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Statistical Reliability Engineering [Kõva köide]

, Edited by (QUALCOMM, Inc., San Diego, CA), (Russian Academy of Sciences, Moscow, Russia), (Sotas, Inc., Rockville, MD)
  • Formaat: Hardback, 528 pages, kõrgus x laius x paksus: 243x163x30 mm, kaal: 968 g
  • Ilmumisaeg: 21-May-1999
  • Kirjastus: Wiley-Interscience
  • ISBN-10: 0471123560
  • ISBN-13: 9780471123569
Teised raamatud teemal:
Statistical Reliability EngineeringSuurem pilt
  • Formaat: Hardback, 528 pages, kõrgus x laius x paksus: 243x163x30 mm, kaal: 968 g
  • Ilmumisaeg: 21-May-1999
  • Kirjastus: Wiley-Interscience
  • ISBN-10: 0471123560
  • ISBN-13: 9780471123569
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9780471123569.html
Märksõnad:
This textbook for post-graduate students explains the theory and application of statistical methods for the estimation of reliability based upon tests of individual units of systems. The book also covers mathematical models for analysis, such as Bayesian estimation, accelerated testing, and Monte Carlo simulation. Annotation c. Book News, Inc., Portland, OR (booknews.com)

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.
Preface xv
PART I UNIT RELIABILITY ESTIMATION
Main Knowledge of Statistics
3(57)
Introduction
3(8)
Reliability Indices
3(2)
Main Tasks of mathematical Statistics
5(2)
Sample
7(4)
Main Distributions
11(8)
Continuous Distributions
11(3)
Discrete Distributions
14(3)
Special Distributions
17(2)
Point Estimation
19(13)
Introduction
19(1)
Properties of Estimators
19(6)
Methods of Estimation
25(5)
Sufficient Statistics
30(2)
Confidence Intervals
32(12)
Introduction
32(2)
Construction of Confidence Intervals
34(4)
Confidence Estimation of Exponential distribution
38(1)
Normal Distribution, Known Variance ***2
39(2)
Approximation Based on Levy-Lindeberg Theorem
41(1)
Clopper-Pearson Confidence Intervals
41(1)
Approximation for Binomial Distribution
42(1)
Confidence Interval for Parameter of Poisson Distribution
43(1)
Test of Hypothesis
44(16)
Introduction
44(1)
Two Simple Hypotheses
45(1)
Neymann-Pearson Criterion
46(5)
Sample Size
51(2)
Composit Parametric Hypotheses
53(4)
Problems
57(3)
Plans of Tests with a single Censorship
60(40)
Introduction
60(3)
Test Without Replacement
60(2)
Test Plans with Renewable Units
62(1)
Exponential Distribution
63(10)
Test Plan [ N U r]
63(3)
Test Plan [ N U T]
66(2)
Test Plan [ N U (r, T)]
68(1)
Test Plan [ N R r]
69(1)
Test Plan [ N R T]
70(2)
Plan [ N R (r, T)]
72(1)
Exponential Methods for IFR Distributions
73(9)
Plan [ N U r]
73(6)
Test Plan [ N R T]
79(3)
Estimation of Unit Reliability Indices for General Parametric Case
82(10)
Point Estimate of Reliability Indices
83(2)
Confident Limits for Reliability Indices
85(7)
Nonparametric Confidence Limits for Distribution Function
92(1)
Bootstrap Method of Approximate Confidence Limits
93(7)
Problems
98(2)
Censored Samples
100(45)
lndroduction
100(1)
Independent Random Censorship
100(3)
Markov Model of Censored Testing Without Renewal
103(1)
Nonparametric Estimates of Reliability Function
104(12)
Kaplan-Meier Estimate
104(5)
Discrete Scheme of Testing
109(3)
Unbiased Estimator
112(1)
Nonparametric Estimator of Resource Function
113(3)
Asymptotic Confidence Limits
116(2)
Accurate Confidence Limits
118(4)
More General Models of Censored Tests: Markov Model with Renewal
122(3)
Appendices
125(20)
Markov Moments of Time
125(1)
Markov Censored Test Model Without Renewal
126(1)
Unbiasedness of Estimate (3.12)
127(4)
Proof of Formula (3.18)
131(1)
Nonparametric Confidence Limits for Test Markov Model [ MMU]
132(7)
Consistency of Confidence Limits (3.24) and (3.25)
139(3)
Markov Test Model [ MMR] with Censorship and Renewal
142(1)
Problems
143(2)
Bayes Methods of Reliability Estimation
145(12)
Introduction
145(1)
Point Estimates and Confidence Limits
146(2)
Bayes Point Estimates
146(1)
Sufficient Statistics
147(1)
Bayes Confidence Limits
148(1)
Binomial Model
148(3)
Exponential Model
151(3)
General Parametric Model
154(3)
Problems
156(1)
Accelerated Testing
157(22)
Introduction
157(1)
Basic Notions and Probabilistic Model
158(8)
ITF Distributions and Accelerated-Life Reliability Model
158(1)
Stress Severity in Terms of TTF Distribution
158(1)
Time Transformation Function for the Case of Constant Stress
159(1)
Accelerated-Life Model (Linear Time Transformation Function)
159(1)
Cumulative-Damage Models and Accelerated-Life Model
160(1)
Proportional-Hazards Model
161(1)
Some Popular AL (Reliability) Models for Percentiles
162(1)
Accelerated-Life Model for Time-Dependent Stress
163(1)
AL Reliability Model for Time-Dependent Stress and Miner's Rule
164(2)
Accelerated-Life Test Data Analysis
166(13)
Exploratory Data Analysis (Criteria of Linearity of Time Transformation Function for Constant Stress)
166(2)
Statistical Methods of Reliability Prediction on the Basis of AL Tests with Constant stress
168(8)
Exploratory Data Analysis for Time-Dependent Stress
176(1)
Statistical Estimation of AL Reliability Models on the BASIS of AL Test with Time-Dependent Stress
176(3)
PART II SYSTEM RELIABILITY ESTIMATION
Testing with no Failures
179(29)
Introduction
181(1)
Series System
181(3)
General Expression for Best Lower Confidence Limit
184(2)
Systems with Monotone Structure
184(2)
Best Lower Confidence Limit for No-Failure Test
186(1)
Structures with Convex Cumulative Hazard Function
186(4)
Series Connection of Groups of Identical Units
189(1)
Series-Parallel System with Identical Redundant Units
189(1)
Series Connection of K our of N subsystems
190(1)
Series-Parallel Structure with Different Redundant Units
190(6)
Parallel Connection
192(2)
Parallel-Series System
194(2)
Systems with Complex Structure (Identical Tests)
196(5)
Computation of Confidence Limit for System bases on a Known Limit for Another System
199(2)
Appendix
201(7)
Confidence Clopper-Pearson Limits for Parameter of Binomial Distribution
201(1)
Best Lower Confidence Limit for PFFO in Case of No-Failure Test
202(1)
Maximum of Convex Function on Convex Set
203(2)
Proof of Theorem 6.1
205(1)
Proof of Theorem 6.2
205(1)
Problems
205(3)
System Confidence Limits Based on Unit Test Results
208(74)
Introduction
208(2)
Practical Applications
208(1)
Formulation of the Problem
209(1)
Calculation by Direct Substitution
210(12)
Point Estimates for Units
210(1)
Confidence Limits
211(1)
General Method
212(4)
Arbitrary Distribution of Statistic S
216(6)
Series Structures
222(9)
Binomial Model
222(1)
Lidstrem-Madden Method
223(8)
Parallel Structures
231(6)
System of Different Units
231(3)
System with Replicated Units
234(3)
Series-Parallel System
237(12)
Superreliable System
237(3)
Method of Hyperplane
240(3)
Method of Hypercube
243(1)
Method of Truncated Hypercube
244(2)
Modified Hyperplane Method
246(3)
Approximate Methods
249(15)
Method of Equivalent Tests
249(5)
Method of Reduction
254(1)
Method of Reduction for Complex Systems
255(2)
Method of Fiducial Probabilities
257(4)
Bootstrap Method
261(1)
Normal Approximation
261(3)
Methods of the Use of basic Structures
264(11)
Basic Ensemble of Trivial Algorithms
265(3)
Constructing New Algorithms from the Basis Collection of Trivial Algorithms
268(3)
Constructing the Optimal Algorithm
271(4)
Bayes Method
275(3)
Appendix
278(4)
Derivation of Formulas (7.26) and (7.27) for Confidence Limits in the Arbitrary Distribution of Test Results x and Statistic S(x)
278(2)
Computation of Confidence Limits for Binomial Testing Plan
280(1)
Problems
281(1)
Confidence Limits for Systems Consisting of Units with Exponential Distribution of Time to Failure
282(46)
Introduction
282(1)
Method of Replacement
283(5)
Description of method
283(1)
Conditions of Method Application
284(1)
Systems with Series--Parallel Structure
285(3)
Systems with Complex Structure
288(6)
``Recurrent'' Structures
288(4)
Monotone Structures
292(2)
Systems with Repairable Units
294(6)
System with a Series Structure
294(2)
Series--Parallel System with Loaded Redundancy
296(1)
General Model of Series Connection of Renewal Subsystems
297(3)
Method of Fiducial Probabilities
300(10)
Introduction and Method Description
300(4)
Conditions of Method Correctness
304(1)
Systems with Series--Parallel Structure
304(2)
Systems with Complex Structure
306(1)
Systems with Renewal
307(3)
Method of Tangent Functions
310(4)
Appendix
314(14)
Confidence Limits for Quasi-Convex and Quasi-Concave Functions
314(7)
Construction of Confident Limits by Substitution Method for Function of Type (8.17)
321(2)
Proof of Quasi-Convexity of Reliability Index for Renewable Systems
323(1)
Conditions of Correctness of Method of Fiducial Probabilities for Calculation of Confidence Limits for System PFFO
324(4)
Sequential Criteria of Hypothesis Test and Confidence Limits for Reliability Indices
328(76)
Introduction
328(8)
Sequential Confidence Limits and Hypothesis Test Criteria
328(3)
Sequential Independent and Identical Test
331(5)
Sequential Wald Criterion
336(20)
Introduction
336(8)
Standard Sequential Wald's Plan of Reliability Index Control
344(11)
Truncated Sequential Wald's Plans
355(1)
Sequential Test of Composite Hypotheses for Multidimensional Parameters
356(5)
Sequential Rules of Composite Hypothesis Exclusion
357(2)
Sequential Criterion for Composite Hypothesis Test
359(2)
Sequential Confience Limits
361(6)
Construction of Sequential Confidence Limits
361(4)
Test Reliability Hypotheses with Sequential Confidence Limits
365(1)
Scheme of Dependent Tests
366(1)
Sequential Confidence Limits and Tests of Hypotheses for Systems on the Base of Unit Test
367(20)
Binomial Model
367(7)
General Parametric Model
374(2)
Markov Model of Tests with Censorship and Unit Renewal
376(5)
Sequential Confidence Limits for Availability Coefficient of Renewable Unit
381(6)
Appendix
387(17)
Computation of Accurate Values of Characteristics of Sequential Test Plans
387(4)
Wald's Equivalency
391(1)
Sequential Optimal Test Criterion for Composite Hypotheses
392(9)
Scheme of Dependent Tests
401(3)
Monte Carlo Simulation
404(21)
Nature and Purpose
404(1)
General Description of a Discrete Simulation Model
405(1)
Detailed Example of Algorithmic Description
406(4)
Random Numbers
410(2)
Sample Computer Simulation
412(1)
Modeling Network Reliability
413(12)
Simulating with Fixed Failed States
418(2)
Modeling Link Failures Until System Failure
420(2)
Accelerated Simulation
422(3)
Monte Carlo Simulation for Optimal Redundancy
425(28)
Introductory Remarks
425(1)
Formulation of Optimal Redundancy Problems
426(1)
Algorithm for Trajectory Generation
427(2)
Description of the idea of the Solution
429(2)
Solution of Direct Optimization Problem
431(7)
Inverse Optimization Problem
438(5)
Appendix
443(10)
Consistency and Asymptotic Unbiasedness of solution to Direct Problem
443(5)
Consistency of Inverse Problem of Optimal Redundancy
448(2)
Restrictions on System MTBF
450(3)
Appendix 453(6)
Bibliography 459(10)
Solutions to Problems 469(28)
Index 497


BORIS GNEDENKO (deceased) was the chairman of the Probability Theory Department of Moscow State University in Russia. IGOR PAVLOV is Professor of Mathematics at Moscow Technical University in Russia. IGOR USHAKOV is Principal Engineer at QUALCOMM, Inc. in San Diego, California.