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題名：多變量製程失控起始點估計與失控來源辨識之一些方法
作者：黃生
作者(外文)：Huang, Sheng
校院名稱：輔仁大學
系所名稱：商學研究所
指導教授：侯家鼎
學位類別：博士
出版日期：2012
主題關鍵詞：失控起始點；失控來源；最大概似估計法；多變量製程；M檢定統計量；類神經網路；change point；source of shift；maximum likelihood estimator；multivariate process；M test；neural network.
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多變量製程管制圖可以監控多變量分配製程，可以藉由失控訊號得知製程產生失控，進而改善製程。一旦失控訊號產生時，如何迅速且正確估計失控起始點與辨認失控的來源是兩個重要的研究議題。以往大部分的研究是估計單變量製程失控起始點的議題，較少學者對多變量製程失控起始點的估計作研究。本研究的第一個主題是應用兩個傳統多變量製程管制圖結合最大概似估計法以估計多變量常態分配製程與多項分配製程失控起始點。
另一方面，針對多種品質特性的計數資料作失控的來源的辨識，較少有學者提及。不同於以往學者多為針對多變量常態分配製程進行失控來源辨識的研究，本研究的第二個主題是針對多項分配製程失控時失控來源的辨識，提出利用M檢定統計量與類神經網路的方法來辨識多項分配製程失控時失控的來源。
針對以上主題本研究分別舉出實際應用的例子，同時也在不同的模擬情境下進行模擬分析，透過實際的模擬結果顯示，在估計多變量製程失控起始點方面，本研究所提出的新方法比傳統管制圖的方法要來的好。另外在多項分配製程失控時失控的來源的確認，本研究所提出的新方法也能夠作有效的辨識。
Correct and quick identification of the change point and the source of a process shift for a multivariate process are two important research issues and have attracted considerable attention in recent years. Although several studies have been made on the identification of the time of change point for a univariate process, there is little agreement on the one for a multivariate process. Different from most of the current research studies which focus on the estimation of the starting time of the process shifts for a univariate process, the first aim of this study is to estimate the change point for a multivariate process. Two effective approaches that combine two traditional multivariate control charts and the method of maximum likelihood are proposed to identify the time of a process variance disturbance for a multivariate normal process, and the time of a process proportion disturbance for a multinomial process.
On the other side, although several studies have been devoted to the identification of the source of process variation that deals with multiple quantitative data, however, little work has been carried out that deals with multiple correlated counts data. Diverging from most of the current research studies which focus on the identification of the source of process shifts for a multivariate normal process, the second aim of this study is to identify the source of process shifts for a multinomial process. Two detection procedures that utilize the M test method and the neural network method are proposed to identify the source of process variation for a multinomial process.
Several illustrative examples are provided to show how to apply the proposed approaches in practice. The positive results with the use of the proposed approaches are also demonstrated by a series of simulation studies. Experimental simulation results reveal that the proposed estimation approaches have better performance than the original multivariate control charts. In addition, the proposed detection procedures are able to effectively identify the source of proportion shifts for a multinomial process.
1.Alt, F. B. (1985). Multivariate quality control. Encyclopedia of Statistical Sciences, S. Kotz and N. L. Johnson, (Eds.), New York: John Wiley & Sons, Inc., 6, 110-122.new window
2.Alt, F. B., Smith, N. D., & Jain, K. (Eds.). (1998). Multivariate Quality Control. Handbook of Statistical Methods for Engineers and Scientists, 2d ed. McGraw-Hill, New York, chap. 21.
3.Anderson, T. W., (1984). An introduction to multivariate statistical analysis, 2d ed., John Wiley & Sons, New York.
4.Böhm, U., Dahm, P. F., McAllister, B. F., & Greenbaum, I. F. (1995). Identifying chromosomal fragile sites from individuals: a multinomial statistical model. Human Genetics, 95(3), 249-256.
5.Cao, L., & Tay, F. E. H. (2001). Financial Forecasting Using Support Vector Machines. Neural Computing & Applications, 184-192.
6.Caudill, M., & Butler, C. (1992). Understanding Neural Networks：Computer Explorations, 2, Cambridge, MA：MIT Press.
7.Chen, J., & Gupta, A. K. (2001). On change point detection and estimation. Communications in Statistics-Simulation and Computation, 30, 665-697.
8.Chen, L. H., & Wang, T. Y. (2004). Artificial Neural Networks to Classify Mean Shifts from Chart Signals. Computers and Industrial Engineering, 47, 195-205.
9.Cheng, C. S., & Cheng, H. P. (2008). Identifying the Source of Variance Shifts in the Multivariate Process Using Neural Network and Support Vector Machines. Expert Systems with Applications, 35(1-2), 198-206.
10.Chiu, J. E., & Kuo, T. I. (2010). Control charts for fraction nonconforming in a bivariate binomial process. Journal of Applied Statistics. 37(10), 1717-1728.
11.Cozzucoli, P. C., (2009). Process Monitoring with Multivariate p-Control Chart. International Journal of Quality, Statistics, and Reliability, 2009.
12.Duncan, A. J. (1950). A chi-square chart for controlling a set of percentages. Industrial Quality Control, 7, 11–15.
13.Freeman, J. A. & Skapura, D. M. (1992). Neural Networks Algorithms, Applications, and Programming Techniques, MA：Addison-wesley Publishing Company.
14.Fuchs, C., & Kenett, R. (1980). A test for detecting outlying cells in the multinomial distribution and two-way contingency tables. Journal of the American Statistical Association, 75, 395-398.
15.Gadre, M. P., & Rattihalli, R. N. (2005). Some group inspection based multi-attribute control charts to identify process deterioration. Economic Quality Control, 20(2), 191-204.
16.Gryna, M. F. (2001). Quality Planning & Analysis: From Product Development Through Use, McGraw-Hill.
17.Hornik, K., Stinchcombe, M., & White, H. (1989). Multilayer Feedforward Networks are Universal Approximation. Neural Networks, 2, 336-359.
18.Hotelling, H. (Eds.). (1947). Multivariate Quality Control. Techniques of Statistical Analysis, Eisenhart, Hastay, and Wallis, New York: McGraw Will, 111-184.
19.Hou, C. D., Chiang, J. & Tai, J. J. (2001). Identifying Chromosomal Fragile Sites from a Hierarchical-Clustering Point of View. Biometrics, 57(2), 435–440.
20.Hou, C. D., Chiang, J., & Tai, J. J. (1999). Testing the nonrandomness of chromosomal breakpoints using highest observed breakages. Human Genetics, 104(4), 350-355.
21.Jackson, J. E. (1985). Multivariate Quality Control. Communications in Statistical Theory Methods, 14, 2657-2688.
22.Johnson, R. A., ＆ Wichern, D. W. (2007). Applied Multivariate Statistical Analysis, Pearson Prentice Hall, Inc.
23.Klimasauskas, C. C. (1991). Applying neural networks, Part 3: Training a neural network, PC-AI, 20-24.
24.Korin, B. P. (1968). On the distribution of a statistic used for testing a covariance matrix, Biometrika, 55, 171-178.
25.Lu, X. S., Xie, M., Goh, T. N., & Lai, C. D. (1998). Control chart for multivariate attribute processes. International Journal of Production Research, 36(12), 3477-3489.
26.Marcucci, M. (1985). Monitoring multinomial processes. Journal of Quality Technology, 17(2), 86–91.
27.Montgomery, D. C. (2005). Introduction to Statistical Quality Control, Wiley, New York.
28.Montgomery, D. C., & Wadsworth, H. M., (1972). Some techniques for multivariate quality control applications, ASQC Technical Conference Transactions, Washington, D. C..
29.Nedumaran, G., Pignatiello, J. J. Jr., ＆ Calvin, J. A. (2000). Identifying the time of a step change with control charts. Quality Engineering, 13(2), 153-159.
30.Nelson, L. S. (1987). A chi-square control chart for several proportions. Journal of Quality Technology, 19(4), 229–231.
31.Noorossana, R., Saghaei, A., Paynabar, K., ＆ Abdi, S. (2009). Identifying the period of a step change in high-yield processes. Quality and Reliability Engineering International, 25(7), 875-883.
32.Pignatiello, J. J. Jr., & Samuel, T. R. (2001a). Identifying the time of a step change in the process fraction nonconforming. Quality Engineering, 13(3), 357-365.
33.Pignatiello, J. J. Jr., & Samuel, T. R. (2001b). Estimation of the change point of a normal process mean in SPC applications. Journal of Quality Technology, 33(1), 82-95.new window
34.Pignatiello, J. J. Jr., ＆ Simpson, J. R. (2002). A magnitude-robust control chart for monitoring and estimating step changes for normal process means. Quality and Reliability Engineering International, 18(6), 429-441.
35.Raz, T., & Wang, J. (1990). Probabilistic and membership approaches in the construction of control charts for linguistic data. Production Planning and Control. 1, 147–157.new window
36.Rosenblatt, F. (1957). The Perception, a Probabilistic Model for Information storage and Organization in Brain. Psych. Review, 62, 386-408.
37.Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986). Learning Internal Representations by Error Propagation, Parallel Distributed Processing. Cambridge, MA：MIT Press.
38.Runger, G. C., Alt, F. B., & Montgomery, D. C. (1996). Contributors to a Multivariate Statistical Process Control Signal. Communications in Statistics: Theory & Methods. 25(10), 2203-2213.
39.Samuel, T. R., Pignatiello, J. J., & Calvin, J. A. (1998a). Identifying the time of a step change with control charts. Quality Engineering, 10(3), 521-527.
40.Samuel, T. R., Pignatiello, J. J., & Calvin, J. A. (1998b). Identifying the time of a step change in a normal process variance. Quality Engineering, 10(3), 529-538.
41.Shao, Y. E., ＆ Hou, C. D. (2006). Estimation of the starting time of a step change disturbance in a gamma process. Journal of the Chinese Institute of Industrial Engineers, 23(4), 319-327.
42.Shao, Y. E., ＆ Hou, C. D. (2009). Estimation of the change point of a uniform process using the EWMA chart and MLE. ICIC Express Letters, 3(3(A)), 451-456
43.Shao, Y. E., ＆ Hou, C. D. (2011). A combined MLE and EWMA chart approach to estimate the change point of a gamma process with individual observations. International Journal of Innovative Computing, Information and Control, 7(5(A)), 2109-2122.
44.Shao, Y. E., & Hsu, B. S. (2009). Determining the Contributors for a Multivariate SPC Chart Signal Using Artificial Neural Networks and Support Vector Machine. International Journal of Innovative Computing, Information and Control, 5(12B), 4899-4906.
45.Shao, Y. E., Hou, C. D., ＆ Wang, H. J. (2004). A study on identifying the time of a step change disturbance with S Control charts and MLE method. Journal of the Chinese Institute of Industrial Engineers, 21(3), 349-357.
46.Shao, Y. E., Hou, C. D., Chao, C. H. ＆ Chen Y. J. (2011). A Decomposition Approach for Identifying the Sources of Variance Shifts in a Multivariate Process. ICIC Express Letters, 5(4(A)), 971-975.
47.Shao, Y. E., Hou, C.-D., ＆ Wang, H. J. (2006). Estimation of the change point of a gamma process by using the S control chart and MLE. Journal of the Chinese Institute of Industrial Engineers, 3(3), 207-214.
48.Shao, Y. E., Huang, H. Y., Hou, C. D., Lin, K. S. ＆ Tsai, J. E. (2011). Change point determination for an attribute process. ICIC Express Letters, 5(9(A)), 3117-3122.
49.Shewhart, W. A. (1931). Economic Control of Quality of Manufactured Product. Van Nostrand, New York.
50.Taleb, H., Limam, M. Hirota, K. (2006). Multivariate fuzzy multinomial control charts. Quality Technology and Quantitative Managemen,. 3, 437-453.
51.Topalidou, E., & Psarakis, S. (2009). Review of multinomial and multiattribute quality control charts. Quality and Reliability Engineering, 25(7), 773-804.
52.Wang, J. H., & Raz, T. (1990). On the construction of control charts using linguistic variables. International Journal of Production Researc, 28, 477–487.
53.Wang, T. Y., & Chen, L. H. (2002). Mean shifts detection and classification in multivariate process: a neural-fuzzy approach. Journal of intelligent manufacturing, 13, 211-221.
54.Wong, F. S. (1991). Time Series Forecasting Using Backpropagation Neural Networks. Neurocomputing, 2, 147-159.
55.Woodall, W. H., & Neube, M. M. (1985). Multivariate CUSUM Quality Control Procedures. Technometrics, 27, 285-292.
56.Zhang, G., Patuwo, B. E., & Hu, M. Y. (1998). Forecasting with Artificial Neural Networks: The State of the Art. International Journal of Forecasting, 14(1), 35-62.new window
57.Zorriassatine, F., Tannock, J. D. T., & O’Brien, C. (2003). Using novelty detection to identify abnormalities caused by mean shifts in bivariate processes. Computer & industrial engineering, 44, 385-408.
58.Surtihadia, J., Raghavachari, M., & Runger, G. (2004). Multivariate control charts for process dispersionInternational. Journal of Production Research, 42(15), 2993-3009.
 
 
 
 
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