Control Chart Pattern Recognition Using Wavelet Analysis and Neural Networks

利用小波分析和類神經網路辨識管制圖之非隨機樣式

鄭慧萍(Hui-Ping Cheng)；鄭春生(Chuen-Sheng Cheng)

管制圖 ； 樣式辨認 ； 小波分析 ； 特徵萃取 ； control chart ； pattern recognition ； wavelet analysis ； feature extraction

品質學報

16卷5期（2009 / 10 / 31）

311 - 321

英文

本文提出一個修正之自組織映射類神經網路，應用於管制圖非隨機樣式之分析。研究目的是在沒有任何有關非隨機性樣式之事前資訊下，發展一個樣式之分群方法。此分群方法爲兩階段之程序，包含樣式之形成和合併。本研究利用小波分析來萃取非隨機性樣式之重要特徵，作爲分群方法之輸入向量。利用模擬和實際數據所獲得之實驗結果顯示，本文所提出之分群程序優於傳統之方法。由原始數據所萃取之特徵可以改善類神經網路之辨識績效。

Control charts are useful tool in detecting out-of-control situations in process data. There are many unnatural patterns that may exist in process data indicating the process is out of control. The presence of unnatural patterns implies that a process is affected by assignable causes, and corrective actions should be taken. Identification of unnatural patterns can greatly narrow the set of possible causes that must be investigated, and thus the diagnostic work could be reduced in length. This paper presents a modified self-organizing neural network developed for control chart pattern analysis. The aim is to develop a pattern clustering approach when no prior knowledge of the unnatural patterns is available. This paper also investigates the use of features extracted from wavelet analysis as the components of the input vectors. Experimental results and comparisons based on simulated and real data show that the proposed approach performs better than traditional approach. Our research concluded that the extracted features can improve the performance of the proposed neural network.

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