整合獨立成份分析與類神經網路於相關性製程上之監控

Process Monitor for Autocorrelated Data by Integrating ICA and Neural Network

楊正弘(Chen-Hong Yang)

指導教授 ： 邱志洲

國立臺北科技大學/管理學院/商業自動化與管理研究所/碩士(2007年)

摘要

為了有效地提高產品的生產品質，企業必須不斷的監控與調整整個生產線，但製程資料若呈現自我相關性時，整合SPC與EPC的方法卻往往引發假警訊的發生。在本篇中，一個結合獨立成份分析(ICA)影像重建機制與類神經網路的方法被提出來進行製程干擾項的辨識工作。經由影像重建後可以發現，我們能夠將原始無干擾製程中所代表的圖樣資訊移除，並將干擾處明顯的呈現出來。其中干擾模式是使用較常見的階梯式干擾與線性式干擾，最後使用類神經網路來進行辨識工作來計算成功辨識率。我們將此結果與傳統的Shewhart管制圖和CUSUM管制圖來做比較，從實驗結果發現，當有明顯的干擾能夠有效的辨識出來，同時其辨識率幾乎不會受到資料相關性的影響。

關鍵字

SPC ； EPC ； 獨立成份分析 ； 類經神網路 ； 製程干擾

並列摘要

Process monitoring and control of a production line is often used in industry to maintain high-quality production and to facilitate high levels of efficiency in the process. However, current process control techniques, such as statistical process control (SPC) and engineering process control (EPC), may not effectively detect abnormalities, especially when autocorrelation is present in the process. This paper proposes an independent component analysis (ICA)-based image reconstruction scheme with a neural network approach to identify disturbances and recognize shifts in the correlated process parameters. The resulting image can effectively remove the textual pattern and preserve disturbances distinctly. We illustrate our approach using two most commonly encountered disturbances, the step-change disturbance and the linear disturbance, in a manufacturing process. For comparison, traditional Shewhart control charts and cumulative sum (CUSUM) charts were applied to evaluate the identification capability of the proposed approach. The experimental results reveal that the proposed method is effective and efficient for disturbance identification in correlated process parameters when disturbance is significant. Additionally, the identification rate made by proposed method is almost free from the influence of the data correlation.

並列關鍵字

statistical process control ； engineering process control ； independent component analysis ； neural networks ； process disturbances

參考文獻

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