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研究生:陳秉泰
研究生(外文):Bing-Tai Chen
論文名稱:變動抽樣間隔之模糊管制圖
論文名稱(外文):A Fuzzy Control Chart with Variable Sampling Interval
指導教授:古東源古東源引用關係
指導教授(外文):Tong-Yuan Koo
學位類別:碩士
校院名稱:國立雲林科技大學
系所名稱:工業工程與管理研究所碩士班
學門:工程學門
學類:工業工程學類
論文種類:學術論文
論文出版年:2005
畢業學年度:93
語文別:中文
論文頁數:46
中文關鍵詞:管制圖變動抽樣間隔模糊理論
外文關鍵詞:Fuzzy Set TheoryVariable Sampling IntervalControl Chart
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本研究主要針對傳統 管制圖偵測製程平均中、小幅度偏移能力較差之缺點,將模糊理論及變動抽樣間隔觀念導入傳統 管制圖中,建構變動抽樣間隔之模糊管制圖,藉由模糊理論同時監控製程品質特性中的平均數、標準差及連續趨勢等統計量,並利用變動抽樣間隔觀念降低偵測製程偏移所需之時間,以提昇傳統 管制圖績效。研究結果發現,變動抽樣間隔模糊管制圖,無論在平均偏移、變異數偏移或者趨勢偏移等製程異常狀況下,其偵測製程異常之績效都優於傳統 管制圖、變動抽樣間隔 管制圖以及模糊管制圖。
Statistical quality control charts are widely used in monitoring production proc-ess. In this thesis, a variable sampling interval fuzzy control chart is proposed to com-bine Fuzzy set theory and variable sampling interval method to improve the capability of detection in monitoring assignable causes. By using Fuzzy set theory, we can monitor the mean, variance and trend at the same time. By judging stability of the outputs of fuzzy control chart, the variable sampling interval method is adopted to reduce the time for detecting the out of control signal. The experimental results dem-onstrate that the performance of the proposed fuzzy control chart is better than that of the traditional control chart or adaptive control chart.
中文摘要 I
英文摘要 II
致 謝 詞 III
圖 目 錄 V
表 目 錄 VI
第一章 緒論 1
第二章 文獻探討 3
2.1適應性 管制圖 3
2.1.1變動抽樣間隔 管制圖 4
2.1.2 適應性管制圖之績效衡量指標 6
2.2 模糊管制圖 8
2.2.1 各類模糊管制圖之比較 14
第三章 研究方法 16
3.1 模糊管制圖 17
3.1.1 模糊集合 17
3.1.2 歸屬函數 20
3.1.3 模糊規則 22
3.1.4 解模糊化 23
3.2 變動抽樣間隔 24
3.4 衡量指標 25
3.5 績效比較 26
第四章 結果與分析 27
4.1 模糊管制圖之使用 27
4.2模擬環境設定 29
4.3 研究結果 30
4.3.1 平均數偏移 31
4.3.2 變異數偏移 32
4.3.3 趨勢偏移 33
4.4 績效分析 34
第五章 結論與未來方向 35
參考文獻 36
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[2] Duncan, A. J., 1971, “The economic design of -charts when there is a multiplic-ity of assignable Causes”, Journal of the American Statistical Association, vol. 66 Issue 333, pp. 107-121.
[3] El-Shal, S. M. and Morris, A. S., 2000, “A fuzzy rule-based algorithm to improve the performance of statistical process control in quality systems”, Journal of Intelli-gent and Fuzzy Systems, vol. 9, pp. 207–223.
[4] Hassen, T., 2002, “On fuzzy and probabilistic control charts”, International Journal of Production Research, vol. 40, pp. 2849–2863.
[5] Kim, D. and Sehun, R., 2002, “Design of a robust fuzzy controller for the arc stabil-ity of CO2 welding process using the Taguchi method”, IEEE Transactions on Sys-tems, Man, and Cybernetics—Part B: Cybernetics, vol. 32, pp. 157-162.
[6] Marion, R. and Zachary, G. S., 2001, “Monitoring the process mean and variance using individual observations and variable sampling intervals,” Journal of Quality Technology, vol. 33, pp. 181-205.
[7] Montgomery, D. C., 2001, Introduction to Statistical Quality Control, 4th Edition, John Wiley & Sons Inc, New York.
[8] Prabhu, S. S., Montgomery, D. C. and Runger, G. C., 1997, “Economic-statistical design of an adaptive chart”, International Journal of Production Economics, vol. 49, pp. 1-15.
[9] Reynolds, M. R., Amin, R. W., Arnold, J. C. and Nachlas, J. A. , 1988, “ charts with variable sampling intervals”, Technometrics, vol. 30, pp. 181-192.
[10] Tagaras, G.., 1998, “A survey of recent developments in the design of adaptive con-trol charts.” Journal of Quality Technology, vol. 30, pp. 212-231.
[11] Tannock, J. D. T., 2003, “A fuzzy control charting method for individuals”, Inter-national Journal of Production Research, vol. 41, pp. 1017–1032.
[12] Tzvi, R. and Wang, J. H., 1990, “Probabilistic and membership approaches in the construction of control charts for linguistic data”, Production Planning & Control, vol. 1, pp. 147-157.
[13] Woodall, W. H., 2000, “Controversies and contradictions in statistical process con-trol”, Journal of Quality Technology, vol. 32, pp. 341-350.
[14] Zadeh, L. A., 1965, “Fuzzy sets”, Information and Control, vol. 8, pp. 338-353.
[15] 林裕章、周昭宇, 2002, “適應性 管制圖之回顧與評估”, 中國統計學報,第40卷第3期, pp.361-390。
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