臺灣博碩士論文加值系統

識別非隨機的管制圖樣式（control chart pattern, CCP）是一個重要的議題，許多學者在管制圖樣式辨識的相關研究中，最常使用的方法為類神經網路，而類神經網路又分為監督式網路學習與非監督式網路學習，兩者辨識效果都有優異的表現。相關研究中大多假設製程數據為相互獨立，但是在連續生產製程中常見自相關的製程數據，故本研究應用支撐向量機與自組織映射網路圖於自相關製程中辨識管制圖樣式，探討監督式網路學習與非監督式網路學習辨識管制圖樣式的績效。
本研究提出的方法先使用小波分析進行資料前處理以萃取其特徵值，減少訊號的噪音，再使用其結果作為支撐向量機與自組織映射網路圖的輸入項，以提升辨識效果。研究結果顯示，未使用小波分析之平均辨識正確率有80%以上，使用小波分析後之平均辨識正確率達94%以上，因此，小波分析能有效提升辨識正確率。

Recognizing unnatural control chart patterns is an important issue. Many scholars used the most common method which is the neural network to recognize control patterns. The neural network has supervised learning network and unsupervised learning network. Both of them have excellent performance. Most of the studies assume that the process data are mutually independent, but autocorrelation process data are common in continuous processes. This study uses Support Vector Machines and Self-organizing Map to recognize control chart patterns in the autocorrelation process, and explore the performance of supervised learning network and unsupervised learning network in recognizing patterns.
The proposed method uses wavelet analysis for data preprocessing to extract eigenvalues and reduce signal noise, and then uses the results as input terms of support vector machines and self-organizing image network graph to improve the recognition effect. The results show that the average recognition accuracy without wavelet analysis is more than 80%. The average recognition accuracy of wavelet analysis is over 94%. Thus, wavelet analysis can effectively improve the recognition accuracy.

目錄
摘要 i
Abstract ii
目錄 iii
表目錄 v
圖目錄 i
第一章 緒論 1
1.1 研究背景與動機 1
1.2 研究目的 3
1.3 研究架構 4
第二章 文獻探討 5
2.1 管制圖與管制圖之非隨機樣式（CCPR） 5
2.1.1 傳統管制圖 5
2.1.2 管制圖之非隨機樣式（CCPR） 5
2.2 支撐向量機 8
2.3 自組織映射網路圖 12
2.4 管制圖樣式辨識績效 15
2.4.1 支撐向量機辨識績效 15
2.4.2 自組織映射網路圖辨識績效 16
2.5 小波分析 16
2.6 結合小波分析於管制圖樣式辨識 18
第三章 研究方法 20
3.1 研究流程 20
3.2 製程數據產生與辨識視窗 22
3.2.1 自相關數據 22
3.2.2 管制圖樣式 23
3.2.3 辨識視窗大小 24
3.3 小波分析 25
3.4 辨識系統 26
3.4.1 支撐向量機 26
3.4.2 自組織映射網路圖 28
3.5 效益評估方法 30
第四章 研究結果 31
4.1 支撐向量機辨識績效 31
4.2 自組織映射網路圖辨識績效 33
4.3 小波分析進行前處理之辨識績效 34
第五章 結論與建議 40
5.1 結論 40
5.2 建議 41
參考文獻 42
附錄 46

1.郭琇靜&顧瑞祥(2007年11月)。應用支援向量機於管制圖異常圖形之辨識。論文發表於原國立新竹教育大學舉辦之「華民國品質學會第 43 屆年會暨第 13 屆全國品質管理研討會」，原新竹教育大學。
2.Al-Assaf, Y. (2004). Recognition of control chart patterns using multi-resolution wavelets analysis and neural networks. Computers & Industrial Engineering, 47(1), 17-29.
3.Byun, H., & Lee, S. W. (2002). Applications of support vector machines for pattern recognition: A survey. In Pattern recognition with support vector machines (pp. 213-236). Springer, Berlin, Heidelberg.
4.Cheng, H. P., & Cheng, C. S. (2009). Control chart pattern recognition using wavelet analysis and neural networks. 品質學報, 16(5), 311-321.
5.Cuentas, S., Peñabaena-Niebles, R., & Garcia, E. (2017). Support vector machine in statistical process monitoring: a methodological and analytical review. The International Journal of Advanced Manufacturing Technology, 91(1-4), 485-500.
6.Dittenbach, M., Rauber, A., & Merkl, D. (2002). Uncovering hierarchical structure in data using the growing hierarchical self-organizing map. Neurocomputing, 48(1-4), 199-216.
7.Du, S., Huang, D., & Lv, J. (2013). Recognition of concurrent control chart patterns using wavelet transform decomposition and multiclass support vector machines. Computers & Industrial Engineering, 66(4), 683-695.
8.Guh, R. S., & Shiue, Y. R. (2005). On-line identification of control chart patterns using self-organizing approaches. International Journal of Production Research, 43(6), 1225-1254.
9.Hachicha, W., & Ghorbel, A. (2012). A survey of control-chart pattern-recognition literature (1991–2010) based on a new conceptual classification scheme. Computers & Industrial Engineering, 63(1), 204-222.
10.Kao, L. J., Lee, T. S., & Lu, C. J. (2016). A multi-stage control chart pattern recognition scheme based on independent component analysis and support vector machine. Journal of Intelligent Manufacturing, 27(3), 653-664.
11.Khormali, A., & Addeh, J. (2016). A novel approach for recognition of control chart patterns: Type-2 fuzzy clustering optimized support vector machine. ISA transactions, 63, 256-264.
12.Kiang, M. Y. (2001). Extending the Kohonen self-organizing map networks for clustering analysis. Computational Statistics & Data Analysis, 38(2), 161-180.
13.Kim, J. S., Park, C. S., Baek, J. G., & Kim, S. S. (2012, December). Control chart pattern recognition using wavelet based neural networks. In Proceedings of World Academy of Science, Engineering and Technology (No. 72, p. 1121). World Academy of Science, Engineering and Technology (WASET).
14.Lin, S. Y., Guh, R. S., & Shiue, Y. R. (2011). Effective recognition of control chart patterns in autocorrelated data using a support vector machine based approach. Computers & Industrial Engineering, 61(4), 1123-1134.
15.Liu, Y. M., & Zhou, H. F. (2013). Control chart pattern recognition based on wavelet analysis. In Applied Mechanics and Materials(Vol. 291, pp. 2479-2485). Trans Tech Publications.
16.Lu, C. J., Shao, Y. E., & Li, C. C. (2014). Recognition of concurrent control chart patterns by integrating ICA and SVM. Applied Mathematics & Information Sciences, 8(2), 681.
17.Miljković, D. (2017, January). Brief review of self-organizing maps. In MIPRO 2017.
18.Namdari, M., Jazayeri-Rad, H., & Hashemi, S. J. (2014). Process fault diagnosis using support vector machines with a genetic algorithm based parameter tuning. Journal of Automation and Control, 2(1), 1-7.
19.Peng, Z. K., & Chu, F. L. (2004). Application of the wavelet transform in machine condition monitoring and fault diagnostics: a review with bibliography. Mechanical systems and signal processing, 18(2), 199-221.
20.Pham, D. T., & Chan, A. B. (1998). Control chart pattern recognition using a new type of self-organizing neural network. Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering, 212(2), 115-127.
21.Pham, D. T., Packianather, M. S., & Charles, E. Y. A. (2008). Control chart pattern clustering using a new self-organizing spiking neural network. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 222(10), 1201-1211.
22.Purintrapiban, U., & Corley, H. W. (2012). Neural networks for detecting cyclic behavior in autocorrelated process. Computers & Industrial Engineering, 62(4), 1093-1108.
23.Taskovski, D., Milchevski, A., & Kostadinov, D. (2012). Classification of Power Quality Disturbances Using Wavelets and Support Vector Machine. Elektronika ir Elektrotechnika, 19(2), 25-30.
24.Valkonen, V. P., Kolehmainen, M., Lakka, H. M., & Salonen, J. T. (2002). Insulin resistance syndrome revisited: application of self-organizing maps. International journal of epidemiology, 31(4), 864-871.
25.Vapnik, V. (2013). The nature of statistical learning theory. Springer science & business media.
26.Wang, D. S., Yu, Y. W., Wang, S. H., & Cheng, B. W. (2013, July). Statistical process control on autocorrelated process. In Service Systems and Service Management (ICSSSM), 2013 10th International Conference on (pp. 81-84). IEEE.
27.Western Electric Company. (1956). Statistical quality control handbook. Western Electric Company.
28.Wu, S., & Chow, T. W. (2004). Clustering of the self-organizing map using a clustering validity index based on inter-cluster and intra-cluster density. Pattern Recognition, 37(2), 175-188.