Integrating Independent Component Analysis and Support Vector Machine for Identifying Process Status Changes

應用獨立成分分析與支援向量機辨識製程狀態之改變

10.6220/joq.2014.21(6).01

黃國格(Kuo-Ko Huang)；鄭春生(Chuen-Sheng Cheng)

非隨機樣式 ； 獨立成分分析 ； 支援向量機 ； time series data pattern ； independent component analysis ； support vector machine

品質學報

21卷6期（2014 / 12 / 31）

413 - 426

英文

管制圖非隨機樣式的辨識在現今的工業製程中已成為不可或缺的監控技術之一，其主要功能為補強監控管制圖所不易偵測之非隨機樣式，透過辨識非隨機樣式之種類，可探討出其製程失控之可歸屬原因，藉以改善製程減少不良品的產生。近年來支援向量機（support vector machine, SVM）已經成功地用在管制圖形狀辨識上，但大多數的研究均以原始資料作為SVM的輸入向量。為了有效地辨識管制圖的非隨機樣式，本研究整合獨立成分分析（independent component analysis, ICA）與SVM，提出一個有效的管制圖非隨機樣式辨識架構。本研究首先利用ICA將管制圖非隨機樣式分解成獨立成分（independent components, ICs），接著找出代表基本非隨機樣式的ICs，最後將ICs作為SVM之輸入向量，以建構非隨機樣式辨識模型。實驗結果顯示，本研究所提出的整合ICA與SVM模式，可以有效地辨識管制圖之非隨機樣式，偵測製程狀態是否改變。

Time series data pattern recognition is critical for statistical process control. This paper assumes that observations from the in-control process consist of in-control signals and random noise. The in-control signals switch to different signal types when the process status changes. In these cases, process data monitoring can be formulated as a pattern recognition task. This paper proposes a novel approach using independent component analysis (ICA) and support vector machine (SVM) for time series data pattern recognition. The proposed method applies ICA to the measurement data to generate independent components (ICs). The ICs include important information contained in the original observations. The ICs then serve as the input vectors for the SVM model to identify the time-series data pattern. Extensive simulation studies indicate that the proposed identifiers perform better than using raw data as inputs.

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