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    政大機構典藏 > 商學院 > 統計學系 > 學位論文 >  Item 140.119/36925
    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/36925


    Title: 設計EWMA管制圖以監控相依製程
    Authors: 余翊寧
    Contributors: 楊素芬
    余翊寧
    Keywords: 管制圖
    相依製程
    馬可夫鏈
    EWMA
    Date: 2007
    Issue Date: 2009-09-18 20:10:22 (UTC+8)
    Abstract: 
    Control charts are used to effectively monitor and determine whether a process is in-control or out-of-control. The properties of EWMA control charts on a single process have been discussed by many researchers. They have proved that EWMA control charts detect small shifts in means or variances more quickly than the traditional Shewhart control charts. However, many products are currently produced in several dependent process steps. In this article, (1) we propose three kinds of EWMA control charts, - , - , and a combined control charts, to monitor the process mean and variance for a single process step, and (2) extend the three kinds of EWMA control charts in (1) to control two dependent steps. The performance of the proposed control charts is measured by using the Markov chain approach. The application of the proposed control charts is illustrated by using some numerical examples, and the performance of the proposed charts is compared by using some numerical examples. The adjusted average time to signal (AATS) and the adjusted average samples to signal (ANOS) are calculated to measure the performance of the proposed EWMA control charts by Markov chain approach. A data set consisting of the measurements of the inside diameter of the cylinder bores in an engine block example illustrates the applications of the three kinds of EWMA control charts for a single step and a empirical automobile braking system example illustrates the applications of the three kinds of EWMA control charts for two dependent steps. Moreover, their performances are compared by some numerical analysis results.
    Reference: [1] Box G. E. P., Hunter W., and Hunter J. (1978). Statistics for Experiments. John Wiley & Sons, New York, NY.
    [2] Crowder, S. V. (1987a), “A Simple Method for Studying Run Length Distributions of Exponentially Weighted Moving Average Charts,”Technometrics 29, 401-407.
    [3] Crowder, S. V. (1987b),”Average Run Lengths of Exponentially Weighted Moving Average Charts,” Journal of Quality Technology 18, pp. 203-210.
    [4] Costa, A. F. B. and Rahim, M. A. (2006), “ A Single EWMA Chart for Monitoring Process Mean and Process Variance,” Quality Technology & Quantitative Management, pp.295-305
    [5] Castagliola, P. (2005a), “A New S2-EWMA Control Chart for Monitoring the Process Variance,” Quality and Reliability Engineering International, Vol. 21, pp.781-794
    [6] Castagliola, P. (2005b), “A R-EWMA Control Chart for Monitoring the Process Range,” Quality and Safety Engineering, Vol. 12(1), pp.31-49
    [7] Montgomery, D. C. (2005). Introduction to Statistical Quality Control, 5th ed. John Wiley & Sons, New York, NY.
    [8] Gan, F. F. (1995), “ Joint monitoring of process mean and variance using exponentially weighted moving average control charts,” Technometrics 33, pp. 446-453
    [9] Chen, G., Cheng, S. W., and Xie, H. (2001), “Monitoring Process Mean and Variability with One EWMA Chart,” Journal of Quality Technology Vol.33, No.2
    [10] Chen, G., Cheng, S. W., and Xie, H. (2004), “A New EWMA Control Chart for Monitoring Both Location and Dispersion,” Quality Technology & Quantitative Management, pp.217-231
    [11] Wade, M. R., and Woodall, W. H. (1993), “A Review and Analysis of Cause-Selecting Control Charts,” Journal of Quality Technology 25, No.3, pp. 161-169
    [12] MacGregor, J.F. and Harris, T.J. (1993), “The Exponentially Weighted Moving Variance,” Journal of Quality Technology, Vol. 25(2), pp. 106-118
    [13] Quesenberry C. P. (1991), “SPC Q charts for Start-Up Process and Short or Long Runs,” Journal of Quality Technology 23, pp. 213-224
    [14] Quesenberry C. P. (1995), “On Properties of Q charts Variables,” Journal of Quality Technology 27, pp. 184-203
    [15] Roberts, S. W. (1959), “Control Chart Tests Based on Geometric Moving Averages,” Technometrics 1, pp. 239-250
    [16] Crowder, S. V., and Hamilton, M. D. (1992), “An EWMA for Monitoring a Process Standard Deviation,” Journal of Quality Technology 24, No.1, pp. 12-21
    [17] Shewhart, W. A. (1931), Economic Control of Quality of Manufactured Product, D. Van Nostrand Co., New York.
    [18] Zhang, G. X. (1984), “A New Type of Control Charts and a Theory of Diagnosis with Control Charts,” World Quality Congress Transactions. American Society for Quality Control, pp. 175-185.
    Description: 碩士
    國立政治大學
    統計研究所
    95354017
    96
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0095354017
    Data Type: thesis
    Appears in Collections:[統計學系] 學位論文

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