an excellent course text for advanced courses in reliability engineering.
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Main Knowledge of Statistics.

Plans of Tests with a Single Censorship.

Censored Samples.

Bayes Methods of Reliability Estimation.

Accelerated Testing.


Testing with No Failures.

System Confidence Limits Based on Unit Test Results.

Confidence Limits for Systems Consisting of Units with Exponential Distribution of Time to Failure.

Sequential Criteria of Hypothesis Test and Confidence Limits for Reliability Indices.

Monte Carlo Simulation.

Monte Carlo Simulation for Optimal Redundancy.



Solutions to Problems.

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Boris Gnedenko
Igor V. Pavlov
Igor A. Ushakov
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