IDENTIFYING THE SOURCE OF VARIANCE SHIFTS IN MULTIVARIATE STATISTICAL PROCESS CONTROL USING ENSEMBLE CLASSIFIERS

診斷多變量製程變異數異常來源之整體式分類模型

李虹葶(Hung-Ting Lee) ； 林志鴻(Chih-Hung Lin) ； 黃奕錞(Yi-Chun Huang) ； 陳忠祐(Jung-You Chen) ； 鄭春生(Chuen-Sheng Cheng)

《品質學報》 23卷3期 (2016/06) Pp. 159-170

https://doi.org/10.6220/joq.2016.23(3).01

摘要

在統計製程管制中，管制圖常被用來監控製程之穩定性。在許多應用中，一件產品之品質是由多個變數來定義。這些品質特性具有相關性，若利用個別之管制圖來監控每一個品質特性將無法有效的監控製程之變化。近年來，多變量管制圖被發展用來監控多個具有相關性之品質特性。偵測製程是否為管制外是應用管制圖之基本工作，除此之外，多變量製程管制之工作還包含在管制圖發出管制外訊號時，找出造成管制外訊號之異常來源，以便進行改善。本研究利用決策樹(decision tree, DT)建立一個整體式分類模型，用來指出多變量製程管制外訊號之異常來源。本研究之重點在於多變量製程之變異數偏移情形。研究結果顯示，本文所提出的整體式分類模型，是一個有效的方法，可以指出製程變異數改變時之異常來源。比較結果也顯示，使用一些重要的特徵，作為分類器之輸入向量元素，可以提升分類器之績效。本文所介紹之整體式分類模型，可以輔助傳統管制圖，鑑定製程變異數發生異常時之異常來源，以加快實施改善行動。

關鍵字

多變量管制圖 ； 變異數偏移 ； 整體式分類 ； 決策樹 ； 特徵

並列摘要

Statistical process control charts are important tools for monitoring process stability in manufacturing and service industries. Using multivariate control charts to monitor two or more related quality characteristic has become increasingly popular in today's environment. Out-of-control signals in multivariate control charts may be caused by one or more variables or a set of variables. In the practice of quality control, in addition to the quick detection of process change, it is also critical to diagnose the change and identify which variable(s) has changed after an out-of-control signal occurred. In this paper, we propose a decision tree (DT) based ensemble classifier to approach the problem of diagnosing out-of-control signal. The focus of this paper is on the variance shifts of a multivariate process. Simulation studies indicate that the proposed ensemble classification model is a successful method in identifying the source of variance change. A comparative study also reveals that DT using extracted features as input vector has slightly better classification performance than using raw data as input. The proposed method could be a useful diagnostic aid in monitoring shifts in process dispersion.

並列關鍵字

multivariate control chart ； variance shift ； ensemble classifications ； decision tree ； features

參考文獻

Alfaro, E.,Alfaro, J. L.,Gámez, M.,García, N.(2009).A boosting approach for understanding out-of-control signals in multivariate control charts.International Journal of Production Research.47(24),6821-6831.

Johnson, N. L.(ed.),Kotz, S.(ed.)(1985).Encyclopedia of Statistical Science.New York:Wiley.

Aparisi, F.,Avendaño, G.,Sanz, J.(2006).Techniques to interpret T2 control chart signals.IIE Transactions.38(8),647-657.

Breiman, L.(1996).Bagging predictors.Machine Learning.24(2),123-140.

Breiman, L.,Friedman, J. H.,Olshen, R. A.,Stone, C. J.(1984).Classification and Regressing Tree.Pacific Grove, CA.:Wadsworth.