Synthetic charts to control bivariate processes with autocorrelated data

被引：13

作者：

Simoes, Felipe Domingues ^{[1
,3
]}

Leoni, Roberto Campos ^{[1
,2
]}

Guerreiro Machado, Marcela Aparecida ^{[1
,3
]}

Branco Costa, Antonio Fernando ^{[1
,3
]}

机构：

[1] Sao Paulo State Univ UNESP, Prod Dept, Guaratingueta, SP, Brazil

[2] Mil Acad Agulhas Negras AMAN, Chair Stat, BR-27534970 Resende, RJ, Brazil

[3] UNESP, Fac Engn, Dept Prod, BR-12516410 Guaratingueta, SP, Brazil

来源：

COMPUTERS & INDUSTRIAL ENGINEERING | 2016年 / 97卷

关键词：

(X)over-bar charts; Steady state; Synthetic; Side-sensitive; Bivariate process; (X)OVER-BAR CHART; PROCESS DISPERSION; OPTIMAL-DESIGN; T-2; CHART; PERFORMANCE;

D O I：

10.1016/j.cie.2016.04.005

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

In this study, we propose the use of simultaneous (X) over bar charts to control bivariate processes with autocorrelated data. The first set of (X) over bar charts is side-sensitive with regard to the same variable (SV (X) over bar charts) and the second one is side-sensitive with regard to both variables (BV (X) over bar charts). The Markov chain approach was used to obtain the steady-state properties of the (X) over bar charts. In comparison with the standard synthetic T-2 chart, the SV and the BV charts signal faster in a wide variety of disturbances, except when the variables are high correlated. The BV charts are simpler and signal faster than the SV charts. (C) 2016 Elsevier Ltd. All rights reserved.

引用

页码：15 / 25

页数：11

共 50 条

[21] THE UTILISATION OF SPECIAL CAUSE CONTROL CHARTS IN THE PRESENCE OF AUTOCORRELATED DATA
Elevli, Sermin
[J]. SIGMA JOURNAL OF ENGINEERING AND NATURAL SCIENCES-SIGMA MUHENDISLIK VE FEN BILIMLERI DERGISI, 2020, 38 (02): : 787 - 793
[22] The Variable Parameters [image omitted] Control Charts for Monitoring Autocorrelated Processes
Lin, Yu-Chang
[J]. COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION, 2009, 38 (04) : 729 - 749
[23] Multivariate Cuscore control charts for monitoring the mean vector in autocorrelated processes
Chen, Shuohui
Nembhard, Harriet Black
[J]. IIE TRANSACTIONS, 2011, 43 (04) : 291 - 307
[24] Support Vector Regression control charts for multivariate nonlinear autocorrelated processes
Ben Khediri, Issam
Weihs, Claus
Limam, Mohamed
[J]. CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS, 2010, 103 (01) : 76 - 81
[25] Control Charts for Monitoring Autocorrelated Processes Based on Neural Networks Model
Camargo, Maria Emilia
Priesnitz Filho, Walter
Russo, Suzana Leitao
dos Santos Dullius, Angela Isabel
[J]. CIE: 2009 INTERNATIONAL CONFERENCE ON COMPUTERS AND INDUSTRIAL ENGINEERING, VOLS 1-3, 2009, : 1881 - 1884
[26] RUN-LENGTH DISTRIBUTIONS OF RESIDUAL CONTROL CHARTS FOR AUTOCORRELATED PROCESSES
WARDELL, DG
MOSKOWITZ, H
PLANTE, RD
[J]. JOURNAL OF QUALITY TECHNOLOGY, 1994, 26 (04) : 308 - 317
[27] Monitoring the covariance matrix of bivariate processes with the DVMAX control charts
Machado, Marcela A. G.
Ho, Linda Lee
Quinino, Roberto C.
Celano, Giovanni
[J]. APPLIED STOCHASTIC MODELS IN BUSINESS AND INDUSTRY, 2022, 38 (01) : 116 - 132
[28] Attribute control charts for monitoring the covariance matrix of bivariate processes
Machado, M. A. G.
Ho, L. L.
Costa, A. F. B.
[J]. QUALITY AND RELIABILITY ENGINEERING INTERNATIONAL, 2018, 34 (02) : 257 - 264
[29] The bivariate dispersion control charts for non-normal processes
Saghir, Aamir
[J]. INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH, 2015, 53 (07) : 1964 - 1979
[30] The T2 chart with mixed samples to control bivariate autocorrelated processes
Leoni, Roberto Campos
Guerreiro Machado, Marcela Aparecida
Branco Costa, Antonio Fernando
[J]. INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH, 2016, 54 (11) : 3294 - 3310

← 1 2 3 4 5 →