多變量批次製程目前廣泛地應用於化工、醫藥、半導體等高科技領域，在這一類的製程型態之下，如何有效利用製程的變數資料診斷出製程的異常現象以及執行錯誤偵測與分類是目前製程管制的重要課題。隨者製造自動化以及資料蒐集檢驗技術愈趨成熟進步，有越來越多的人工智慧資料分析技術受到重視，人工智慧藉由電腦的演算能力可實踐資料的學習與辨識。本研究應用類神經網路模式提供一套製程監控與錯誤分類的分析架構，首先蒐集代表正常狀態下的製程資料做為訓練自主特徵映射網路的樣本，透過自主特徵映射網路可將高維度的資料做分群並透過拓樸圖的結構展現出資料的分布特性，訓練完成的SOM可記憶製程於正常狀態下的資料型態，之後當一個新的觀測值輸入時再藉由MQE chart量化訊息的呈現，可看出製程狀態隨時間的變化趨勢同時了解製程當下狀態與正常狀態的差異程度。在前述製程監控模組的階段，一旦發現製程出現異常信號，下一步可利用訓練完成的倒傳遞類神經網路辨識出發生偏移的變數和其偏移的度量大小，幫助工程師快速了解錯誤發生的可歸屬原因，在實際的案例分析中也可看出應用類神經網路模式確實能看出資料分佈的型態並推斷出可能的異常資料點，此外也提供了對製程資料不同角度的詮釋。

Nowadays, multivariate batch process has been widely adopted in chemical, pharmaceutical and semiconductor high-tech industries. Under such type of process, how to utilize the process data effectively to diagnose abnormal phenomenon and execute fault classification becomes a critical issue. With the progress of advanced automatic data collection and inspection techniques, more and more artificial intelligent data analysis methods are now being emphasized and implemented in the real manufacturing environment. Through the quick computation capability of computer, learning and pattern recognition of data can be realized. In this research, some neural network architectures are provided as the process monitoring and fault classification tools. First, collect the data sets under normal process condition. These data sets are used to train Self-Organizing Map (SOM). By the algorithm of SOM, high dimensional data are automatically clustered into the predefined number of groups and also the natural distribution of the data can be observed via topology map. The trained SOM can remember the structure of data under normal process condition. After that when a new observation comes, through the quantitative information shown by MQE chart, we could know how far it differs from the normal process state. While the process signals something wrong in the previous describe monitoring stage, next step is using the trained back propagation network (BPN) to find out which process variables shift and its shift magnitude. In the real case study, we see neural networks do help the engineer point out the possible abnormal observations and provide the different point of view of the process data analysis.

[1] 林東慶 (2006), 「以灰色理論和類神經網路預測航空客貨運量之變化」, 國立成功大學民航研究所碩士論文。

[2] 莊達人 (2008), VLSI製造技術, 第六版, 高立圖書有限公司。

[3] 曾介亭 (2006), 「多代理人系統在建構 APC 作業平台之研究」, 國立成功大學工程科學系專班碩士論文。

[4] 張斐章,張麗秋 (2009), 類神經網路導論：原理與應用, 滄海圖書有限公司。

[5] 張佳蓉 (2007), 「半導體製程與設備之健康指標分析」, 國立清華大學工業工程與工程管理學系碩士論文。

[6] 張慶宏 (2009), 「利用Hotelling’s T2 分解方法處理機台錯誤偵測與分類之研究」, 國立清華大學統計學研究所碩士論文。

[7] 葉怡成 (2009), 類神經網路模式應用與實作, 儒林圖書有限公司。

[8] 趙安國 (2008), 「批次剖面資料之錯誤偵測分析」, 國立清華大學統計學研究所碩士論文。

[9] 羅華強 (2008), 類神經網路─Matlab的應用, 高立圖書有限公司。

[10] Akima, H., (1970). A New Method of Interpolation and Smooth Curve Fitting Based on Local Procedures, Journal of Association for Computing Machinery, 17, 589-602.

[11] Bose, B. K., (2007). Neural Network Applications in Power Electronics and Motor Drives: An Introduction and Perspective, IEEE Transactions on Industrial Electronics, 54, 14-33.

[12] Crosier, R. B., (1988). Multivariate generalizations of cumulative sum quality control schemes. Technometrics, 30 (3), 291–303.

[13] Chen L. H., Wang T. Y., (2004). Artificial Neural Network to Classify Mean Shifts from Multivariate χ2 Chart Signals, Computer & Industrial Engineering, 47, 195-205.

[14] Guh, R. S. (2005). A hybrid learning-based model for on-line detection and analysis of control chart patterns. Computers and Industrial Engineering, 49, 35–62.

[15] Hotelling, H., (1947). Multivariate quality control—illustrated by the air testing of sample bombsights, In: C. Eisenhart, M.W. Hastay and W.A. Wallis, eds. Techniques of statistical analysis. New York: McGraw-Hill, 11–184.

[16] Kohonen, T., (1988). An introduction to neural computing, Neural Networks, 1(1), 3-16.

[17] Lowry, C. A., Woodall, W. H., Champ, C. W. and Rigdon, S. E., 1992. A multivariate exponentially weighted moving average control chart. Technometrics, 34 (1), 46–53.

[18] Lowry, C. A., Montgomery, D. C., (1995). A review of multivariate control charts. IIE Transactions, 27 (6), 800–810.

[19] Mason, R. L., Tracy, N.D., Young, J.C. (1995). Decomposition of T2 for Multivariate Control Chart Interpretation, Journal of Quality Technology, Vol.27, No.2, 99-108.

[20] Mason, R. L., Tracy, N.D., Young, J.C. (1997). A Practical Approach for Interpreting Multivariate T2 Control Chart Signals, Journal of Quality Technology, Vol.29, No.4, 396-406.

[21] Mason, R. L., Young, J.C. (1999). Improving the Sensitivity of the T2 Statistics in Multivariate Process Control, Journal of Quality Technology, Vol.31, No.2, 155-165.

[22] Mason, R. L., Chou, Y. M., Sullivan, J. H., Stoumbos, Z. G., Young, J.C. (2003). Systematic Patterns in T2 Charts, Journal of Quality Technology, Vol.35, No.1, 47-58.

[23] Nguyen, D., and Widrow, B. (1990). Improving the learning speed of 2-layer neural networks by choosing initial values of the adaptive weights. Proceedings of IEEE International Joint Conference on Neural Networks III pp. 21–26.

[24] Nomikos, P. and MacGregor, J. F. (1995). Multivariate SPC Charts for Monitoring Batch Processes, Technometrics, 37, 41-59.
[25] Niaki, S. T. A., & Abbasi, B. (2005). Fault diagnosis in multivariate control charts using artificial neural networks. Quality and Reliability Engineering International, 21, 825–840.

[26] Tracy, N. D., Young, J. C., Mason, R. L. (1992). Multivariate Control Charts for Individual Observations, Journal of Quality Technology, Vol.24, No.2, 88-95.

[27] Wang, C. H., Kuo, W., and Qi, H. R., (2007). An integrated approach for process monitoring using wavelet analysis and competitive neural network. International Journal of Production Research, 45 (1), 227–244.

[28] Yu, J. B., Wang, S. (2009). Using Minimum Quantization Error chart for the monitoring of process states in multivariate manufacturing processes, Computers & Industrial Engineering, 57, 1300–1312.

[29] Yu, Jianbo and Xi, Lifeng (2009). A hybrid learning-based model for on-line monitoring and diagnosis of out-of-control signals in multivariate manufacturing processes, International Journal of Production Research, 47: 15, 4077—4108.

[30] Zorriassatine, F. and Tannock, J. D. T., (1998). A review of neural networks for statistical process control. Journal of Intelligent Manufacturing, 9 (3), 209–224.