John Wiley & Sons Design of Experiments for Reliability Achievement Cover ENABLES READERS TO UNDERSTAND THE METHODS OF EXPERIMENTAL DESIGN TO SUCCESSFULLY CONDUCT LIFE TESTIN.. Product #: 978-1-119-23769-3 Regular price: $107.48 $107.48 Auf Lager

Design of Experiments for Reliability Achievement

Rigdon, Steven E. / Pan, Rong / Montgomery, Douglas C. / Borror, Connie M.

Wiley Series in Probability and Statistics

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1. Auflage Mai 2022
416 Seiten, Hardcover
Wiley & Sons Ltd

ISBN: 978-1-119-23769-3
John Wiley & Sons

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ENABLES READERS TO UNDERSTAND THE METHODS OF EXPERIMENTAL DESIGN TO SUCCESSFULLY CONDUCT LIFE TESTING TO IMPROVE PRODUCT RELIABILITY

This book illustrates how experimental design and life testing can be used to understand product reliability in order to enable reliability improvements. The book is divided into four sections. The first section focuses on statistical distributions and methods for modeling reliability data. The second section provides an overview of design of experiments including response surface methodology and optimal designs. The third section describes regression models for reliability analysis focused on lifetime data. This section provides the methods for how data collected in a designed experiment can be properly analyzed. The final section of the book pulls together all of the prior sections with customized experiments that are uniquely suited for reliability testing. Throughout the text, there is a focus on reliability applications and methods. It addresses both optimal and robust design with censored data.

To aid in reader comprehension, examples and case studies are included throughout the text to illustrate the key factors in designing experiments and emphasize how experiments involving life testing are inherently different. The book provides numerous state-of-the-art exercises and solutions to help readers better understand the real-world applications of experimental design and reliability. The authors utilize R and JMP(r) software throughout as appropriate, and a supplemental website contains the related data sets.

Written by internationally known experts in the fields of experimental design methodology and reliability data analysis, sample topics covered in the book include:
* An introduction to reliability, lifetime distributions, censoring, and inference for parameter of lifetime distributions
* Design of experiments, optimal design, and robust design
* Lifetime regression, parametric regression models, and the Cox Proportional Hazard Model
* Design strategies for reliability achievement
* Accelerated testing, models for acceleration, and design of experiments for accelerated testing

The text features an accessible approach to reliability for readers with various levels of technical expertise. This book is a key reference for statistical researchers, reliability engineers, quality engineers, and professionals in applied statistics and engineering. It is a comprehensive textbook for upper-undergraduate and graduate-level courses in statistics and engineering.

Preface xxx

PART I RELIABILITY xxx

1 Reliability Concepts xxx

1.1 Definitions of Reliability xxx

1.2 Concepts for Lifetimes xxx

1.3 Censoring xxx

2 Lifetime Distributions xxx

2.1 The Exponential Distribution xxx

2.2 The Weibull Distribution xxx

2.3 The Gamma Distribution xxx

2.4 The Lognormal Distribution xxx

2.5 Log Location and Scale Distributions xxx

2.5.1 The Smallest Extreme Value Distribution xxx

2.5.2 The Logistic and Log Logistic Distributions xxx

3 Inference for Parameters of Life Distributions xxx

3.1 Nonparametric Estimation of the Survival Function xxx

3.1.1 Confidence Bounds for the Survival Function xxx

3.1.2 Estimating the Hazard Function xxx

3.2 Maximum Likelihood Estimation xxx

3.2.1 Censoring Contributions to Likelihoods xxx

3.3 Inference for the Exponential Distribution xxx

3.3.1 Type II Censoring xxx

3.3.2 Type I Censoring xxx

3.3.3 Arbitrary Censoring xxx

3.3.4 Large Sample Approximations xxx

3.4 Inference for the Weibull xxx

3.5 The SEV Distribution xxx

3.6 Inference for Other Models xxx

3.6.1 Inference for the GAM(_; _) Distribution xxx

3.6.2 Inference for the Log Normal Distribution xxx

3.6.3 Inference for the GGAM(_; _; _) Distribution xxx

3.7 Bayesian Inference xxx

PART II DESIGN OF EXPERIMENTS xxx

4 Fundamentals of Experimental Design xxx

4.1 Introduction to Experimental Design xxx

4.2 A Brief History of Experimental Design xxx

4.3 Guidelines for Designing Experiments xxx

4.4 Introduction to Factorial Experiments xxx

4.4.1 An Example xxx

4.4.2 The Analysis of Variance for a Two Factor Factorial xxx

4.5 The 2k Factorial Design xxx

4.5.1 The 22 Factorial Design xxx

4.5.2 The 23 Factorial Design xxx

4.5.3 A Singe Replicate of the 2k Design xxx

4.5.4 2k Designs are Optimal Designs xxx

4.5.5 Adding Center Runs to a 2k Design xxx

4.6 Fractional Factorial Designs xxx

5 Further Principles of Experimental Design xxx

5.1 Introduction xxx

5.2 Response Surface Methods and Designs xxx

5.3 Optimization Techniques in Response Surface Methodology xxx

5.4 Designs for Fitting Response Surfaces xxx

5.4.1 Classical Response Surface Designs xxx

5.4.2 Definitive Screening Designs xxx

5.4.3 Optimal Designs in RSM xxx

PART III REGRESSION MODELS FOR RELIABILITY STUDIES xxx

6 Parametric Regression Models xxx

6.1 Introduction to Failure Time Regression xxx

6.2 Regression Models with Transformations xxx

6.2.1 Estimations and Confidence Interval for Transformed Data xxx

6.3 Generalized Linear Models xxx

6.4 Incorporating Censoring in Regression Models xxx

6.5 Weibull Regression xxx

6.6 Nonconstant Shape Parameter xxx

6.7 Exponential Regression xxx

6.8 The Scale Accelerated Failure Time Model xxx

6.9 Checking Model Assumptions xxx

6.9.1 Residual Analysis xxx

6.9.2 Distribution Selection xxx

7 Semiparametric Regression Models xxx

7.1 The Proportional Hazards Model xxx

7.2 The Cox Proportional Hazards Model xxx

7.3 Inference for the Cox Proportional Hazards Model xxx

7.4 Checking Assumptions for the Cox PH Model xxx

PART IV EXPERIMENTAL DESIGN FOR RELIABILITY STUDIES xxx

8 Design of Single Testing Condition Reliability Experiments xxx

8.1 Life Testing xxx

8.1.1 Life Test Planning with Exponential Distribution xxx

8.1.2 Life Test Planning for Other Lifetime Distributions xxx

8.1.3 Operating Characteristic Curves xxx

8.2 Accelerated Life Testing xxx

8.2.1 Acceleration Factor xxx

8.2.2 Physical Acceleration Models xxx

8.2.3 Relationship Between Physical Acceleration Models and Statistical Models xxx

8.2.4 Planning Single Stress Level ALTs xxx

9 Design of Multi Factor and Multi Level Reliability Experiments xxx

9.1 Implications of Design for Reliability xxx

9.2 Statistical Acceleration Models xxx

9.2.1 Lifetime regression model xxx

9.2.2 Proportional hazard model xxx

9.2.3 Generalized linear model xxx

9.2.4 Converting PH model with right censoring to GLM xxx

9.3 Planning ALTs with Multiple Stress Factors at Multiple Stress Levels xxx

9.3.1 Optimal test plans xxx

9.3.2 Locality of Optimal ALT plans xxx

9.3.3 Comparing Optimal ALT Plans xxx

9.4 Bayesian Design for GLM 400 xxx

9.5 Reliability Experiments with Design and Manufacturing Process Variables xxx

Appendices xxx

A The Survival Package in R xxx

B Design of Experiments using JMP xxx

C The Expected Fisher Information Matrix xxx

D Data Sets xxx

E Distributions Used in Life Testing xxx
Steven E. Rigdon, PhD, is Professor in the Department of Biostatistics at Saint Louis University. He is also Distinguished Research Professor Emeritus at Southern Illinois University Edwardsville. His research interests include spatial disease surveillance and reliability assessment.

Rong Pan, PhD, is Associate Professor of Industrial Engineering at the School of Computing, Informatics, and Decision Systems Engineering at Arizona State University. His research interests include failure time data analysis, design of experiments, multivariate statistical quality control, time series analysis, and control.

Douglas C. Montgomery, PhD, is Regents Professor of Industrial Engineering and ASU Foundation Professor of Engineering at Arizona State University. His research interests include industrial statistics and design of experiments.

Laura J. Freeman, PhD, is Research Associate Professor of Statistics and Director of the Intelligent Systems Division of the National Security Institute at Virginia Tech. Her research interests include design of experiments, leveraging experimental methods in emerging technology research with a focus in cyber-physical systems, artificial intelligence (AI), and machine learning.

S. E. Rigdon, Southern Illinois University; D. C. Montgomery, Georgia Institute of Technology; C. M. Borror, Arizona State University