Applying Decision Tree-Based Ensemble Classifiers for Diagnosing Mean Shift Signals in Multivariate Control Chart_

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題名：	Applying Decision Tree-Based Ensemble Classifiers for Diagnosing Mean Shift Signals in Multivariate Control Chart
書刊名：	品質學報
作者：	鄭春生／李虹葶
作者(外文)：	Cheng, Chuen-sheng／Lee, Hung-ting
出版日期：	2014
卷期：	21:2
頁次：	頁91-103
主題關鍵詞：	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
原始連結：	連回原系統網址
相關次數：	被引用次數:期刊(2) 博士論文(0) 專書(0) 專書論文(0) 排除自我引用:2 共同引用:0 點閱:67

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。

以文找文

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.

以文找文

期刊論文
1.	Wang, T. Y.、Chen, L. H.(2002)。Mean shifts detection and classification in multivariate process: a neural-fuzzy approach。Journal of Intelligent Manufacturing，13(3)，211-221。
2.	Yu, J. B.、Xi, L. F.、Zhou, X. J.(2009)。Identifying source(s) of out-of-control signals in multivariate manufacturing processes using selective neural network ensemble。Engineering Applications of Artificial Intelligence，22(1)，141-152。
3.	Niaki, S.T.A.、B. Abbasi(2005)。Fault diagnosis in multivariate control charts using artificial neural networks。International Quality and Reliability Engineering，21(8)，825-840。
4.	Shao, Y.E.、C.H. Wu、B.Y. Ho、B.S. Hsu(2008)。A neural network-based approach to identifying outof- control variables for multivariate control charts。Lecture Notes in Computer Science，5236，644-652。
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12.	Aparisi, F.、Avendaño, G.、Sanz, J.(2006)。Techniques to interpret T2 control chart signals。IIE Transactions，38(8)，647-657。
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17.	He, S. G.、He, Z.、Wang, G. A.(2011)。Online monitoring and fault identification of mean shifts in bivariate processes using decision tree learning techniques。Journal of Intelligent Manufacturing，24(1)，1-10。
18.	Ma, Y. Z.、Zhao, F. Y.(2003)。A new approach to diagnosing signals from multivariate EWMA control chart。International Journal of Plant Engineering and Management，8(4)，427-431。
19.	Mason, R. L.、Tracy, N. D.、Young, J. C.(1997)。A practical approach for interpreting multivariate T2 control chart signals。Journal of Quality Technology，29(4)，396-406。
20.	Niaki, S. T. A.、Fallahnezhad, M. S.(2009)。Decision-making in detecting and diagnosing faults of multivariate statistical quality control systems。International Journal of Advanced Manufacturing Technology，42(7-8)，713-724。
21.	Psarakis, S.(2011)。The use of neural networks in statistical process control charts。Quality and Reliability Engineering International，27(5)，641-650。
22.	Yu, J. B.、Xi, L. F.(2009)。A neural network ensemble-based model for on-line monitoring and diagnosis of out-of-control signals in multivariate manufacturing processes。Expert Systems with Applications，36(1)，909-921。
23.	Yu, J. B.、Xi, L. F.(2009)。A hybrid learning-based model for on-line monitoring and diagnosis of out-of-control signals in multivariate manufacturing processes。International Journal of Production Research，47(15)，4077-4108。
24.	Chen, L. H.、Wang, T. Y.(2004)。Artificial neural networks to classify mean shifts from multivariate X2 chart signals。Computers and Industrial Engineering，47(2/3)，195-205。
25.	Cheng, C. S.、Cheng, H. P.(2008)。Identifying the Source of Variance Shifts in the Multivariate Process Using Neural Network and Support Vector Machines。Expert Systems with Applications，35(1/2)，198-206。
26.	Freund, Y.(1995)。Boosting a weak learning algorithm by majority。Information and Computation，121(2)，256-285。
27.	Guh, R. S.(2007)。On-line identification and quantification of mean shifts in bivariate processes using a neural network-based approach。Quality and Reliability Engineering International，23(3)，367-385。
28.	Jackson, J. E.(1985)。Multivariate quality control。Communications in Statistics-Theory and Methods，14(11)，2657-2688。
29.	Sun, R.、Tsung, F.(2003)。A kernel-distance-based multivariate control chart using support vector Methods。International Journal of Production Research，41(13)，2975-2989。
30.	Yu, L.、Yue, W.、Wang, S.、Lai, K. K.(2010)。Support vector machine based multiagent ensemble learning for credit risk evaluation。Expert Systems with Applications，37(2)，1351-1360。
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會議論文
1.	Aparisi, F.、Avendaño, G.、Sanz, J.(2007)。Neural networks to identify the out-of-control process variables when a MEWMA chart is employed。the The 16th IASTED International Conference on Applied Simulation and Modelling，230-235。
2.	Aparisi, F.、Carrión, A.(2010)。Artificial neural networks for identifying the signals of multivariate EWMA control chart。2010 10th International Conference on Intelligent Systems Design and Applications，427-431。
3.	Cheng, C. S.、Cheng, H. P.、Huang, K. K.(2009)。Interpreting the mean shift signals in multivariate control charts using support vector machine-based classifier。IEEE International Conference on Industrial Engineering and Engineering Management，1-4。

圖書
1.	Breiman, L.、Friedman, J. H.、Olshen, R. A.、Stone, C. J.(1984)。Classification and Regressing Tree。Pacific Grove, CA：Wadsworth。
2.	Montgomery, D. C.(2009)。Statistical Quality Control。New York：John Wiley & Sons。

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