The median control chart for process monitoring in short production runs

被引:4
|
作者
Khoo, Michael B. C. [1 ]
Saha, Sajal [2 ]
Teh, Sin Yin [3 ]
Haq, Abdul [4 ]
Lee, How Chinh [5 ]
机构
[1] Univ Sains Malaysia, Sch Math Sci, George Town 11800, Malaysia
[2] Int Univ Business Agr & Technol, Dept Math, Dhaka, Bangladesh
[3] Univ Sains Malaysia, Sch Management, George Town, Malaysia
[4] Quaid I Azam Univ, Dept Stat, Islamabad, Pakistan
[5] Univ Tunku Abdul Rahman, Fac Sci, Dept Phys & Math Sci, Kampar, Malaysia
关键词
Truncated average run length; Expected truncated average run length; Truncated standard deviation of the run length; Median chart; Short production runs; T CONTROL CHARTS; CUSUM CHARTS; SHEWHART; COEFFICIENT;
D O I
10.1080/03610918.2020.1783557
中图分类号
O21 [概率论与数理统计]; C8 [统计学];
学科分类号
020208 ; 070103 ; 0714 ;
摘要
The implementation of thechart requires a large number of samples from an underlying process model, which poses a major problem in short production runs, like in the fast-paced smart manufacturing environment. In this study, the median chart, as a robust alternative to thechart, is used to efficiently monitor the normal or non-normal processes in short production runs. The sensitivity of the median chart is assessed in terms of the truncated average run length (TARL), truncated standard deviation of the run length (TSDRL) and expected TARL criteria. The in-control and out-of-control run length performances of theand the median charts are compared when sampling from non-normally distributed processes in short production runs. It is found that when a non-normal process is in-control, the median chart outperforms thechart, as the latter possesses smaller in-control TARL and higher in-control TSDRL values. In addition, for a non-normal (heavy-tailed) out-of-control process, the median chart prevails over thechart. An illustrative example is given to explain the implementation of the median chart in short production runs.
引用
收藏
页码:5816 / 5831
页数:16
相关论文
共 50 条
  • [31] OWave control chart for monitoring the process mean
    Cohen, Achraf
    Tiplica, Teodor
    Kobi, Abdessamad
    CONTROL ENGINEERING PRACTICE, 2016, 54 : 223 - 230
  • [32] An attribute control chart for monitoring the variability of a process
    Ho, Linda Lee
    Quinino, Roberto Costa
    INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS, 2013, 145 (01) : 263 - 267
  • [33] A Synthetic Control Chart for Monitoring Process Variability
    Rajmanya, S. V.
    Ghute, V. B.
    QUALITY AND RELIABILITY ENGINEERING INTERNATIONAL, 2014, 30 (08) : 1301 - 1309
  • [34] A Note on the Median Control Chart
    Park, Hyo-Il
    COMMUNICATIONS FOR STATISTICAL APPLICATIONS AND METHODS, 2013, 20 (02) : 107 - 113
  • [35] A GLR control chart for monitoring a multinomial process
    Lee, Jaeheon
    Peng, Yiming
    Wang, Ning
    Reynolds, Marion R., Jr.
    QUALITY AND RELIABILITY ENGINEERING INTERNATIONAL, 2017, 33 (08) : 1773 - 1782
  • [36] 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):
  • [37] A Side Sensitive Group Runs Control Chart for Detecting Shifts in the Process Mean
    M. P. Gadre
    R. N. Rattihalli
    Statistical Methods and Applications, 2007, 16 : 27 - 37
  • [38] A side sensitive group runs control chart for detecting shifts in the process mean
    Gadre, M. P.
    Rattihalli, R. N.
    STATISTICAL METHODS AND APPLICATIONS, 2007, 16 (01): : 27 - 37
  • [39] A Gaussian Process Control Chart for Monitoring Autocorrelated Process Data
    Alshraideh, Hussam
    Khatatbeh, Enas
    JOURNAL OF QUALITY TECHNOLOGY, 2014, 46 (04) : 317 - 322
  • [40] An intelligent control chart for monitoring of autocorrelated egg production process data based on a synergistic control strategy
    Mertens, K.
    Vaesen, I.
    Loeffel, J.
    Kemps, B.
    Kamers, B.
    Zoons, J.
    Darius, P.
    Decuypere, E.
    De Baerdemaeker, J.
    De Ketelaere, B.
    COMPUTERS AND ELECTRONICS IN AGRICULTURE, 2009, 69 (01) : 100 - 111