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Statistical Methods for the Reliability of Repairable Systems [Kõva köide]

4.00/5 (2 hinnangut Goodreads-ist)
(University of Michigan), (Southern Illinois University)
  • Formaat: Hardback, 304 pages, kõrgus x laius x paksus: 242x163x26 mm, kaal: 642 g
  • Sari: Wiley Series in Probability and Statistics
  • Ilmumisaeg: 23-May-2000
  • Kirjastus: Wiley-Interscience
  • ISBN-10: 0471349410
  • ISBN-13: 9780471349419
Teised raamatud teemal:
Statistical Methods for the Reliability of Repairable SystemsSuurem pilt
  • Formaat: Hardback, 304 pages, kõrgus x laius x paksus: 242x163x26 mm, kaal: 642 g
  • Sari: Wiley Series in Probability and Statistics
  • Ilmumisaeg: 23-May-2000
  • Kirjastus: Wiley-Interscience
  • ISBN-10: 0471349410
  • ISBN-13: 9780471349419
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9780471349419.html
Märksõnad:
Rigdon (statistics, Southern Illinois University) and Basu (University of Missouri) offer industry professionals a systematic treatment of probabilistic models used for repairable systems as well as the statistical methods for analyzing data generated from them. The guide explains the difference between repairable and nonrepairable systems and helps develop an understanding of stochastic point processes. Data analysis methods are discussed for both single and multiple systems and include graphical methods, point estimation, interval estimation, hypothesis tests, goodness-of-fit tests, and reliability prediction. No index. Annotation c. Book News, Inc., Portland, OR (booknews.com)

A unique, practical guide for industry professionals who need to improve product quality and reliability in repairable systems

Owing to its vital role in product quality, reliability has been intensely studied in recent decades. Most of this research, however, addresses systems that are nonrepairable and therefore discarded upon failure. Statistical Methods for the Reliability of Repairable Systems fills the gap in the field, focusing exclusively on an important yet long-neglected area of reliability. Written by two highly recognized members of the reliability and statistics community, this new work offers a unique, systematic treatment of probabilistic models used for repairable systems as well as the statistical methods for analyzing data generated from them.

Liberally supplemented with examples as well as exercises boasting real data, the book clearly explains the difference between repairable and nonrepairable systems and helps readers develop an understanding of stochastic point processes. Data analysis methods are discussed for both single and multiple systems and include graphical methods, point estimation, interval estimation, hypothesis tests, goodness-of-fit tests, and reliability prediction. Complete with extensive graphs, tables, and references, Statistical Methods for the Reliability of Repairable Systems is an excellent working resource for industry professionals involved in producing reliable systems and a handy reference for practitioners and researchers in the field.

Arvustused

"This new book does a fantastic job of giving detailed coverage and practical illustration to repairable systems reliability..." (Technometrics, Vol. 42, No. 4, May 2001)

"Intended for engineers, quality managers and statisticians, this book could also be used for a graduate level course in reliability." (Short Book Reviews, Vol. 21, No. 2, August 2001)

"...a thorough and systematic presentation..." (La Doc Sti, September 2000)

"...a comprehensive presentation of materials associated with reliability of repairable systems..." (Journal of Quality Technology, Vol. 33, No. 4, October 2001)

"Most of the literature in reliability deals with modelling and estimation in non-repairable systems; however this book considers repairable systems only." (Zentralblatt MATH, Vol. 963, 2001/13)

"...will be found useful by academicians as well as by practitioners." (Mathematical Reviews, 2002b)

"...an excellent resource and textbook a book that will contribute to filling an existing need in the reliability area and will further motivate researchers to look into developing statistical methods appropriate for data from repairable systems..." (Journal of the American Statistical Association, Vol. 97, No. 458, June 2002)

Preface xi
Terminology and Notation for Repairable Systems
1(32)
Basic Terminology and Examples
1(6)
Nonrepairable Systems
7(16)
The Exponential Distribution
12(4)
The Weibull Distribution
16(3)
Gamma Distribution
19(4)
Basic Theory of Point Processes
23(7)
Overview of Models
30(1)
Exercises
31(2)
Probabilistic Models: The Poisson Process
33(32)
The Poisson Process
33(12)
The Homogeneous Poisson Process
45(8)
Gap Lengths for the HPP
52(1)
The Nonhomogeneous Poisson Process
53(7)
Likelihood Functions
54(4)
Time Truncated Case
58(2)
Exercises
60(5)
Probabilistic Models: Renewal and Other Processes
65(22)
Renewal Process
65(9)
The Piecewise Exponential Model
74(1)
Modulated Processes
75(3)
The Branching Poisson Process
78(4)
Imperfect Repair Models
82(2)
Exercises
84(3)
Analyzing Data from a Single Repairable System
87(94)
Graphical Methods
87(12)
Duane Plots
90(6)
Total Time on Test Plots
96(3)
Nonparametric Methods for Estimating λ
99(7)
Natural Estimates of the Intensity Function
99(1)
Kernel Estimates
100(1)
An Estimate Assuming a Convex Intensity Function
100(1)
Examples
101(5)
Testing for the Homogeneous Poisson Process
106(6)
Inference for the Homogeneous Poisson Process
112(4)
Inference for the Power Law Process: Failure Truncated Case
116(19)
Point Estimation for β and θs;
116(3)
Interval Estimation and Tests of Hypotheses
119(5)
Estimation of the Intensity Function
124(3)
Goodness-of-Fit Tests
127(8)
Statistical Inference for the Time Truncated Case
135(9)
Point Estimation for β and θs;
135(2)
Interval Estimation and Tests of Hypotheses
137(2)
Estimation of the Intensity Function
139(2)
Goodness-of-Fit Tests
141(3)
The Effect of Assuming an HPP when the True Process is a Power Law Process
144(2)
Bayesian Estimation
146(16)
Bayesian Inference for the Parameters of the HPP
148(4)
Bayesian Inference for Predicting the Number of Failures from the HPP
152(2)
Bayesian Inference for the Parameters of the Power Law Process
154(7)
Bayesian Inference for Predicting the Number of Failures
161(1)
Inference for a Modulated Power Law Process
162(6)
Maximum Likelihood Estimation of θs;, β, and κ
162(3)
Hypothesis Tests for the Modulated Power Law Process
165(1)
Confidence Intervals for Parameters
166(1)
An Example
167(1)
Inference for the Piecewise Exponential Model
168(4)
Standards
172(4)
MIL-HDBK-189
172(1)
MIL-HDBK-781 and MIL-STD-781
173(2)
ANSI/IEC/ASQ 61164
175(1)
Other Inference Procedures for Repairable Systems
176(1)
Exercises
177(4)
Analyzing Data from Multiple Repairable Systems
181(48)
Identical Homogeneous Poisson Processes
181(8)
Point Estimation for θs;
182(1)
Interval Estimation for θs;
183(3)
Hypothesis Testing for θs;
186(3)
Nonidentical Homogeneous Poisson Processes
189(4)
Two Failure Truncated Systems
189(2)
κ Systems
191(2)
Parametric Empirical Bayes and Hierarchical Bayes Models for the HPP
193(14)
Parametric Empirical Bayes Models
195(9)
Hierarchical Bayes Models
204(3)
Power Law Process for Identical Systems
207(6)
Testing for the Equality of the Growth Parameters in the Power Law Process
213(5)
Testing Equality of β's for Two Systems
215(2)
Testing Equality of β's for κ Systems
217(1)
Power Law Process for Nonidentical Systems
218(3)
Parametric Empirical Bayes Models for the PLP
221(6)
Exercises
227(2)
Appendix A Tables
229(38)
A.1 Critical Values for the Chi-square Distribution
230(6)
A.2 Critical Values for the F Distribution
236(6)
A.3 Confidence Limits for the Mean of a Poisson Distribution Given an Observation of c Events
242(2)
A.4 Factors for Obtaining a Confidence Interval for the Intensity at the Time of the Last Failure for a Failure Truncated Power Law Process
244(6)
A.5 Factors for Obtaining a Confidence Interval for the Intensity at the Time of the Last Failure for a Time Truncated Power Law Process
250(6)
A.6 Critical Values for the Cramer-von Mises Goodness-of-fit Test
256(4)
A.7 Critical Values for Lilliefors' Goodness-of-fit Test
260(7)
References 267
STEVEN E. RIGDON, PhD, is Professor of Statistics at Southern Illinois University Edwardsville. ASIT P. BASU, PhD, is Professor of Statistics at the University of Missouri Columbia.