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    題名: 監控兩相依品質變數之變異數比之 EWMA 管制圖
    EWMA Control Charts for Monitoring the Ratio of Variances of Two Correlated Quality Variables
    作者: 周鈺宸
    Chou, Yu-Chen
    貢獻者: 楊素芬
    Yang, Su-Fen
    周鈺宸
    Chou, Yu-Chen
    關鍵詞: 變異數比值
    二元分配
    管制圖
    平均連串長度
    Ratio of variances
    bivariate distributions
    control chart
    average run length
    日期: 2024
    上傳時間: 2024-09-04 14:56:47 (UTC+8)
    摘要: 在統計製程管制的研究領域中,兩相依品質變數之變異數比值的追蹤 在某些實務製程中是重要的,但是文獻中尚未有見探討。因此,我們的研 究旨在探索兩相依品質變異數比的變化,以監控製程是否失控。在實務上, 製程穩定性分析、參數優化以及生產效率評估等應用,都需要追蹤變異數 比值。
    在本研究中,我們分別提出兩種方法,建立不同的 EWMA 變異數比例 管制圖。第一種,我們提出使用兩相依品質變數之樣本變異數之差異之分 配建立 EWMA 變異數比例管制圖,以追蹤兩相依品質變數之母體變異數之 比。第二種方法考慮符號檢定方法(sign test method),根據兩相依品質變 數之樣本變異數的差異是否大於其期望值,並定義指標變數分配以建立符 號管制圖。我們分別再以數值分析評估在二元常態、伽馬、偏常態母體分 配下,這兩種管制圖的管制界線與失控的偵測力。最後,我們以實際的半 導體數據驗證這兩種管制圖的應用與失控的偵測力。
    In statistical process control, monitoring the ratio of variances of correlated quality variables is crucial for some practical processes. However, this topic has not been explored in literature. Our study aims to investigate the ratio of variances of two correlated quality variables to monitor whether the ratio of two variances process is out of control. In practice, monitoring the ratio of two variances is essential for process stability analysis, parameter optimization and production efficiency evaluation.
    In this research, we propose two methods to establish EWMA ratio of variances control charts without the specified distributions of quality variables. The first method uses the distribution of the difference in the sample variances of two correlated quality variables to construct an EWMA ratio of variances control chart to monitor the population ratio of variances of two correlated quality variables. The second method considers the sign method to construct a sign-based control chart, where an indicator variable distribution is defined based on whether the difference in sample variances of two correlated quality variables exceeds its expected value. We conduct numerical analyses to calculate the control limits and evaluate detection capabilities of these two EWMA control charts under the bivariate normal, gamma, and skew-normal population distributions. Finally, we validate the application and detection capabilities of these two proposed control charts by using real semiconductor data.
    參考文獻: [1] Alt, F. B., & Smith, N. D. (1988). 17 multivariate process control. In P. R. Krishnaiah & L. N. Rao (Eds.), Handbook of statistics (Vol. 7, pp. 333-351). North-Holland.
    [2] Aitchison, J. (2005). A concise guide to compositional data analysis. In Compositional Data Analysis Workshop.
    [3] Azzalini, A., & Capitanio, A. (1999). Statistical applications of the multivariate skew normal distribution. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 61(3), 579-602.
    [4] Azzalini, A., & Valle, A. D. (1996). The multivariate skew-normal distribution. Biometrika, 83(4), 715-726.
    [5] Celano, G., Castagliola, P., Faraz, A., & Fichera, S. (2014). Statistical performance of a control chart for individual observations monitoring the ratio of two normal variables. Quality and Reliability Engineering International, 30(8), 1361-1377.
    [6] Chowdhury, S., Mukherjee, A., & Chakraborti, S. (2014). A new distribution‐ free control chart for joint monitoring of unknown location and scale parameters of continuous distributions. Quality and Reliability Engineering International, 30(2), 191-204.
    [7] Costa, A. F. B., & Machado, M. A. G. (2011). A control chart based on sample ranges for monitoring the covariance matrix of the multivariate processes. Journal of Applied Statistics, 38(2), 233-245.
    [8] Crosier, R. B. (1988). Multivariate generalizations of cumulative sum quality- control schemes. Technometrics, 30(3), 291-303.
    [9] Fan, J., Shu, L., Yang, A., & Li, Y. (2021). Phase I analysis of high-dimensional covariance matrices based on sparse leading eigenvalues. Journal of Quality Technology, 53(4), 333-346.
    [10] Hoteling, H. (1947). Multivariate quality control, illustrated by the air testing of sample bombsights. Techniques of statistical analysis, 111-184.
    [11] Kenney, J. F. (1939). Mathematics of statistics. D. van Nostrand.
    [12] Lee, R. Y., Holland, B. S., & Flueck, J. A. (1979). Distribution of a ratio of correlated gamma random variables. SIAM Journal on Applied Mathematics,
    36(2), 304-320.
    [13] Liu, R. Y. (1995). Control charts for multivariate processes. Journal of the
    American Statistical Association, 90(432), 1380-1387
    [14] Li, B., Wang, K., & Yeh, A. B. (2013). Monitoring the covariance matrix via
    penalized likelihood estimation. IIE Transactions, 45, 132-146.
    [15] Li, Z., Zou, C., Wang, Z., & Huwang, L. (2013). A multivariate sign chart for monitoring process shape parameters. Journal of Quality Technology, 45(2), 149-165.
    [16] Lowry, C. A., & Montgomery, D. C. (1995). A review of multivariate control charts. IIE Transactions (Institute of Industrial Engineers), 27(6), 800-810
    [17] Lowry, C. A., Woodall, W. H., Champ, C. W., & Rigdon, S. E. (1992). A
    multivariate exponentially weighted moving average control chart.
    Technometrics, 34(1), 46-53.
    [18] Maboudou-Tchao, E. M., & Agboto, V. (2013). Monitoring the covariance
    matrix with fewer observations than variables. Computational Statistics & Data Analysis, 64, 99-112.
    108
    [19] McCann, M., & Johnston, A. (2008). SECOM Data Set Center for Machine Learning and Intelligent Systems. University of California, Irvine, CA.
    [20] Melo, M. S., Ho, L. L., & Medeiros, P. G. (2017). Max D: an attribute control chart to monitor a bivariate process mean. The International Journal of Advanced Manufacturing Technology, 90, 489-498.
    [21] Mukherjee, A., & Chakraborti, S. (2012). A distribution‐free control chart for the joint monitoring of location and scale. Quality and Reliability Engineering International, 28(3), 335-352.
    [22] Nguyen, H. D., Nadi, A. A., Tran, K. P., Castagliola, P., Celano, G., & Tran, K. D. (2021). The effect of autocorrelation on the Shewhart-RZ control chart. arXiv preprint arXiv:2108.05239.
    [23] ÖKSOY, D., Boulos, E., & DAVID PYE, L. (1993). Statistical process control by the quotient of two correlated normal variables. Quality Engineering, 6(2), 179-194.
    [24] Pignatiello Jr, J. J., & Runger, G. C. (1990). Comparisons of multivariate CUSUM charts. Journal of Quality Technology, 22(3), 173-186.
    [25] Riaz, M., Zaman, B., Raji, I.A., Omar, M.H., Mehmood, R., Abbas, N. (2022). An Adaptive EWMA Control Chart Based on Principal Component Method to Monitor Process Mean Vector. Mathematics, 10(12), 2025.
    [26] Roberts, S. W. (1959). Control Chart Tests Based on Geometric Moving Averages. Technometrics, 239-250.
    [27] Rose, C., & Smith, M. D. (2002). MathStatica: mathematical statistics with mathematica. In Compstat: Proceedings in Computational Statistics (pp. 437- 442). Physica-Verlag HD.
    [28] Ross, G. J., & Adams, N. M. (2012). Two nonparametric control charts for detecting arbitrary distribution changes. Journal of Quality Technology, 44(2), 102-116.
    [29] Shewhart, W. A. (1924). Some applications of statistical methods to the analysis of physical and engineering data. Bell System Technical Journal, 3(1), 43-87. Tran,
    [30] Shu, L., & Fan, J. (2018). A distribution‐free control chart for monitoring high‐ dimensional processes based on interpoint distances. Naval Research Logistics (NRL), 65(4), 317-330.
    [31] Susanty, A., Ulkhaq, M. M., & Amalia, D. (2018). Using multivariate control chart to maintain the quality of drinking water in accordance with standard. International Journal of Applied Science and Engineering, 15(2), 83-94.
    [32] Tran, K. P., Castagliola, P., & Celano, G. (2016). Monitoring the ratio of two normal variables using run rules type control charts. International Journal of Production Research, 54(6), 1670-1688.
    [33] Wang, S., & Reynolds Jr., M. R. (2013). A GLR control chart for monitoring the mean vector of a multivariate normal process. Journal of Quality Technology, 45(1), 18-33.
    [34] Yang, S. F. (2016). An improved distribution-free EWMA mean chart. Communications in Statistics-Simulation and Computation, 45(4), 1410-1427. 33.
    [35] Yang, S. F., & Arnold, B. C. (2016). A new approach for monitoring process variance. Journal of Statistical Computation and Simulation, 86(14), 2749-2765.
    [36] Yang, S. F., Lin, J. S., & Cheng, S. W. (2011). A new nonparametric EWMA sign control chart. Expert Systems with Applications, 38(5), 6239-6243
    110
    [37] Yang, S. F., Lin, Y. C., & Yeh, A. B. (2021). A Phase II depth‐based variable dimension EWMA control chart for monitoring process means. Quality and Reliability Engineering International, 37(6), 2384-2398.
    [38] Yang, S. F., Yeh, A. B., & Chou, C. C. (2023). A phase II multivariate EWMA chart for monitoring multi-dimensional ratios of process means with individual observations. Computers & Industrial Engineering, 183, 109490
    [39] Yen, C. L., & Shiau, J. J. H. (2010). A multivariate control chart for detecting increases in process dispersion. Statistica Sinica, 1683-1707.
    描述: 碩士
    國立政治大學
    統計學系
    111354021
    資料來源: http://thesis.lib.nccu.edu.tw/record/#G0111354021
    資料類型: thesis
    顯示於類別:[統計學系] 學位論文

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