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Reliability: Probabilistic Models and Statistical Methods de Lawrence Leemis

Reliability: Probabilistic Models and Statistical Methods

Autor Lawrence Leemis
en Limba Engleză Paperback – 19 dec 1994
Appropriate for a one-semester advanced undergraduate or a graduate level course in reliability engineering.
An elementary introduction to the probabilistic models and statistical methods used by reliability engineers as applied to, for example, electrical or mechanical systems. Leemis offers explanations of how the mathematical models and results apply to engineering design and the analysis of lifetime data sets, with simple, supplementary proofs and derivations provided when necessary.
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Specificații

ISBN-13: 9780137205172
ISBN-10: 0137205171
Pagini: 288
Dimensiuni: 178 x 235 x 19 mm
Greutate: 0.97 kg
Ediția:1
Editura: Pearson Education
Colecția Prentice Hall
Locul publicării:Upper Saddle River, United States

Cuprins



 1. Introduction.


 2. Coherent Systems Analysis.


 3. Lifetime Distributions.


 4. Parametric Lifetime Models.


 5. Specialized Models.


 6. Repairable Systems.


 7. Lifetime Data Analysis.


 8. Parametric Estimation for Models without Covariates.


 9. Parametric Estimation for Models with Covariates.


10. Nonparametric Methods and Model Adequacy.

Caracteristici

  • eases students' difficulty in differentiating between probabilistic models and statistical methods by clearly separating coverage of each topic.
  • offers many examples and exercises that have closed-form answers for the sake of brevity and simplicity, but assures students that this will not always be the case when performing a reliability analysis.
  • keeps parameterizations consistent throughout, but exposes students to these differences by parameterizing the exponential distribution by its failure rate in Chapters 4 and 8, and by its mean in Chapter 7.
  • covers models, methods, and data sets that apply equally well to all three areas of application—actuarial science, biostatistics, and reliability engineering.
  • keeps number of data sets used in the text to a minimum to insure that students do not get the techniques and the data sets confused.