就現代產業而言，管制圖(Control Charts)為品質管理最常使用的手法之一。由於產品日漸複雜且各品質特性間通常具有一定程度的相關性，因此會採用多變量管制圖(Multivariate Control Charts)來避免誤判的情形發生。Hotelling's T^2為目前最普遍使用的多變量管制圖，但其在製程平均值為中小幅度偏移的情形下，偵測效果不佳，因此可加入連串規則(Sensitizing Rules)來提升偵測的能力。另外，當Hotelling's T^2管制圖偵測出製程發生異常時，無法直接獲得相關的資訊，通常需透過計算繁複的分解法或主成分分析來解決，且這些方法只能提供造成製程異常的品質特性，不能獲得更多的異常資訊。所以有學者結合類神經網路的學習能力，建構出多變量管制圖來監控即時線上的作業系統。在類神經網路中，因支援向量機為基於邊際最大化(Margin Maximum)的理論找出最佳的超平面來進行分類，故在分類上有相對較優的效果。本研究加入連串規則提高Hotelling's T^2管制圖的偵測效能，並結合支援向量機來辨識出造成製程異常的品質特性以及其平均值偏移量的大小。實驗結果指出，支援向量機可以有效地判斷造成製程異常來源以及平均值偏移的幅度。

In many industries, control charts is one of the most frequently used tools for quality management. Because of the complexity of product and the correlation of the characteristics, using multivariate control charts can avoid misleading results. The most widely used multivariate control chart is Hotelling's T^2. However, it has little power when detecting small or moderate process shifts. The use of supplementary sensitizing rules can improve the performance of detection. When Hotelling's T^2 control chart detects the signal of process, it usually accesses the relevant information through the decomposition technique or principal analysis. Unfortunately, these techniques only provide information that which characteristic(s) contributed to the signal, and can't offer advanced information. Therefore, application of neural networks to multivariate control chart has been investigated by several researchers. Support vector machines bases on the theory of margin maximum to find the best hyperplane, so it has better rate of accuracy classification than other neural networks methods. This study supplements sensitizing rules to Hotelling's T^2 control chart to improve the performance of detection, and combines with support vector machines to identify the characteristic or group of characteristics that are responsible for the signal and to classify the magnitude of the mean shifts. The experimental results demonstrate that the support vector machines can effectively identify the characteristic or group of characteristics that caused the process mean shifts and the magnitude of the shifts.

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邱靜娥、陳世輝和江桂霖 (2006)，應用類神經網路與連串規則於Hotelling’s T2管制圖，中華民國品質學會第42屆年會暨第12屆全國品質管理研討會，國立雲林科技大學工業工程與管理研究所
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