查詢結果分析
相關文獻
- Applying Decision Tree-Based Ensemble Classifiers for Diagnosing Mean Shift Signals in Multivariate Control Chart
- 半導體產業多變量製程監控概論
- 現代製程監控平臺技術發展與應用
- 製程間具相關數據之趨勢模型偵測探討
- Distributional Property of the Incapability Index C抅抅 Under Edgeworth Series Distribution for Processes with Symmetric Tolerances
- An Integrated Approach to Semiconductor Equipment Monitoring
- 軟體品質保證系統:架構與個案之探討
- 資料挖掘的運用--以決策樹方法分析保送甄試學生入學四技二專報到情形
- 類神經網路於製程管制之應用:具相關性數據特性模型之偵測與分析
- 提昇製程品質控制績效之研究--結合管制圖與移動平均控制法
頁籤選單縮合
題 名 | Applying Decision Tree-Based Ensemble Classifiers for Diagnosing Mean Shift Signals in Multivariate Control Chart=應用整體式決策樹分類模型於多變量管制圖平均數偏移之診斷 |
---|---|
作 者 | 鄭春生; 李虹葶; | 書刊名 | 品質學報 |
卷 期 | 21:2 2014.04[民103.04] |
頁 次 | 頁91-103 |
分類號 | 494.56 |
關鍵詞 | 整體式學習; 決策樹; 平均數偏移; 多變量統計製程管制; 統計製程管制; Ensemble learning; Decision tree; Mean shift; Multivariate statistical process control; MSPC; Statistical process control; SPC; |
語 文 | 英文(English) |
中文摘要 | 多變量管制圖之主要目的是用來偵測製程中是否發生異常訊號。若偵測出製程發生變異,則應立即處理異常之訊號並診斷其發生異常原因為何,使製程回復至穩定之管制狀態內。Hotelling T^2管制圖可以監控多個品質特性之異常發生,且擁有良好之績效,然而,Hotelling T^2管制圖卻無法判斷是由製程中哪一個品質特性發生變異。為了有效確認發生異常之品質特性為何,且提高其辨識績效,本研究以決策樹為基礎建構診斷系統。此系統是以Hotelling T^2管制圖進行監控並應用決策樹整體式分類模型進行辨識。本研究提出以樣本多樣性之方法建構多個分類模型並以統計特徵值(平均數與馬氏距離)作為診斷系統之輸入向量。由研究結果顯示,以整體式決策樹整合之辨識系統,其辨識績效為最佳。 |
英文摘要 | The Hotelling T^2 control chart is an important tool for monitoring process shift in multivariate statistical process control (MSPC). Detecting and diagnosing out-of-control variables are required tasks when a multivariate control chart signals. This paper presents a decision tree-based ensemble model to address diagnosing issue in multivariate process control. The commonly used ensemble methods, including bagging and AdaBoost are considered in this paper. To improve the classification performance, we propose using a set of features extracting from process data. Results from comparative studies indicate that these features with certain ensemble classifiers can significantly improve classification performance. The proposed approach contributes to process monitoring and identifyingmean shift sources in MSPC, which can assist engineers to effectively identifyresponsible variables and accelerate improvement action generation. |
本系統中英文摘要資訊取自各篇刊載內容。