English  |  正體中文  |  简体中文  |  Post-Print筆數 : 27 |  Items with full text/Total items : 113822/144841 (79%)
Visitors : 51835045      Online Users : 453
RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
Scope Tips:
  • please add "double quotation mark" for query phrases to get precise results
  • please goto advance search for comprehansive author search
  • Adv. Search
    HomeLoginUploadHelpAboutAdminister Goto mobile version
    政大機構典藏 > 商學院 > 統計學系 > 學位論文 >  Item 140.119/49600
    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/49600


    Title: 適應性累積和損失管制圖之研究
    The Study of Adaptive CUSUM Loss Control Charts
    Authors: 林政憲
    Contributors: 楊素芬
    林政憲
    Keywords: 累積和管制圖
    適應性管制圖
    VSI管制圖
    VSS管制圖
    VSSI管制圖
    損失函數
    馬可夫鍊
    基因演算法
    CUSUM control chart
    Adaptive control chart
    VSI control chart
    VSS control chart
    VSSI control chart
    Loss function
    Markov chain
    Genetic algorithm
    Date: 2009
    Issue Date: 2010-12-08 14:54:06 (UTC+8)
    Abstract: The CUSUM control charts have been widely used in detecting small process shifts since it was first introduced by Page (1954). And recent studies have shown that adaptive charts can improve the efficiency and performance of traditional Shewhart charts. To monitor the process mean and variance in a single chart, the loss function is used as a measure statistic in this article. The loss function can measure the process quality loss while the process mean and/or variance has shifted. This study combines the three features: adaption, CUSUM and the loss function, and proposes the optimal VSSI, VSI, and FP CUSUM Loss chart. The performance of the proposed charts is measured by using Average Time to Signal (ATS) and Average Number of Observations to Signal (ANOS). The ATS and ANOS calculations are based on Markov chain approach. The performance comparisons between the proposed charts and some existing charts, such as X-bar+S^2 charts and CUSUM X-bar+S^2 charts, are illustrated by numerical analyses and some examples. From the results of the numerical analyses, it shows that the optimal VSSI CUSUM Loss chart has better performance than the optimal VSI CUSUM Loss chart, optimal FP CUSUM Loss chart, CUSUM X-bar+S^2 charts and X-bar+S^2 charts. Furthermore, using a single chart to monitor a process is not only easier but more efficient than using two charts simultaneously. Hence, the adaptive CUSUM Loss charts are recommended in real process.
    The CUSUM control charts have been widely used in detecting small process shifts since it was first introduced by Page (1954). And recent studies have shown that adaptive charts can improve the efficiency and performance of traditional Shewhart charts. To monitor the process mean and variance in a single chart, the loss function is used as a measure statistic in this article. The loss function can measure the process quality loss while the process mean and/or variance has shifted. This study combines the three features: adaption, CUSUM and the loss function, and proposes the optimal VSSI, VSI, and FP CUSUM Loss chart. The performance of the proposed charts is measured by using Average Time to Signal (ATS) and Average Number of Observations to Signal (ANOS). The ATS and ANOS calculations are based on Markov chain approach. The performance comparisons between the proposed charts and some existing charts, such as X-bar+S^2 charts and CUSUM X-bar+S^2 charts, are illustrated by numerical analyses and some examples. From the results of the numerical analyses, it shows that the optimal VSSI CUSUM Loss chart has better performance than the optimal VSI CUSUM Loss chart, optimal FP CUSUM Loss chart, CUSUM X-bar+S^2 charts and X-bar+S^2 charts. Furthermore, using a single chart to monitor a process is not only easier but more efficient than using two charts simultaneously. Hence, the adaptive CUSUM Loss charts are recommended in real process.
    Reference: [1] Albin, S. L., Kang, L and Shea, G. (1997), “An X and EWMA chart for individual observations,” Journal of Quality Technology, 29, 41-48
    [2] Amin, R. W. and Miller, R. W. (1993), “A Robustness Study of Charts with Variable Sampling Intervals,” Journal of Quality Technology, 25, 36-44
    [3] Amin, R. W., Wolff, H., Besenfelder, W. and Baxley R. Jr. (1999), “EWMA control charts for the smallest and largest observations,” Journal of Quality Technology, 31, 189-206
    [4] Annadi, H. P., Keats, J. B., Runger, G. C. and Montgomery, D. C. (1995), “An adaptive sample size CUSUM control chart,” International Journal of Production Research, 33, 1605-1616
    [5] Arnold, J. C. and Reynolds Jr., M. R. (2001), “CUSUM control charts with variable sample sizes and sampling intervals,” Journal of Quality Technology, 33, 66-81
    [6] Braverman, J. D. (1981), “Fundamentals of Statistical Quality Control,” Reston Publishing Company, Reston, VA.
    [7] Brook D. and Evans D. A. (1972), “An approach to the probability distribution of cusum run length,” Biometrika, 59, 3, 539-549
    [8] Chen, G., Cheng, S. W. and Xie, H. (2001), “Monitoring Process Mean and Variability With One EWMA Chart,” Journal of Quality Technology, 33, 223-233
    [9] Costa, A. F. B. (1994), “ charts with Variable Sample Size,” Journal of Quality Technology, 26, 155-163
    [10] Costa, A. F. B. (1997), “ charts with Variable Sample Size and Sampling Intervals,” Journal of Quality Technology, 29, 197-204
    [11] Costa, A. F. B. (1998), “Joint and R charts with variable parameters,” IIE Transactions, 30, 505-514
    [12] Costa, A. F. B. (1999a), “Joint and R charts with variable sample size and sampling intervals,” Journal of Quality Technology, 31, 387-397
    [13] Costa, A. F. B. (1999b), “ charts with variable parameters,” Journal of Quality Technology, 31, 408-416
    [14] Costa, A. F. B. and Magalhaes S. (2006), “An Adaptive Chart for Monitoring the Process Mean and Variance,” Quality and Reliability Engineering International, 23, 821-831
    [15] Costa, A. F. B. and Rahim M. A. (2006), “A Single EWMA Chart for Monitoring Process Mean and Variance,” Quality Technology and Quantitative Management, 3, 295-305
    [16] DeVor, R. E., Chang, T. and Sutherland, J. W. (1992), “Statistical quality design and control : contemporary concepts and methods”
    [17] Hawkins, D. M. (1992), “A Fast Approximation for Average Run Lengths of CUSUM Control Charts,” Journal of Quality Technology, 24, 37-43
    [18] Hawkins, D. M. (1993), “Cumulative Sum Control Charting: An Underutilized SPC Tool,” Quality Engineering, 5(3), 463-477
    [19] IMSL (1991), Users Manual, Math/Library, Vol. 2, IMSL, Inc., Houstin, Texas
    [20] Luceno, A. and Puig-Pey, J. (2002), “Computing the Run Length Probability Distribution for CUSUM Charts,” Journal of Quality Technology, 34, 209-215
    [21] Luo, Y., Li, Z. and Wang Z. (2009), “Adaptive CUSUM control chart with variable sampling intervals,” Computational Statistics and Data Analysis, 53, 2693-2701
    [22] Montgomery D. C. (2009), “Statistical Quality Control 6th Edition”, Aptara, Inc.
    [23] Patnaik P. B. (1949), “The Non-central Chi-square and F-distributions and Their Applications,” Biometrika, 36, 1/2, 202-232
    [24] Reynolds, M. R., Jr., Amin, R. W., Arnold, J. C. and Nachlas, J. A. (1988), “ Charts With Variable Sampling Intervals,” Technometrics, 30, 181-192
    [25] Reynolds, M. R., Jr., Amin, R. W. and Arnold, J. C. (1990), “CUSUM charts with variable sampling intervals,” Technometrics, 32, 371-384
    [26] Reynolds, M. R., Jr. and Arnold J. C. (1989), “Optimal one-sided Shewhart control charts with variable sampling intervals,” Sequential Analysis, 8, 51-77
    [27] Wu, Z. and Yu, T. (2006), “Weighted-loss-function control charts,” The International Journal of Advanced Manufacturing Technology, 31, 107-115
    [28] Wu, Z., Zhang, S. and Wang, P (2007), “A CUSUM Scheme with Variable Sample Sizes and Sampling Intervals for Monitoring the Process Mean and Variance,” Quality and Reliability Engineering International, 23, 157-170
    [29] Zhang, S. and Wu, Z. (2006), “Monitoring the process mean and variance using a weighted loss function CUSUM scheme with variable sampling intervals,” IIE Transactions, 38, 377-387
    [30] Zhang, S. and Wu, Z. (2007), “A CUSUM scheme with variable sample sizes for monitoring process shifts,” The International Journal of Advanced Manufacturing Technology, 33, 977-987
    Description: 碩士
    國立政治大學
    統計研究所
    97354001
    98
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0097354001
    Data Type: thesis
    Appears in Collections:[統計學系] 學位論文

    Files in This Item:

    File SizeFormat
    400101.pdf855KbAdobe PDF2889View/Open


    All items in 政大典藏 are protected by copyright, with all rights reserved.


    社群 sharing

    著作權政策宣告 Copyright Announcement
    1.本網站之數位內容為國立政治大學所收錄之機構典藏,無償提供學術研究與公眾教育等公益性使用,惟仍請適度,合理使用本網站之內容,以尊重著作權人之權益。商業上之利用,則請先取得著作權人之授權。
    The digital content of this website is part of National Chengchi University Institutional Repository. It provides free access to academic research and public education for non-commercial use. Please utilize it in a proper and reasonable manner and respect the rights of copyright owners. For commercial use, please obtain authorization from the copyright owner in advance.

    2.本網站之製作,已盡力防止侵害著作權人之權益,如仍發現本網站之數位內容有侵害著作權人權益情事者,請權利人通知本網站維護人員(nccur@nccu.edu.tw),維護人員將立即採取移除該數位著作等補救措施。
    NCCU Institutional Repository is made to protect the interests of copyright owners. If you believe that any material on the website infringes copyright, please contact our staff(nccur@nccu.edu.tw). We will remove the work from the repository and investigate your claim.
    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - Feedback