Self-starting X-bar control chart based on Six Sigma quality and sometimes pooling procedure

被引:2
|
作者
Ravichandran, J. [1 ]
机构
[1] Amrita Univ, Amrita Vishwa Vidyapeetham, Amrita Sch Engn Coimbatore, Dept Math, Coimbatore, Tamil Nadu, India
关键词
DPMO; Q-statistics; self-starting charts; Six Sigma quality; sometimes pooling; DESIGN;
D O I
10.1080/00949655.2018.1551394
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
Self-starting control charts have been proposed in the literature to allow process monitoring when only a small amount of relevant data is available. In fact, self-starting charts are useful in monitoring a process quickly, without having to collect a sizable Phase I sample for estimating the in-control process parameters. In this paper, a new self-starting control charting procedure is proposed in which first an effective initial sample is chosen from the perspective of Six Sigma quality, then the successive sample means are either pooled or not pooled (sometimes pooling procedure) for computing next Q-statistics depending upon its signal. It is observed that the sample statistics obtained so from this in-control Phase I situation can serve as more efficient estimators of unknown parameters for Phase II monitoring. An example is considered to illustrate the construction of the proposed chart and to compare its performance with the existing ones.
引用
收藏
页码:362 / 377
页数:16
相关论文
共 10 条
  • [1] SIX SIGMA-BASED X-BAR CONTROL CHART FOR CONTINUOUS QUALITY IMPROVEMENT
    Ravichandran, Joghee
    [J]. INTERNATIONAL JOURNAL FOR QUALITY RESEARCH, 2016, 10 (02) : 257 - 266
  • [2] The Improved Quality-oriented X-bar and R Control Chart
    Zhang Yu
    Yang Musheng
    [J]. RECENT ADVANCE IN STATISTICS APPLICATION AND RELATED AREAS, PTS 1 AND 2, 2008, : 173 - 176
  • [3] A control chart to monitor the process mean based on inspecting attributes using control limits of the traditional X-bar chart
    Quinino, R. C.
    Ho, L. L.
    Cruz, F. R. B.
    Bessegato, L. F.
    [J]. JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION, 2020, 90 (09) : 1639 - 1660
  • [4] A teaching-learning based optimization approach for economic design of X-bar control chart
    Ganguly, Abhijeet
    Patel, Saroj Kumar
    [J]. APPLIED SOFT COMPUTING, 2014, 24 : 643 - 653
  • [5] Ensemble ANN-Based Recognizers to Improve Classification of X-bar Control Chart Patterns
    Hassan, Adnan
    [J]. IEEM: 2008 INTERNATIONAL CONFERENCE ON INDUSTRIAL ENGINEERING AND ENGINEERING MANAGEMENT, VOLS 1-3, 2008, : 1996 - 2000
  • [6] Design of X-bar control chart based on Inverse Rayleigh Distribution under repetitive group sampling
    Shafqat, Ambreen
    Huang, Zhensheng
    Aslam, Muhammad
    [J]. AIN SHAMS ENGINEERING JOURNAL, 2021, 12 (01) : 943 - 953
  • [7] Monitoring Using X-Bar Control Chart Using Neutrosophic-Based Generalized Multiple Dependent State Sampling with Application
    Nasrullah Khan
    Liaquat Ahmad
    Muhammad Aslam
    [J]. International Journal of Computational Intelligence Systems, 15
  • [8] Monitoring Using X-Bar Control Chart Using Neutrosophic-Based Generalized Multiple Dependent State Sampling with Application
    Khan, Nasrullah
    Ahmad, Liaquat
    Aslam, Muhammad
    [J]. INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS, 2022, 15 (01)
  • [9] A New Regional Drought Index under X-bar Chart Based Weighting Scheme – The Quality Boosted Regional Drought Index (QBRDI)
    Zulfiqar Ali
    Sadia Qamar
    Nasrulla Khan
    Muhammad Faisal
    Saad Sh. Sammen
    [J]. Water Resources Management, 2023, 37 : 1895 - 1911
  • [10] A New Regional Drought Index under X-bar Chart Based Weighting Scheme - The Quality Boosted Regional Drought Index (QBRDI)
    Ali, Zulfiqar
    Qamar, Sadia
    Khan, Nasrulla
    Faisal, Muhammad
    Sammen, Saad Sh.
    [J]. WATER RESOURCES MANAGEMENT, 2023, 37 (05) : 1895 - 1911