Design of Experiments for Reliability Achievement

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

Wiley 2022년 04월 27일 출간

작품소개

책소개

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？ 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

목차

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

작가정보

저자(글) Steven E. Rigdon

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.

저자(글) Rong Pan

저자(글) Douglas C. Montgomery

저자(글) Connie M. Borror