Design of Experiments for Reliability Achievement (Wiley Series in Probability and Statistics)

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.

這本書旨在幫助讀者理解實驗設計的方法，以成功進行壽命測試，從而提高產品的可靠性。書籍分為四個部分。第一部分聚焦於統計分佈和建模可靠性數據的方法。第二部分概述了實驗設計，包括響應曲面方法和最優設計。第三部分描述了針對壽命數據的可靠性分析的回歸模型。該部分提供了如何正確分析設計實驗中收集的數據的方法。書籍的最後一部分通過定制實驗將前面的各個部分結合在一起，這些實驗非常適合進行可靠性測試。整本書都著重於可靠性應用和方法。它涵蓋了包括有實驗設計和可靠性分析在內的最佳和強健設計，以及帶有被審查數據的回歸模型。

為了幫助讀者理解，書中包含了許多例子和案例研究，以說明設計實驗的關鍵因素，並強調壽命測試相對於其他實驗的獨特性。書中提供了許多最新的練習和解答，以幫助讀者更好地理解實驗設計和可靠性的實際應用。作者在適當的地方使用了R和JMP^(R)軟件，並提供了相關數據集的補充網站。

本書由實驗設計方法學和可靠性數據分析領域的國際知名專家撰寫，涵蓋的主題包括：

- 可靠性介紹、壽命分佈、審查和壽命分佈參數的推斷
- 實驗設計、最優設計和強健設計
- 壽命回歸、參數回歸模型和Cox比例風險模型
- 可靠性達成的設計策略
- 加速測試、加速模型和加速測試的實驗設計

本書以易於理解的方式介紹了可靠性，適合各種技術水平的讀者。它是統計研究人員、可靠性工程師、質量工程師以及應用統計和工程領域的專業人士的重要參考資料。這是一本適用於高年級本科和研究生統計和工程課程的綜合教材。

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.

Steven E. Rigdon, 博士，是聖路易斯大學生物統計學系的教授。他也是南伊利諾伊大學愛德華茲維爾分校的傑出研究教授。他的研究興趣包括空間疾病監測和可靠性評估。

Rong Pan, 博士，是亞利桑那州立大學計算機、信息學和決策系統工程學院工業工程學副教授。他的研究興趣包括故障時間數據分析、實驗設計、多變量統計質量控制、時間序列分析和控制。

Douglas C. Montgomery, 博士，是亞利桑那州立大學工業工程學的校董教授和ASU基金會工程學教授。他的研究興趣包括工業統計和實驗設計。

Laura J. Freeman, 博士，是維吉尼亞理工大學統計學研究副教授，也是國家安全研究所智能系統部門的主任。她的研究興趣包括實驗設計，在新興技術研究中利用實驗方法，專注於物聯網系統、人工智能和機器學習。