Reliability: Modeling, Prediction, and Optimization

Bringing 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

Spis treści:
CONTEXT OF RELIABILITY ANALYSIS.
An Overview.
Illustrative Cases and Data Sets.
BASIC RELIABILITY METHODOLOGY.
Collection and Preliminary Analysis of Failure Data.
Probability Distributions for Modeling Time to Failure.
Basic Statistical Methods for Data Analysis.
RELIABILITY MODELING, ESTIMATION, AND PREDICTION.
Modeling Failures at the Compon ent Level.
Modeling and Analysis of Multicomponent Systems.
Advanced Statistical Methods for Data Analysis.
Software Reliability.
Design of Experiments and Analysis of Variance.
Model Selection and Validation.
RELIABILITY MANAGEMENT, IMPROVEMENT, AND OPTIMIZATION.
Reliability Management.
Reliability Engineering.
Reliability Prediction and Assessment.
Reliability Improvement.
Maintenance of Unreliable Systems.
Warranties and Service Contracts.
Reliability Optimization.
EPILOGUE.
Case Studies.
Resource Materials.
Appendices.
References.
Indexes.

Nota biograficzna:
WALLACE R. BLISCHKE, PhD, Statistics, is Professor Emeritus, Marshall School of Business, University of Southern California.
D. N. PRABHAKAR MURTHY, PhD, is Professor of Engineering and Operations Management at the University of Queensland, St. Lucia, Australia.

Okładka tylna:
Bringing 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 lif e 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