查詢結果分析
相關文獻
- Identifying the Out-of-Control Variables of Multivariate Control Chart Using Ensemble SVM Classifiers
- Identifying the Source of Variance Shifts in Multivariate Statistical Process Control Using Ensemble Classifiers
- 應用類神經網路與支援向量機以辨認製程多重品質特性出錯之研究
- IGCC發電方式簡介
- 中興製GCS操作氣缸銹水異常原因驗證及洩氣裝置簡易修復方法
- Integrating Independent Component Analysis and Support Vector Machine for Identifying Process Status Changes
- Real-Time Monitoring of the Quality of Multivariate Processes with a SVM Based Classifier Ensemble Approach
- Determination of Order Quantity for Perishable Products by Using the Support Vector Machine
- 結合多孔性整體式聚合物與金奈米粒子之汞離子捕捉器
- Applying Decision Tree-Based Ensemble Classifiers for Diagnosing Mean Shift Signals in Multivariate Control Chart
頁籤選單縮合
題名 | Identifying the Out-of-Control Variables of Multivariate Control Chart Using Ensemble SVM Classifiers=建構辨識多變量管制圖異常來源之整體式分類系統 |
---|---|
作 者 | 鄭春生; 李虹葶; | 書刊名 | 工業工程學刊 |
卷期 | 29:5 2012.07[民101.07] |
頁次 | 頁314-323 |
分類號 | 440.8 |
關鍵詞 | 多變量管制圖; 異常來源辨識; 整體式; 支援向量機; multivariate control chart; interpretation of signal; ensemble classifiers; support vector machine; |
語文 | 英文(English) |
英文摘要 | Out-of-control signals in multivariate charts may be caused by one or few variables or a set of variables. Multivariate process control often encounters with the diagnosis or interpretation difficulty of an out-of-control signal to determine which variable is responsible for the signal. In this article, we formulate the diagnosis of out-of-control signal as a classification problem. The proposed system includes a shift detector and a classifier. The traditional multivariate chart works as a mean shift detector. Once an out-of-control signal is generated, an SVM-based ensemble classifier is used to recognize the variables that have shifted. We propose using subgroup data and extracted features (sample mean and Mahalanobis distance) as the input vectors of classifier. The performance of the proposed system was evaluated by computing its classification accuracy. We use the traditional decomposition method as a benchmark for comparison. The simulation studies indicate that the proposed ensemble classification model is a successful method in identifying the source of the mean change. The results also reveal that SVM using extracted features as input vector has slightly better classification performance than using raw data as input. The proposed method may facilitate the diagnosis of the out-of-control signal. |
本系統之摘要資訊系依該期刊論文摘要之資訊為主。