English  |  正體中文  |  简体中文  |  Post-Print筆數 : 27 |  Items with full text/Total items : 113648/144635 (79%)
Visitors : 51630390      Online Users : 549
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 140.119/60431
    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/60431


    Title: 適應性加權損失管制圖之研究
    The Study of Adaptive Weighted Loss Control Charts for Dependent Process Steps
    Authors: 林亮妤
    Lin,Liang Yu
    Contributors: 楊素芬
    Yang,Su Fen
    林亮妤
    Lin,Liang Yu
    Keywords: 管制圖
    變動參數
    相依製程
    損失函數
    最佳化技術
    馬可夫鏈
    Control charts
    Variable parameters
    Dependent process steps
    Loss function
    Optimization technique
    Markov chain
    Date: 2009
    Issue Date: 2013-09-05 15:10:33 (UTC+8)
    Abstract: 近年來有許多研究發現,適應性管制圖在偵測製程或產品幅度偏移時的速度比傳統的舒華特管制圖來的快,許多文獻也討論到利用適應性管制技術同時監控製程的平均數和變異數。隨著科技的發達,許多產品在製造上更加精密,現今普遍使用的固定參數管制圖並無法有效率的偵測出製程失控,導致巨大的成本損失。為了改善現有管制圖的偵測效率與有效控制製程失控下的損失,我們提出了三種適應性加權損失管制圖,包括變動抽樣間隔(VSI)、變動樣本數與抽樣間隔(VSI)、變動管制參數(VP)來偵測單一製程與兩相依製程的平均數和變異數。採用製程發生變動後到管制圖偵測出異常訊息所需的平均時間(AATS)與所需的總觀測數(ANOS)來衡量管制圖的偵測績效,並利用馬可夫鏈推導計算得之。從數值分析中發現,適應性加權損失管制圖在「偵測小偏移幅度時的偵測效率」與「成本的控制」明顯比傳統管制圖表現的更好,再加上每一個製程僅需採用單一管制圖,對使用者也較為簡便並且容易理解,因此適應性加權損失管制圖在實務上是值得被推薦使用的。
    Recent research has shown that control charts with adaptive features detect process shifts faster than traditional Shewhart charts. In this article, we propose three kinds of adaptive weighted loss (WL) control charts, variable sampling intervals (VSI) WL control charts , variable sample sizes and sampling intervals (VSSI) WL control charts and variable parameters (VP) WL control charts, to monitor the target and variance on a single process step and two dependent process steps simultaneously. These adaptive WL control charts may effectively distinguish which process step is out-of-control. We use the Markov chain approach to calculate the adjusted average time to signal (AATS) and average number of observations to signal (ANOS) in order to measure the performance of the proposed control charts. From the numerical examples and data analyses, we find the adaptive WL control charts have better detection abilities and performance than fixed parameters (FP) WL control charts and FP Z(X-bar)-Z(Sx^2) and Z(e-bar)-Z(Se^2) control charts. We also proposed the optimal adaptive WL control charts using an optimization technique to minimize AATS when users cannot specify the values of the variable parameters. In addition, we discuss the impact of misusing weighted loss of outgoing quality control chart. In conclusion, using a single chart to monitor a process is inherently easier than using two charts. The WL control charts are easy to understand for the users, and have better performance and detection abilities than the other charts, thus, we recommend the use of WL control charts in the real industrial process.
    Reference: [1]Amin, R. W. and Miller, R. W. (1993), “A robustness study of X-bar Charts with variable sampling intervals,” Journal of Quality Technology 25, 36-44.
    [2]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.
    [3]Chen, G., Cheng, S. W. and Xie, H. (2001), “Monitoring process mean and variability with one EWMA chart,” Journal of Quality Technology 33(2), 223-233.
    [4]Chengular, I. N., Arnold, J. C. and Reynolds, M. R., JR. (1989), “Variable sampling intervals for multiparameter Shewhart charts, ” Communications in Statistics – Theory and Methods 18, 1769–1792.
    [5]Cinlar, E. (1975), Introduction to stochastic process. Englewood Cliffs, NJ:Prentice-Hall.
    [6]Constable, G. K., Cleary, M. J., Tickel, C. and Zhang, G. X. (1988), “Use of Cause-Selecting Charts in the Auto Industry,” ASQC Quality Congress Transactions. American Society for Quality Control, 597-602.
    [7]Costa, A. F. B. (1994), “X-bar Charts with variable sample size,” Journal of Quality Technology 26(3), 155-163.
    [8]Costa, A. F. B. (1997), “X-bar Charts with variable sample size and sampling intervals,” Journal of Quality Technology 29(2), 197-204.
    [9]Costa, A. F. B. (1998), “Joint X-bar and R Charts with variable parameters,” IIE Transactions 30(4), 505-514
    [10]Costa, A. F. B. (1999a), “Joint X-bar and R Charts with variable sample size and sampling intervals,” Journal of Quality Technology 31, 387-397.
    [11]Costa, A. F. B. (1999b), “X-bar Charts with variable parameters,” Journal of Quality Technology 31, 408-416.
    [12]Cyrus, D. (1997), Statistical Aspects of Quality Control Academic Press, London.
    [13]Daudin, J. J. (1992), “Double sampling X-bar Charts,” Journal of Quality Technology 24, 78-87.
    [14]Grabov, P. and Ingman, D. (1996), “Adaptive control limits for bivariate process monitoring,” Journal of Quality Technology 28, 320-330.
    [15]Mandel, B. J. (1969), “The Regression Control Chart,” Journal of Quality Technology 1, 1-9.
    [16]Penrose, K., Nelson, A. and Fisher, A. (1985), “Generalized body composition prediction equation for men using simple measurement techniques” Medicine and Science in Sports and Exercise,17(2), 189.
    [17]Prabhu, S. S., Montgomery, D. C. and Runger, G. G. (1994), “ A combine dadaptive sample size and sampling interval control scheme,” Journal of Quality Technology, 26(3), 164–176.
    [18]Prabhu, S. S., Runger, G. C. and Keats, J. B. (1993), “An adaptive sample size X-bar chart,” International Journal of Production Research, 31,2895–2909.
    [19]Reynolds, M. R., JR. (1996), “Variable-sampling-interval control charts with
    Sampling at Fixed Times,” IIE Transactions 28, 497-510.
    [20]Reynolds, M. R., JR. and Glosh, B. K. (1981), “Designing control charts for means and variances,” ASQC Quality Congress Transactions, American Society for Quality Control ,San Francisco ,400-407.
    [21]Reynolds, M. R., JR., Amin, R. W., Arnold, J. C. and Nachlas, J. A (1988), “ charts with variable sampling intervals,” Technometrics 30(2), 181-192.
    [22]Reynolds, M. R., JR. and Arnold, J. C., (1989), “ Optimal one-sided Shewhart control chart with variable sampling intervals,” Sequential Analysis 8, 51-77.
    [23]Reynolds, M. R., JR., Arnold, J. C. and Baik (1996), “ Variable sampling interval X-bar Charts in the presence of Correlation,” Journal of Quality Technology 28, 1-28.
    [24]Reynolds, M. R., JR. and Stoumbos, Z. G. (2001), “ Monitoring the process mean and variance using individual observations and variable sampling intervals,” Journal of Quality Technology 33, 181–205.
    [25]Runger, G. C. and Montgomery, D. C. (1993), “Adaptive sampling enhancements for Shewhart control charts,” IIE Transactions 25, 41-51.
    [26]Runger, G. C. and Pignatiello, J. J., JR. (1991), “Adaptive sampling for process control,” Journal of Quality Technology 23, 135-155.
    [27]Shewhart, W. A. (1931), Economic Control of Quality of Manufactured Product, D. Van Nostrand Co., New York.
    [28]Spiring, F. A. and Yeung, A. S. (1998), “A general class of loss functions with individual applications,” Journal of Quality Technology 30, 152-162.
    [29]Sullivan, J. H. and Woodall, W. H. (1996), “A control chart for preliminary analysis of individual observations,” Journal of Quality Technology 28, 265-278.
    [30]Tagaras, G. (1998), “A survey of recent developments in the design of adaptive control charts”, Journal of Quality Technology 30, 212-231.
    [31]Taguchi, G. (1986), Introduction to Quality Engineering, Asian Productivity Organization, Tokyo.
    [32]Wade, M. R. and Woodall, W. H. (1993), “A review and analysis of cause-selecting control charts,” Journal of Quality Technology 25(2), 161-169.
    [33]Wu, Z. and Tian, Y. (2006) “Weighted-loss-function control charts,” International Journal of Production Research, 31, 107–115.
    [34]Yang, S. and Su, H. (2007), “Adaptive Control Scheme for Dependent Process Steps,” International Journal of Loss Prevention and Industrial Process, Vol. 20,15-25.
    [35]Zhang, G. X. (1984), “A new type of control charts and a theory of diagnosis with control chats,” World Quality Congress Transactions. American Society for Quality Control, 175-185.
    Description: 碩士
    國立政治大學
    統計研究所
    97354008
    98
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0097354008
    Data Type: thesis
    Appears in Collections:[統計學系] 學位論文

    Files in This Item:

    File Description SizeFormat
    400801.pdf1042KbAdobe PDF2318View/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