Nonparametric Distribution_Free Control Charts Based on Rank Statistics_图书

简介

Parametric control charts, such as Shewhart chart, Cumulative Sum(CUSUM) chart, Exponentially Weighted Moving Average (EWMA) chart, and their extensions, have been proven to perform satisfactory in many situations.However, they are often constructed based on the assumption that the underlying processfollowsnormal (or multi-normal, for multivariate control charts) distribution. The performance of parametric control charts could be seriously affected if

the normal assumption is violated, despite the effect of central limit theorem. Inthisresearch, several distribution-freenonparametriccontrol chartsare proposed.

The proposed control charts do not relyon normal assumption, and they can be used when the underlying process distribution is not well known. The nonparametric control charts are developed to address some major topics in statisti-

cal process control (SPC), such as monitoring process mean, monitoring process variance, Phase I (retrospective) analysis of historical data sample, and monitoring linear profiles. The nonparametric methods are often lessfavorable compared

to parametric control charts, due to their lower power-of-the-test. However, it is shown in the dissertation that, our proposed nonparametric control charts perform quit close to their parametric counterparts, if the process parameters areconsidered being estimated from reference sample.

The exact run-length distributions of the proposed control charts are derived, the average run-length (ARL) properties are investigated, and several numerical examples are presented for illustration purpose. It has been found, parametric control charts generally have too short in-control ARLs under non-normal distributions, and the proposed nonparametric control charts perform consistentlyin terms of in-control ARL under all distribution scenarios. A notable improvement of the proposed nonparametric control charts, over existing nonparametriccontrol charts, is that they are still sensitive under normal distribution. Therefore, they can be used in place of the traditional parametric control charts without losing much power.

作者信息

Li Suyi

出生:

籍贯: Harbin, China

工作单位:

简介: Dr. Li Suyi was born and raised in Harbin, China. He went to Tianjin University in 1998 and obtained Bachelor of Engineering degree from the IndustrialEngineering Department in 2002. Starting from 2004, he was a direct-PhD student in the Industrial & Systems Engineering Department, National University of Singapore. His PhD degree was conferred in 2011. Dr. Li worked in Quality Assurance Department of some world renowned electronic and semi-conductor manufacturing companies. He is a Six-Sigma Black Belt, and has extensive experience in Quality Management and Six-Sigma training.

CIP数据

National Library Board, Singapore Cataloguing in Publication Data

Name: Li, Suyi, 1980-

Title: Nonparametric distribution-free control charts based on rank statistics / Suyi Li.

Description: Singapore : Encyclopaedic Publishing Pte. Ltd., [2020] | Includes bibliographic references.

Identifier(s): OCN 1130396650 | ISBN 978-981-14-4070-0 (paperback)

Subject(s): LCSH: Quality control--Statistical methods. | Process control--Statistical methods.

Classification: DDC 620.00450151--dc23

Chapter 1 Introduction

1.1 Median and Max/Min Charts

1.2 Group Signed-Rank Charts

1.3 Sequential Rank Charts

1.4 Research Gaps

1.5 Structure of the Dissertation

Chapter 2 Nonparametric Cusum and Ewma Control Charts for Detecting Step Shirts in Process Mean

2.1 Introduction

2.2 The Wilcoxon Rank-Sum (WRS) Based CUSUM and EWMA Control Charts

2.3 Design of W-CUSUMand W-EWMA Control Charts

2.4 ARL Performance Comparison

2.5 A Numerical Example

2.6 Effect of Reference Sample Size and Subgroup Size

2.7 Conclusion

Chapter 3 Nonparametric Cusum and Ewma Control Charts for Detecting step Shifts in Process Variance

3.1 Introduction

3.2 Siegel-Tukey Test based Nonparametric Control Charts

3.3 Design of ST-CUSUMand ST-EWMA Control Charts

3.4 Comparison to Parametric Control Charts

3.5 Integration with W-charts

3.6 Numerical Examples

3.7 Conclusion

Chatper 4 Nonparametric Change-Point Control Chart for Phase I Analysis

4.1 Introduction

4.2 A Change-Point type Nonparametric Phase I Control Chart

4.3 Derivation of Joint Distribution

4.4 Performance Comparison

4.5 A Numerical Example

4.6 Diagnostic Application

4.7 Discussion

4.8 Conclusion

Chapter 5 Nonparametric Control Charts for Monitoring Linear Profiles

5.1 Introduction

5.2 Linear Mixed Model and Parameter Estimation

5.3 Distribution-Free Profile Control Charts

5.4 ARL Performance Comparison

5.5 Numerical Examples

5.6 Monitor Error Terms and Phase I Analysis

5.7 Conclusion

Chapter 6 Conclusions and Future Words

References

Nonparametric Distribution-Free Control Charts Based on Rank Statistics

Li Suyi

National Library Board, Singapore Cataloguing in Publication Data