臺灣人文及社會科學引文索引資料庫系統

:::

詳目顯示

回上一頁
第 1 筆 / 總合 1 筆   第一頁 上一頁 下一頁 最後一頁
/1頁
題名：廣泛加權移動平均中位數與多變量管製圖之設計策略探討
作者：楊玲
作者(外文)：Ling Yang
校院名稱：國立臺灣科技大學
系所名稱：工業管理系
指導教授：徐世輝
學位類別：博士
出版日期：2005
主題關鍵詞：管製圖；指數加權移動平均；廣泛加權移動平均；平均連串長度；中位數；多變量；工程製程管制；品質成本；control chart；EWMA；generally weighted moving average；average run length；median；multivariate；EPC；quality cost
原始連結：連回原系統網址new window
相關次數：
  • 被引用次數被引用次數:期刊(0) 博士論文(0) 專書(0) 專書論文(0)
  • 排除自我引用排除自我引用:0
  • 共同引用共同引用:0
  • 點閱點閱:1
有效率的品質管制可以增加生產力、降低成本，因此，很多研究人員致力於增強偵測製程偏移的管制能力。在製造業的許多應用上，因為修華特管製圖的繪製簡便、易於解釋而獲得廣泛應用，但因修華特管製圖在偵測小的製程偏移時顯得沒有效率，其他可選擇的管製圖，如累積和(CUSUM)管製圖與指數加權移動平均(EWMA)管製圖陸續被發展出來。Sheu與Lin 提出的廣泛加權移動平均(GWMA)管製圖，已證實在偵測製程平均數的小量偏移上，比修華特管製圖與EWMA管製圖更有效率。
在本論文中，我們提出GWMA中位數管製圖(GWMA- )之設計架構，以偵測製程中位數，我們利用電腦模擬來評估GWMA- 管製圖的平均連串長度，結果顯示GWMA- 管製圖在偵測製程中位數/平均數之微小偏移上，其偵測的效率比Castagliola所提出的EWMA- 管製圖好。接著我們提出多變量GWMA管製圖(Multivariate GWMA；MGWMA)之設計架構，以監測製程的平均數向量，同樣利用電腦模擬來評估管製圖的平均連串長度，結果顯示MGWMA管製圖在偵測製程平均數向量之微小偏移上，亦比Lowry等人所提出的多變量EWMA (MEWMA)管製圖更有效率。
另外，在生產管理上為了同時獲得消除可歸屬變異與共同變異的好處，研究有效率的整合工程製程管制(EPC)與統計製程管制(SPC)(簡稱EPC/SPC)的方法，已經逐步引起興趣，然而大部份皆只在單變量的情況，因此在本論文中，我們提出整合多變量工程製程管制與多變量統計製程管制(簡稱MEPC/MSPC)之管制架構，並應用Lorenzen-Vance的成本模式與Taguchi的品質成本函數以發展MEPC/MSPC 管製圖的經濟模型，在合理的情況下討論MEPC與MSPC的管制效率。並在MEPC/MSPC架構下，分別比較Hotelling多變量管製圖、MEWMA管製圖與MGWMA管製圖的偵測效率並討論相關的管制策略。無論以統計的觀點或以經濟的觀點，電腦模擬的結果皆顯示在偵測製程平均數向量的微小偏移上，結合MEPC與MGWMA管製圖的效率比MEPC/MEWMA管製圖及MEPC/Hotelling多變量管製圖的效率還要好。
Effective quality control can be instrumental in increasing productivity and reducing cost. Many researchers have focused on enhancing the ability of the control chart to detect process shifts. In many applications、in manufacturing industries、the Shewhart control charts are used for statistical process control because they are simple to plot and easy to interpret. However、the Shewhart control charts are relatively inefficient in detecting small process shifts. Alternative control charts、such as the CUSUM control chart and the EWMA control chart have been developed. The generally weighted moving average (GWMA) control chart proposed by Sheu and Lin has been shown to perform much better than both the Shewhart control chart and the EWMA control chart in monitoring small shifts of the process mean.
In this thesis、we propose the GWMA median control chart (GWMA- ) to monitor the process median. We use simulation to evaluate the ARL properties of the control chart. The simulation result reveals that the GWMA- control chart performs much better than the EWMA- control chart (proposed by Castagliola) in detecting small shifts of the process mean/median. We also propose a multivariate GWMA control chart (MGWMA) to monitor the process mean vector. Again、the simulation is used and the result also reveals that the MGWMA control chart performs much better than the MEWMA control chart (proposed by Lowry et al.) in detecting small shifts of the process mean vector.
For manufacturing processes、in order to obtain the advantages of eliminating both assignable causes and common causes、an effective method to integrate engineering process control (EPC) and statistical process control (SPC) is rousing escalated interests. However、most of the studies are restricted to univariate case. In this thesis、we propose a control scheme for integrating multivariate EPC and multivariate SPC (MEPC/MSPC). The Lorenzen-Vance total cost model and the Taguchi’s quality cost function are used to develop an economic model for MEPC/MSPC charts. We demonstrate the potential effectiveness of MEPC/MSPC in a reasonable and general situation. Under MEPC/MSPC scheme、some MSPC charts are compared and the control strategy is discussed. Whether from the statistical point of view or the economic point of view、the simulation result reveals that combining MEPC and the MGWMA control chart is more efficient than the MEPC/MEWMA control chart and the MEPC/Hotelling multivariate control chart in detecting small shifts of the mean vector.
[1] Box, G.E.P. and Kramer, T. "Statistical Process Monitoring and Feedback Adjustment -- a Discussion," Technometrics, vol. 34, pp. 251-285, 1992.
[2] Box, G.E.P., Jenkins, G.M. and Reinsel, G.C. Time Series Analysis: Forecasting and Control, Prentice-Hall, Englewood Cliffs, New Jersey, 1994.
[3] Bulter, S.W. and Stefani, J.A. "Supervisory Run-to-Run Control of Polysilicon Gate Etch Using in Situ Ellipsometry," IEEE Transactions on Semiconductor Manufacturing, vol. 7, pp. 193-201, 1994.
[4] Castagliola, P. "X-median Control Chart for Skewed Populations Using a Scaled Weighted Variance Method," International Journal of Reliability, Quality and Safety Engineering, vol. 7, pp. 237-252, 2000.
[5] Castagliola, P. "An (X-median/R)-EWMA Control Chart for Monitoring the Process Sample Median," International Journal of Reliability, Quality and Safety Engineering, vol. 8, pp. 123-135, 2001.
[6] Chang, Y.S. and Bai, D.S. "Median Control Charts for Skewed Populations," Asia Pacific Management Review, vol. 6, pp. 267-277, 2001.
[7] Choobineh, F. and Ballard, J.L. "Control-Limits of QC Charts for Skewed Distribution Using Weighted-Variance," IEEE Transactions on Reliability, vol. 36, pp. 473-477, 1987.
[8] Crosier, R.B. "Multivariate Generalizations of Cumulative Sum Quality Control Schemes," Technometrics, vol. 30, pp. 291-303, 1988.
[9] Crowder, S.V. "A Simple Method for Studying Run-Length Distributions of Exponentially Weighted Moving Average Charts," Technometrics, vol. 29, pp. 401-407, 1987.
[10] Crowder, S.V. "Design of Exponentially Weighted Moving Average Schemes," Journal of Quality Technology, vol. 21, pp. 155-162, 1989.
[11] Crowder, S.V. and Hamilton, M. "An EWMA for Monitoring a Process Standard Deviation," Journal of Quality Technology, vol.24, pp. 12-21, 1992.
[12] Del Castillo, E. and Rajagopal, R. "A Multivariate Double EWMA Process Adjustment Scheme for Drifting Processes," IIE Transactions, vol. 34, pp. 1055-1068, 2002.
[13] Hawkins, D.M. "Multivariate Quality Control Based on Regression-Adjusted Variables," Technometrics, vol. 33, pp. 61-75、1991.
[14] Healy、J.D. "A Note on Multivariate CUSUM Procedures," Technometrics, vol. 29, pp. 409-412, 1987.
[15] Hotelling, H. "Multivariate Quality Control – Illustrated by the Air Testing of Sample Bombsights," in Techniques of Statistical Analysis, Eisenhart, C., Hastay, M. W. and W. A. Wallis, Eds. McGraw-Hill, New York, pp. 111-184, 1947.
[16] Ingolfsson, A. and Sachs, E. "Stability and Sensitivity of an EWMA Controller," Journal of Quality Technology, vol. 25, pp. 271-287, 、1993.
[17] Jiang, W. and Tsui, K. L. "An Economic Model for Integrated APC and SPC Control Charts," IIE Transactions, vol. 32, pp. 505-513, 2000.
[18] Johnson, R.A. and Wichern, D.W. Applied Multivariate Statistical Analysis, Prentice-Hall, pp. 163-164, 2002.
[19] Linderman, K. and Love, T. "Economic and Economic Statistical Designs for MEWMA Control Charts," Journal of Quality Technology, vol. 32, pp. 410-417, 2000.
[20] Linderman, K. and Love, T. "Implementing Economic and Economic Statistical Designs for MEWMA Charts," Journal of Quality Technology, vol. 32, pp. 457-463, 2000.
[21] Lorenzen, T. J. and Vance, L. C. "The Economic Design of Control Charts: a Unified Approach," Technometrics, vol. 28, pp. 3-10, 1986.
[22] Lowry, C. A., Woodall, W. H., Champ, C. W. and Rigdon, S. E. "A Multivariate Exponentially Weighted Moving Average Control Chart," Technometrics, vol. 34, pp. 46-53, 1992.
[23] Lucas, J. M. and Saccucci, M. S. "Exponentially Weighted Moving Average Control Schemes: Properties and Enhancements," Technometrics, vol. 32, pp. 1-12, 1990.
[24] MacGregor, J. F. "On-line Statistical Process Control," Chemical Engineering Process, vol. 10, pp. 21-31, 1988.
[25] MacGregor, J. F. and Harris, T. J. "The Exponentially Weighted Moving Variance," Journal of Quality Technology, vol. 25, pp. 106-118, 1993.
[26] Molnau, W. E., Montgomery, D. C. and Runger, G. C. "Statistically Constrained Economic Design of the Multivariate Exponentially Weighted Moving Average Control Chart," Quality Reliability Engineering International, vol. 17, pp. 39-49, 2001.
[27] Montgomery, D. C., Keats, J. B., Runger, G. C. and Messina, W. S. "Integrating Statistical Process Control and Engineering Process Control," Journal of Quality Technology, vol. 26, pp. 79-87, 1994.
[28] Montgomery, D. C., Torng, J.C.C., Cochran, J. K. and Lawrence, F. P. "Statistically Constrained Economic Design of the EWMA Control Chart," Journal of Quality Technology, vol. 27, pp. 250-256, 1995.
[29] Montgomery, D. C. Introduction to Statistical Quality Control, 4th ed., John Wiley & Sons, New York, 2001.
[30] Ng, C. H. and Case, K. E. "Development and Evaluation of Control Charts Using Exponentially Weighted Moving Average," Journal of Quality Technology, vol. 21, pp. 242-250, 1989.
[31] Pan, R. and del Castillo, E. "Integration of Sequential Process Adjustment and Process Monitoring Techniques," Quality Reliability and Engineering International, vol. 19, pp. 371-386, 2003.
[32] Pignatiello, J. J. Jr. and Runger, G. C. "Comparisons of Multivariate CUSUM Charts," Journal of Quality Technology, vol. 22, pp. 173-186, 1990.
[33] Prabhu, S. S. and Runger, G. C. "Designing a Multivariate EWMA Control Chart," Journal of Quality Technology, vol. 29, pp. 8-15, 1997.
[34] Raich, A. and Cinar, A. "Statistical Process Monitoring and Disturbance Diagnosis in Multivariate Continuous Process," AIChE Journal, vol. 42, pp. 995-1009, 1996.
[35] Roberts, S. W. "Control Chart Tests Based on Geometric Moving Averages," Technometrics, vol. 1, pp. 239-250, 1959.
[36] Roberts, S. W. "A Comparison of Some Control Chart Procedures," Technometrics, vol. 8, pp、411-430, 1966.
[37] Robinson, P. B. and Ho, T. Y. "Average Run Lengths of Geometric Moving Average Charts by Numerical Methods," Technometrics, vol. 20, pp. 85-93, 1978.
[38] Ross, S. M. A Course in Simulation, Macmilan, New York, 1990.
[39] Sachs, E., Hu, A. and Ingolfsson, A. "Run by Run Process Control: Combining SPC and Feedback Control," IEEE Transactions on Semiconductor Manufacturing, vol. 8, pp. 26-43, 1995.
[40] Saniga, E. M. "Economic Statistical Control-Chart with an Application to X-bar and R Charts," Technometrics, vol. 31, pp. 313-320, 1989.
[41] Sheu, S. H. and Griffith, W. S. "Optimal Number of Minimal Repairs Before Replacement of a System Subject to Shocks," Naval Research Logistics, vol. 43, pp. 319-333, 1996.
[42] Sheu, S. H. "A Generalized Age and Block Replacement of a System Subject to Shocks," European Journal of Operational Research, vol. 108, pp. 345-362, 1998.
[43] Sheu, S. H. "Extended Optimal Replacement Model for Deteriorating Systems," European Journal of Operational Research, vol. 112, pp. 503-516, 1999.
[44] Sheu, S. H. and Lin, T. C. "The Generally Weighted Moving Average Control Chart for Detecting Small Shifts in the Process Mean," Quality Engineering, vol. 16, pp. 209-231, 2003.
[45] Sweet, A. L. "Control Charts Using Coupled Exponentially Weighted Moving Averages," IIE Transactions, vol. 18, pp. 26-33, 1986.
[46] Taguchi, G., Elsayed, E. A. and Hsiang, T. C. Quality Engineering in Production Systems, McGraw-Hill, New York, 1989.
[47] Tseng, S. T., Chou, R. J. and Lee, S. P. "A Study on a Multivariate EWMA Controller," IIE Transactions, vol. 34, pp. 541-549, 2002.
[48] Tsung, F. and Shi, J. "Integrated Design of Run-to-Run PID Controller and SPC Monitoring for Process Disturbance Rejection," IIE Transactions, vol. 31, pp. 517-527, 1999.
[49] Tsung, F. "Statistical Monitoring and Diagnosis of Automatic Controlled Processes Using Dynamic PCA," International Journal of Production Research, vol. 38, pp. 625-637, 2000.
[50] Woodall, W. H. and Ncube, M. M. "Multivariate CUSUM Quality Control Procedures," Technometrics, vol. 27, pp. 285-292, 1985.
[51] Wortham, A. W. and Ringer, L. J. "Control via Exponential Smoothing," The Logistics Review, vol. 7 (32), pp. 33-40, 1971.
[52] Yang, K. and Trewn, J. Multivariate Statistical Methods in Quality Management, McGraw Hill, 2004.
 
 
 
 
第一頁 上一頁 下一頁 最後一頁 top
QR Code
QRCODE

臺灣人文及社會科學引文索引資料庫

目前上線人數：794（臺灣：781，外國：13） ／ 本年度總使用人次：2625314 ／ 訪客人次（自102年09月）：68426688
國家圖書館著作權聲明 Copyright © 2013 All rights reserved.
本館地址: 100201臺北市中山南路20號． 本館地圖及交通資訊．
總機：(02)23619132．最佳瀏覽解析度為1024x768以上