On‐line Identification and Quantification of Mean Shifts in Bivariate Processes using a Neural Network‐based Approach

Quality and Reliability Engineering International - Tập 23 Số 3 - Trang 367-385 - 2007
Ruey‐Shiang Guh1
1Department of Industrial Management, National Formosa University, Huwei, Yunlin 632, Taiwan, Republic of China

Tóm tắt

AbstractMany statistical process control (SPC) problems are multivariate in nature because the quality of a given process or product is determined by several interrelated variables. Various multivariate control charts (e.g. Hotelling's $T^2$, multivariate cumulative sum and multivariate exponentially weighted moving average charts) have been designed for detecting mean shifts. However, the main shortcoming of such charts is that they can detect an unusual event but do not directly provide the information required by a practitioner to determine which variable or group of variables has caused the out‐of‐control signal. In addition, these charts cannot provide more detailed shift information, for example the shift magnitude, which would be very useful for quality practitioners to search the assignable causes that give rise to the out‐of‐control situation. This work proposes a neural network‐based model that can identify and quantify the mean shifts in bivariate processes on‐line. The performance evaluation performed by the simulation demonstrates that the proposed model outperforms the conventional multivariate control schemes in terms of average run length, and can accurately estimate the magnitude of the shift of each of the shifted variables in a real‐time mode. Extensive simulation is also carried out to examine the effects of correlation on the performance of the proposed model. A numerical example is presented to illustrate the usage of the proposed model. Although a mean shift identification and quantification tool for bivariate SPC is the particular application presented here, the proposed neural network‐based methodology can be applied to multivariate SPC in general. Copyright © 2006 John Wiley & Sons, Ltd.

Từ khóa


Tài liệu tham khảo

Hotelling HH, 1947, Techniques of Statistical Analysis

10.1080/00401706.1988.10488402

10.1080/00224065.1990.11979237

10.2307/1269551

Ryan TP, 1989, Statistical Methods for Quality Improvement

10.2307/1269008

10.1080/00224065.1994.11979526

Morrison DF, 1976, Multivariate Statistical Methods

Johnson RA, 1992, Applied Multivariate Statistical Analysis

10.1080/07408179508936797

10.1080/00224065.1996.11979699

Mason RL, 1997, Assessment of multivariate process control techniques, Journal of Quality Technology, 29, 140, 10.1080/00224065.1997.11979743

10.1080/03610928508829069

10.1002/0471725331

Fuchs C, 1994, Multivariate profile charts for statistical process control, Technometrics, 36, 182, 10.1080/00401706.1994.10485765

10.1080/00224065.1993.11979451

Wierda SJ, 1994, Gromingen Theses in Economics, Management and Organization

10.1080/00224065.1995.11979573

Mason RL, 1996, Monitoring a multivariate step process, Journal of Quality Technology, 28, 39, 10.1080/00224065.1996.11979635

10.1080/00224065.1997.11979791

Cheng CS, 1994, A neural network approach for detecting shifts in the process mean and variability, Journal of the Chinese Institute of Industrial Engineers, 11, 67

10.1016/0360-8352(94)00024-H

10.1080/00207549608905024

10.1080/07408179808966453

10.1023/A:1008818817588

10.1080/002075499190987

10.1080/002075499191049

10.1016/S0360-8352(02)00215-2

10.1023/A:1015738906895

10.1080/002075497195650

10.1023/A:1008975131304

10.1080/00224065.1992.12015232

10.1080/00207549508904863

Western Electric Co., Inc, 1958, Statistical Quality Control Handbook

Law AM, 1982, Simulation Modelling and Analysis

Lehman RS, 1977, Computer Simulation and Modelling: An Introduction

Hwarng HB, 1993, Back‐propagation pattern recognizers for X‐bar control charts: Methodology and performance, Computers and Industrial Engineering, 24, 219, 10.1016/0360-8352(93)90010-U

Rumelhart DE, 1986, Parallel Distributed Processing: Explorations in the Microstructure of Cognition, 318, 10.7551/mitpress/5236.001.0001

NeuralWare, Inc, 2001, NeuralWorks Professional II Plus Reference Guide

Gnanadesikan R, 1977, Methods for Statistical Data Analysis of Multivariate Observations

Alwan LC, 1995, The problem of misplaced control limits, Journal of Royal Statistical Society, 44, 269

10.1108/02656710210415749

Thông tin
Thông tin xuất bản

Quality and Reliability Engineering International

Tập 23 Số 3

367-385

Thông tin tác giả