當製程有一可歸屬原因發生時，一般常見之情況為，製程之品質特徵值發生一固定位移變動，使該值脫離穩定狀態下之目標規格值，而另一種情況為，該值發生一隨時間而變動之位移，稱其參數漂移，使其隨時間改變下逐漸遠離目標規格值，對於漂移情況而言，目前多數常見之管制圖對其之監測效果較無效率，原因在於，這些管制圖大多以假設參數為固定位移之變動所致，而且就現實之產業情況來說，存在許多需要去監測參數漂移之情況，如：航太產業中，引擎葉片之生產條件嚴苛，設備刀具之磨損將造成品質特徵值之改變，因此對於品質參數之變化須精準監控，故本論文依此考量之下，以製程平均數變動為例，建構設計一用以監測平均數發生線性漂移情況之管制圖，稱IPC-D管制圖，並將其與文獻中之相關管制圖進行監測能力比較。本論文假設製程變數服從常態分配，於已進入製程穩定狀態下時，考慮平均數即時監測之情況，管制圖之建構方法為應用資訊理論中，Kullback-Leibler Information(K-L distance)之概念建構管制界限及其檢定統計量，而其中之計算流程也為管制圖之主體架構，將提供一較貼近現實情況之樣本計算流程，為採取一由後往前計算樣本之方式，因製程之期數不同，需要由不同之樣本資訊，估計製程漂移率與K-L distance，該流程之優點為能妥善利用最新之樣本資訊。管制圖建構完成後，其監測能力將與其他管制圖進行比較，本論文以平均連串長度(average run length, ARL)，做為評估監測能力之指標，在比較前，須調整管制界限使得ARL值與欲比較之管制圖相同，但因IPC-D管制圖之ARL值及管制界限無一解析解可求得，故透過蒙地卡羅方法，以百萬次之模擬，估計ARL值及管制界限，藉由調整型一誤差值，使得相對應之管制界限能得到欲比較之值。最後在結果發現，IPC-D管制圖之監測能力，在整體上優於所比較之管制圖，在一相對廣泛之平均數漂移範圍中有良好之表現，但對於漂移率遽增之情況而言，監測能力將有下降之趨勢，但是於中小速率之漂移變化而言，IPC-D管制圖為一適合且有效用於此情況之工具之一。

The objective of this thesis is to investigate the design and the performance of control chart with respect to a statistical process control (SPC) monitoring problem. In particular, this thesis develops a control chart (called the IPC-D chart) for monitoring the normal process mean subject to linear drift. We construct the IPC-D chart derived from Kullback-Leibler information (K-L distance). The performance of IPC-D chart which is measured by average run length (ARL) is compared to generalized likelihood ratio (GLR) charts, cumulative sum (CUSUM) charts and cumulative score (CUSCORE) charts designed for drift detection. By the measure of performance with IPC-D chart, we also compare GLR chart for monitoring a normal process mean subject to linear drift (called the GLR-D chart) since both of them require only a control limit to be specified. In terms of the ARL for detection, the IPC-D chart has better performance for a wide range of drift rates relative to the GLR-D chart when the out-of-control process is truly a linear drift. The IPC-D chart does not require specification of any tuning parameters, and has the advantage that, estimate of the drift rate is immediately available. An equation is provided to accurately approximate the control limit given a in-control ARL so that the IPC-D chart can be conveniently used. A numerical example is used to show how to work the IPC-D chart.

中文文獻：
李庭媁(2014)，應用資訊理論於管制圖之建構，國立成功大學工業與資訊管理學系碩士論文。
翁奕軒(2015)，應用資訊理論於建構同時監控製程平均數及製程變異數之管制圖，國立成功大學工業與資訊管理學系碩士論文。
廖可歆(2016)，應用資訊理論於建構多變量管制圖，國立成功大學工業與資訊管理學系碩士論文。
英文文獻：
Alwan, L. C., Ebrahimib, N., & Soofia, E. S. (1998). Information theoretic framework for process control. European Journal of Operational Research, 111, 3, 526-542.
Amiri, A., & Khosravi, R. (2012). Estimating the change point of the cumulative count of a conforming control chart under a drift. Scientia Iranica, 19, 3, 856-861.
Amiri, A., Mehrjoo, M., & Pasek, Z. J. (2013). Modifying simple linear profiles monitoring schemes in phase II to detect decreasing step shifts and drifts. International Journal of Advanced Manufacturing Technology, 64, 1323-1332.
Arizono, I., & Ohta, H. (1989). A test for normality based on Kullback-Leibler information. American Statistician, 43, 1, 20-22.
Bissell, A. F. (1984). The performance of control charts and CUSUMs under linear trend. Applied Statistics-Journal of the Royal Statistical Society Series C, 33, 145–151.
Box, G. E. P., & Jenkins, G. M. (1966). Models for prediction and control: VI diagnostic checking. Technical Report No. 99, The University of Wisconsin Madison, 27p.
Box, G., & Rami ́rez, J. (1992). Cumulative score charts. Quality and Reliability Engineering International, 8, 17-27.
Brook, D., & Evans, D.A. (1972). An approach to the probability distribution of CUSUM run length. Biometrika, 59, 539-549.
Davis, R. B., & Woodall, W. H. (1988). Performance of the control chart trend rule under linear shift. Journal of Quality Technology, 20, 260-262.
Fahmy, H. M., & Elsayed, E. A. (2006). Detection of linear trends in process mean. International Journal of Production Research, 44, 487–504.
Gan, F. F. (1991). EWMA control chart under linear drift. Journal of Statistical Computation and Simulation, 38, 181-200.
Gan, F. F. (1992). CUSUM control charts under linear drift. Statistician, 41, 1, 71-84.
Gan, F. F. (1996). Average run lengths for cumulative sum control charts under linear trend. Applied Statistics-Journal of the Royal Statistical Society Series C, 45, 505–512.
Hawkins, D. (1993). Cumulative sum control charting: An underutilized SPC tool. Quality Engineering, 5, 3, 463-477.
He, F., Shu, L., & Tsui, K. L. (2014). Adaptive CUSUM charts for monitoring linear drifts in Poisson rates. International Journal Of Production Economics, 148, 14-20.
Heskett, J. L., Jones, T. O., Loveman, G. W., Sasser, W. E., & Schlesinger, L. A. (1994). Putting the service-profit chain to work. Harvard Business Review, March-April, 164–174.
Huang, W., Shu, L., & Jiang, W. (2012). Evaluation of exponentially weighted moving variance control chart subject to linear drifts. Computational Statistics & Data Analysis, 56, 12, 4278-4289.
IMD website, < https://www.imd.org/?MRK_CMPG_SOURCE= >, September 25, 2017 visited.
Kanagawa, A., Arizono, I., & Ohta, H. (1997). Design of the control chart based on Kullback-Leibler information. Frontiers in Statistical Quality Control 5, Physica-Verlag, Heidelberg, 183-192.
Kazemi, M. S., Bazargan, H., & Yaghoobi, M. A. (2014). Estimating the drift time for processes subject to linear trend disturbance using fuzzy statistical clustering. International Journal of Production Research, 52, 11, 3317-3330.
Kullback, S. (1978). Information Theory and Statistics, Peter Smith, Gloucester 409p.
Kullback, S., & Leibler, R. A. (1951). On information and sufficiency. Annals of Mathematical Statistics, 22, 1, 79–86.
Kupperman, M. (1956). Further applications of information-theory to multivariate-analysis and statistical-inference. Annals of Mathematical Statistics, 27, 4, 1184-1186.
Lorden, G. (1971). Procedures for reacting to a change in distribution. The Annals of Mathematical Statistics, 42, 1897-1908.
Lucas, J. M., & Saccucci, M. S. (1990). Exponentially weighted moving average control schemes: Properties and enhancements. Technometrics, 32, 1, 1-12.
Montgomery, D. C. (2009). Introduction to Statistical Quality Control, 6th ed, John Wiley & Sons, Inc., United States of America 754p.
Page, E. S. (1954). Continuous inspection schemes. Biometrika, 41, 100-115.
Reynolds, M. R., & Lou, J. Y. (2010). An evaluation of a GLR control chart for monitoring the process mean. Journal of Quality Technology, 42, 287–310.
Reynolds, M. R., & Stoumbos, Z. G. (2001). Individuals control schemes for monitoring the mean and variance of processes subject to drifts. Stochastic Analysis and Applications, 19, 863–892.
Roberts, S. W. (1959). Control chart tests based on geometric moving averages. Technometrics, 42, 97-102.
Shannon, C. E. (1948). A mathematical theory of communication. Bell System Technical Journal, 27, 379-423 & 623-656.
Su, Y., Shu, L., & Tsui, K. L. (2011). Adaptive EWMA procedures for monitoring processes subject to linear drifts. Computational Statistics & Data Analysis, 55, 10, 2819-2829.
Takemoto, Y., & Arizono, I. (2005). A study of multivariate control chart based on Kullback–Leibler information. The International Journal of Advanced Manufacturing Technology, 25, 1205-1210.
Watakabe, K., & Arizono, I. (1999). The power of the control chart based on the log-likelihood ratio statistic. Naval Research Logistics, 46, 8, 928-951.
Willsky, A. S., & Jones, H. L. (1976). Generalized likelihood ratio approach to detection and estimation of jumps in linear-systems. IEEE Transactions on Automatic Control, 21, 108-112.
Xu, L., Wang, S., & Reynolds, M. R. (2013). A generalized likelihood ratio control chart for monitoring the process mean subject to linear drifts. Quality and Reliability Engineering International, 29, 4, 545-553.
Zou, C., Liu, Y., & Wang, Z. (2009). Comparisons of control schemes for monitoring the means of processes subject to drifts. Metrika, 70, 141–163.