Rassoul Noorossana, Abbas Saghaei, Amirhossein Amiri

Statistical Analysis of Profile Monitoring

• Gebundenes Buch

Produktbeschreibung

This groundbreaking reviews the latest research on the use of control charts to monitor process and product quality profiles. . It is written by experts in the field of statistical process control (such as J. D. Williams of General Electric Global Research, Jeffrey B. Birch at VPI, and Longcheen Huwang of the Institute of Statistics at Tsing Hua University Hsin Chu). The book presents a comprehensive coverage of profiles monitoring definitions, techniques, models, and application examples, especially in various fields of engineering and statistics. It covers state-of-the-art materials and deserves a place among the tools of every quality engineer who works in monitoring and improving statistical processes.

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A one-of-a-kind presentation of the major achievements in statistical profile monitoring methods

Statistical profile monitoring is an area of statistical quality control that is growing in significance for researchers and practitioners, specifically because of its range of applicability across various service and manufacturing settings. Comprised of contributions from renowned academicians and practitioners in the field, Statistical Analysis of Profile Monitoring presents the latest state-of-the-art research on the use of control charts to monitor process and product quality profiles. The book presents comprehensive coverage of profile monitoring definitions, techniques, models, and application examples, particularly in various areas of engineering and statistics.

The book begins with an introduction to the concept of profile monitoring and its applications in practice. Subsequent chapters explore the fundamental concepts, methods, and issues related to statistical profile monitoring, with topics of coverage including:
Simple and multiple linear profiles
Binary response profiles
Parametric and nonparametric nonlinear profiles
Multivariate linear profiles monitoring
Statistical process control for geometric specifications
Correlation and autocorrelation in profiles
Nonparametric profile monitoring

Throughout the book, more than two dozen real-world case studies highlight the discussed topics along with innovative examples and applications of profile monitoring. Statistical Analysis of Profile Monitoring is an excellent book for courses on statistical quality control at the graduate level. It also serves as a valuable reference for quality engineers, researchers and anyone who works in monitoring and improving statistical processes.

Produktdetails

• Wiley Series in Probability and Statistics .
• Verlag: Wiley & Sons
• 1. Auflage
• Seitenzahl: 332
• Erscheinungstermin: 13. September 2011
• Englisch
• Abmessung: 240mm x 161mm x 22mm
• Gewicht: 663g
• ISBN-13: 9780470903223
• ISBN-10: 0470903228
• Artikelnr.: 33257663

Autorenporträt

RASSOUL NOOROSSANA, PhD, is Professor of Industrial Engineering at Iran University of Science and Technology. A Certified Six Sigma Black Belt, he has published extensively in the areas of statistical quality control, engineering statistics, total quality management, and Six Sigma. ABBAS SAGHAEI, PhD, is Associate Professor of Industrial Engineering at Islamic Azad University, Iran. He currently focuses his research in the areas of statistical process control, design of experiments, and Six Sigma. AMIRHOSSEIN AMIRI, PhD, is Assistant Professor of Industrial Engineering at Shahed University, Iran. He has authored numerous papers in the areas of statistical process control and improvement, quality management and productivity, and design of experiments.

Inhaltsangabe

Preface ix Contributors xi 1 Introduction to Profile Monitoring 1 Introduction
1 1.1 Functional Relationships Qualified as Profiles
6 1.2 Functional Relationships not Qualified as Profiles
13 1.3 Structure of This Book
15 References
19 2 Simple Linear Profiles 21 Introduction
21 2.1 Phase I Simple Linear Profile
22 2.2 Phase II Simple Linear Profile
53 2.3 Special Cases and an Important Application
74 2.4 Diagnostic Statistics
77 2.5 Violation of the Model Assumptions
81 Appendix
83 References
89 3 Multiple Linear and Polynomial Profiles 93 Introduction
93 3.1 Monitoring Multiple Linear Profiles
94 3.2 Monitoring Polynomial Profiles
108 References
116 4 Binary Response Profiles 117 Introduction
117 4.1 Model Setting and Parameter Estimation
118 4.2 Phase I Control
120 4.3 Phase II Monitoring
122 4.4 Applications
123 4.5 Conclusions
126 References
128 5 Parametric Nonlinear Profiles 129 Introduction
129 5.1 Nonlinear Model Estimation
130 5.2 Phase I Methods
132 5.3 Phase II Methods
142 5.4 Variance Profiles
145 Appendix
154 References
155 6 Nonparametric Nonlinear Profiles 157 Introduction
157 6.1 Model Formulation and Nonparametric Example
159 6.2 Splines
162 6.3 Component Analysis
170 6.4 Wavelets
174 References
187 7 Multivariate Linear Profiles Monitoring 189 Introduction
189 7.1 Monitoring Multivariate Simple Linear Profiles
190 7.2 Monitoring Multivariate Multiple Linear Profiles
204 References
216 8 Statistical Process Control for Geometric Specifications 217 Introduction
217 8.1 Examples of Geometric Feature Concerning Circularity
221 8.2 Control Charts for Profile Monitoring
224 8.3 Simple Approaches for Monitoring Manufactured Profiles: The Industrial Practice
233 8.4 Performance Comparison
237 8.5 Moving from 2D Profiles to 3D Surfaces
245 8.6 Concluding Remarks
249 Acknowledgments
250 References
250 9 Correlation and Autocorrelation in Profiles 253 Introduction
253 9.1 Methods for WPA for Linear Models
255 9.2 Methods for BPC for Linear Models
257 9.3 Methods for WPA and BPC for Other (Nonlinear) Models
258 9.4 Phase I Analysis
259 9.5 Phase II Analysis
262 9.6 Related Issues: Rational Subgrouping and Random Effects
263 9.7 Discussion and Open Questions
266 Acknowledgment
267 References
267 10 Nonparametric Profile Monitoring 269 Introduction
269 10.1 Monitoring Profiles Based on Nonparametric Regression
270 10.2 Nonparametric Profile Monitoring Using Change-Point Formulation and Adaptive Smoothing
281 10.3 Nonparametric Profile Monitoring by Mixed-Effects Modeling
288 Appendix A: Approximate the Distributions of Quadratic Forms Like zT Az
299 Appendix B: The Expression of lrt
k in Model (10.8)
300 References
301 Index 303