An adaptive chart for monitoring the process mean and variance

被引:54
|
作者
Costa, Antonio F. B. [1 ]
De Magalhaes, Maysa S.
机构
[1] Univ Estadual Paulista, FEG, BR-12516410 Guaratingueta, SP, Brazil
[2] IBGE, ENCE, BR-20231050 Rio De Janeiro, Brazil
关键词
Shewhart charts; AATS; adaptive charts; non-central chi-square distribution; single charts to; control the process mean and variance; joint (X)over-bar and R charts;
D O I
10.1002/qre.842
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
Traditionally, an (X) over bar chart is used to control the process mean and an R chart is used to control the process variance. However, these charts are not sensitive to small changes in the process parameters. The adaptive ($) over bar and R charts might be considered if the aim is to detect small disturbances. Due to the statistical character of the joint (X) over bar and R charts with fixed or adaptive parameters, they are not reliable in identifing the nature of the disturbance, whether it is one that shifts the process mean, increases the process variance, or leads to a combination of both effects. In practice, the speed with which the control charts detect process changes may be more important than their ability in identifying the nature of the change. Under these circumstances, it seems to be advantageous to consider a single chart, based on only one statistic, to simultaneously monitor the process mean and variance. In this paper, we propose the adaptive non-central chi-square statistic chart. This new chart is more effective than the adaptive (X) over bar and R charts in detecting disturbances that shift the process mean, increase the process variance, or lead to a combination of both effects. Copyright (c) 2006 John Wiley & Sons, Ltd.
引用
下载
收藏
页码:821 / 831
页数:11
相关论文
共 50 条
  • [31] A Bayesian Control Chart for Monitoring Process Variance
    Lin, Chien-Hua
    Lu, Ming-Che
    Yang, Su-Fen
    Lee, Ming-Yung
    APPLIED SCIENCES-BASEL, 2021, 11 (06):
  • [32] A SYNTHETIC CONTROL CHART FOR MONITORING THE PROCESS MEAN OF SKEWED POPULATIONS BASED ON THE WEIGHTED VARIANCE METHOD
    Khoo, Michael B. C.
    Wu, Zhang
    Atta, Abdu M. A.
    INTERNATIONAL JOURNAL OF RELIABILITY QUALITY & SAFETY ENGINEERING, 2008, 15 (03): : 217 - 245
  • [33] A joint monitoring of the process mean and variance with a generally weighted moving average maximum control chart
    Chatterjee, Kashinath
    Koukouvinos, Christos
    Lappa, Angeliki
    Roupa, Paraskevi
    COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION, 2023, 53 (11) : 5122 - 5142
  • [34] Monitoring the process mean and variance using a synthetic control chart with two-stage testing
    Costa, Antonio F. B.
    de Magalhaes, Maysa S.
    Epprecht, Eugenio K.
    INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH, 2009, 47 (18) : 5067 - 5086
  • [35] A rational sequential probability ratio test control chart for monitoring process shifts in mean and variance
    Ou, Yanjing
    Wu, Zhang
    Khoo, Michael B. C.
    Chen, Nan
    JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION, 2015, 85 (09) : 1765 - 1781
  • [36] Joint monitoring of process mean and variance
    Gan, FF
    NONLINEAR ANALYSIS-THEORY METHODS & APPLICATIONS, 1997, 30 (07) : 4017 - 4024
  • [37] Monitoring the mean and the variance of a stationary process
    Knoth, S
    Schmid, W
    STATISTICA NEERLANDICA, 2002, 56 (01) : 77 - 100
  • [38] Joint monitoring of process mean and variance
    Department of Mathematics, National University of Singapore, 10 Kent Ridge Crescent, Singapore 119260, Singapore
    Nonlinear Anal Theory Methods Appl, 7 (4017-4024):
  • [39] Maximum weighted adaptive CUSUM charts for simultaneous monitoring of process mean and variance
    Haq, Abdul
    Razzaq, Faiqa
    JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION, 2020, 90 (16) : 2949 - 2974
  • [40] OWave control chart for monitoring the process mean
    Cohen, Achraf
    Tiplica, Teodor
    Kobi, Abdessamad
    CONTROL ENGINEERING PRACTICE, 2016, 54 : 223 - 230