應用類神經網路與支援向量機建構多變量管制圖非隨機樣式之辨識系統

Multivariate Control Chart Pattern Recognition using Artificial Neural Network and Support Vector Machine

張秉宸(Ping-Chen Chang)

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(2007年)

https://doi.org/10.6838/YZU.2007.00215

摘要

單變量蕭華特管制法 (univariate Shewhart control chart) 常被用來診斷製程中是否存在可歸屬原因所造成之變異。若製程存在可歸屬原因，則管制圖上之管制統計量會超出管制界限或呈現特定之非隨機樣式。管制圖中常見之非隨機樣式包括：趨勢樣式、偏移樣式、混合樣式與週期性樣式等。但在一些製程管制中，我們必須使用多變量管制圖 (multivariate control chart) 來同時監控數個彼此間具有相關性之品質特性。因此，辨認多變量管制圖中之非隨機樣式也是多變量製程管制中的一個重要研究議題。如果能夠正確辨識出非隨機樣式，將可縮小判斷製程可歸屬原因之範圍，對於規劃改善對策有所助益。

本研究之主要目的是使用類神經網路與支援向量機，建構一個能偵測與辨識多變量管制圖中之非隨機樣式的辨識系統，作為實施矯正措施及改善產品品質的重要依據。本研究首先探討多變量管制圖之特性，藉以了解多變量管制圖之統計量與不同相關性係數之對應關係。其次，研究中以類神經網路與支援向量機為基礎，建構出之多變量非隨機樣式辨識系統。研究結果顯示類神經網路與支援向量機之辨識績效並無明顯差異，但皆較傳統多變量區別分析為佳。

本研究亦提出兩種不同方式之辨識程序，用以改善非隨機樣式分類之效益。程序一是以一個階段之系統架構，同時將正常數據與非隨機樣式進行分類。程序二則為一個兩階段之系統架構。第一階段用以判斷製程中是否出現非隨機樣式，第二階段是將非隨機樣式進行分類。實驗結果顯示，本研究所提出之程序二可以對非隨機樣式有更佳之辨識能力。

最後，在敏感度分析中，針對類神經網路與支援向量機之系統參數與訓練樣本進行測試。由實驗結果發現，分類器對於參數的調整或訓練樣本結構之改變，都能夠呈現穩健的辨識績效。

關鍵字

非隨機樣式 ； 多變量製程管制 ； 類神經網路 ； 支援向量機 ； 區別分析

並列摘要

In the past, univariate Shewhart control charts have been widely used to determine whether assignable causes of process variation are presented. Control chart pattern recognition is an important aspect in the application of control charts. The presence of non-random patterns indicates that a process is affected by assignable causes, and corrective actions should be taken. A particular non-random pattern is often associated with a set of assignable causes. Identification of non-random patterns can greatly narrow the set of possible causes that must be investigated, and thus the diagnostic search could be reduced in length.

A recent research indicated that non-random patterns may also occur in multivariate control charts. Therefore, the pattern recognition of multivariate control chart is also an important research issue in multivariate process control. The purpose of this research was to investigate the feasibility of applying statistical learning algorithms for multivariate control chart pattern recognition. In this research, we considered two recognizers based on artificial neural network (ANN) and support vector machine (SVM). Furthermore, we used discriminant analysis as a baseline for comparison. The results showed that the performances of ANN and SVM were similar in classifying patterns of multivariate control charts. Both ANN and SVM can perform significantly better than discriminant analysis.

In addition, two procedures were developed and compared in this research. The first procedure was used to recognize and classify both random data and non-random patterns. The second procedure was a two-stage approach. At the first stage, the ANN-based and SVM-based classifiers can be used to detect whether non-random patterns of process are presented. The second stage was to classify the types of non-random patterns. Results from our experiment showed that the second procedure performed better than the first procedure.

Finally, this research investigated the effects of changing the parameters of ANN and SVM. The results exhibited that ANN-based and SVM-based classifiers are quite robust against changing of parameters.

並列關鍵字

non-random patterns ； multivariate process control ； artificial neural network ； support vector machine ； discriminant analysis

參考文獻

1.Cheng, C. S., “A neural network approach for the analysis of control chart patterns,” International Journal of Production Research, 35, 3, 667-697 (1997).

2.Chih, W. H., and Rollier D. A., “Diagnostic characteristics for bivariate pattern recognition scheme in SPC,” International Journal of Quality & Reliability Management, 11, 53-66 (1992).

3.Chih, W. H., and Rollier D. A., “A methodology of pattern recognition schemes for two variables in SPC,” International Journal of Quality & Reliability Management, 12, 86-107 (1995).

4.Chinnam, R. B., “Support vector machines for recognizing shifts in correlated and other manufacturing processes,” International Journal of Production Research, 40, 4449-4466 (2002).

5.Fausett, L., Fundamentals of Neural Networks, Prentice-Hill, Inc., New Jersey (1994).