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Informationen zum Autor 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. Klappentext 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 applications 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 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. Zusammenfassung * This is the first book of its kind on the subject. It is written by experts in the field (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). It is current and presents state-of-the-art materials. Inhaltsverzeichnis 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 Int...
List of contents
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
