應用統計特徵值以支援向量機與類神經網路建構紗線張力 非隨機樣式之辨識系統

Application of Statistical Features in Yarn Tension Pattern Recognition using Support Vector Machine and Artificial Neural Network

吳思瑤(Szu-Yao Wu)

指導教授 ： 鄭春生

元智大學/工程學院/工業工程與管理學系/碩士(2008年)

摘要

在統計製程管制 (statistical process control, SPC) 中，管制圖是最常被應用的工具，可用來判斷製程中是否因存在可歸屬原因而造成製程的變異。製程中若存在可歸屬原因，管制圖上之統計量會超出管制上下界或呈現特定之非隨機性樣式，包括趨勢樣式、偏移樣式、週期樣式及混合樣式。然而在實際製程中，常出現各式各樣之非隨機樣式，無法以上述非隨機性樣式來歸類；因此，如何在眾多複雜的非隨機樣式中，正確對每種非隨機樣式作辨識，縮小診斷製程中可歸屬原因之範圍，將有助於規劃改善對策的施行與效益。

本研究之目的為以相關係數為基礎，發展出一系列自我比對的相關係數及使用其它有用之特徵值，對紗線之製程數據作轉換；再使用支援向量機與類神經網路，建構一個能對紗線張力非隨機性樣式作有效辨識與偵測之辨識系統，以作為實施製程規劃矯正措施及改善產品品質之重要依據。

研究顯示，原始數據經特徵值轉換後，在支援向量機與類神經網路對於非隨機樣式的辨識績效上，呈現較佳的辨識績效；兩種分類器之辨識能力亦相同。

關鍵字

紗線張力 ； 非隨機樣式 ； 單變量管制圖 ； 相關係數 ； 統計特徵值 ； 支援向量機 ； 類神經網路

並列摘要

Control chart pattern recognition is an important work in statistical process control. A control chart may present several unnatural patterns which including trends, sudden shifts, mixtures, and cyclic patterns. The occurrence of unnatural patterns implies that the process is affected by assignable causes, and corrective actions should be taken. Actually, the types of unnatural patterns which exist in real process are comprehensive. Identification correctly of unnatural patterns can greatly narrow the set of possible causes that must be investigated, and thus the diagnostic search time could be reduced in length.

The purpose of this research was to develop two classifiers based on support vector machine (SVM) and artificial neural network (ANN) to classify unnatural patterns in yarn tension data. First, we apply some statistical features to extract distinguished features from raw data. The extracted features are used as the components of the input vectors. Secondly, we develop SVM-based and ANN-based classifiers for control chart pattern recognition. The performances of two recognizers using statistical features extracted from correlation coefficient as the components of the input vectors was investigated and compared.

The results show that the SVM and ANN have similar recognition performances. Extensive comparisons indicate that the proposed recognizers perform better than that using raw data as inputs. Our research concluded that the extracted statistical features can reduce the input vectors while maintaining good levels of accuracy.

並列關鍵字

yarn tension ； unnatural patterns ； control chart ； correlation coefficient ； statistical feature ； support vector machine ； artificial neural network

參考文獻

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