Reliability: Modeling, Prediction, and OptimizationBringing together business and engineering to reliability analysis With manufactured products exploding in numbers and complexity, reliability studies play an increasingly critical role throughout a product's entire life cycle-from design to post-sale support. Reliability: Modeling, Prediction, and Optimization presents a remarkably broad framework for the analysis of the technical and commercial aspects of product reliability, integrating concepts and methodologies from such diverse areas as engineering, materials science, statistics, probability, operations research, and management. Written in plain language by two highly respected experts in the field, this practical work provides engineers, operations managers, and applied statisticians with both qualitative and quantitative tools for solving a variety of complex, real-world reliability problems. A wealth of examples and case studies accompanies: * Comprehensive coverage of assessment, prediction, and improvement at each stage of a product's life cycle * Clear explanations of modeling and analysis for hardware ranging from a single part to whole systems * Thorough coverage of test design and statistical analysis of reliability data * A special chapter on software reliability * Coverage of effective management of reliability, product support, testing, pricing, and related topics * Lists of sources for technical information, data, and computer programs * Hundreds of graphs, charts, and tables, as well as over 500 references |
Contents
Example 1 | 5 |
6 Electric Power System | 12 |
Illustrative Cases and Data Sets | 64 |
Collection and Preliminary Analysis of Failure Data | 67 |
31 | 92 |
Probability Distributions for Modeling Time to Failure | 93 |
Basic Statistical Methods for Data Analysis | 135 |
RELIABILITY MODELING ESTIMATION AND PREDICTION | 169 |
Reliability Prediction and Assessment | 511 |
Reliability Improvement | 537 |
Maintenance of Unreliable Systems | 559 |
Warranties and Service Contracts | 589 |
Reliability Optimization | 619 |
Case Studies | 661 |
Resource Materials | 693 |
Appendix A Probability | 725 |
Modeling and Analysis of Multicomponent Systems | 201 |
Advanced Statistical Methods for Data Analysis | 243 |
Software Reliability | 287 |
Design of Experiments and Analysis of Variance | 319 |
13 | 328 |
18 | 340 |
Model Selection and Validation | 375 |
RELIABILITY MANAGEMENT IMPROVEMENT | 425 |
Reliability Engineering | 467 |
Appendix B Introduction to Stochastic Processes | 735 |
Statistical Tables | 749 |
Basic Results on Stochastic Optimization | 763 |
References | 771 |
Notes | 777 |
Exercises | 790 |
797 | |
805 | |
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Common terms and phrases
analyzed ANOVA applications approach assume asymptotic basic Bayesian block bond strength buyer calculated censoring Chapter complex component confidence interval context cycle data sets decreases defective denote density function discussed Distribution Equation distribution function engineering error estimate Example expected experiment exponential distribution factors failed items failure distribution failure modes failure rate fault tree fractiles gamma distribution given in Table hardware increases interaction Inverse Gaussian Distribution involves lognormal distribution maintenance manufacturer mean Minitab MTTF Murthy nonconforming normal distribution Note number of failures obtained operational optimal prediction prior distribution probability plot problems random variable redundancy regression reliability analysis repair replacement sample scale parameter Section software reliability specific standard deviation statistical strategies stress structure subsystem system reliability t₁ tion tolerance intervals treatment values variance warranty cost warranty period Weibull distribution