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題名：整合獨立成分分析與統計製程管制圖於產品件內和件間變異監控之應用
書刊名：品質學報
作者：鄭春生／黃國格
作者(外文)：Cheng, Chuen-sheng／Huang, Kuo-ko
出版日期：2013
卷期：20:1
頁次：頁137-154
主題關鍵詞：件內和件間變異；統計製程管制；獨立成分分析；Within-part and between-part variations；Statistical process control；Independent component analysis
原始連結：連回原系統網址new window
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隨著產品特性之複雜化，我們需要在一件產品的不同位置量測品質特性。因此在監控產品品質之量測值時，由變異來源造成的時間性變異樣式與空間性變異樣式可能會成為觀測到的件內和件間變異。利用傳統管制圖來監控原始觀測值的件內和件間變異雖然可行，但並非是一種有效率或者有效益之方法。此乃因為觀測值是由不同變異來源交互影響之後的結果。如果可以直接監控變異來源之變化，將會是一個更具有效率及效益之製程管制方法。本研究之目的是應用獨立成分分析自原始觀測數據分離出獨立成分（變異來源）後，再使用I-MR管制圖與Hotelling T^2管制圖對獨立成分進行監控。本研究所提出之監控方式將以一個模擬範例及一個物理氣相沉積薄膜的實際製程資料加以驗證。本研究是以監控製程觀測值之I-MR-R/S管制圖做為比較基準，並以平均連串長度作為績效指標。實驗結果顯示，當製程出現平均數偏移以及平均數呈趨勢遞增之製程異常情形時，本研究所提之方法會比傳統管制圖更快偵測到製程平均數的變化狀況。
With the complexity of product characteristics, it is now necessary to measure quality characteristic at different locations across a part. In the monitoring of product quality measurements, the temporal pattern and spatial variation pattern caused by a variation source may turn out to be the observed within- and between-part variations. It is feasible to apply traditional control charts to monitoring the within- and between-part variations. However, it may be neither effective nor efficient due to the fact that observed measurements are mixture of several variation sources.The proposed scheme first applies ICA methodology to the process observations to generate the independent components that contain different characteristics of the process. The I-MR control chart and Hotelling T^2 control chart are then used to monitor the independent components for process control. The proposed procedures were implemented via a simulated processes and a case study of the physical vapor deposition process. The experimental results show that the proposed methods can detect faults faster than I-MR-R/S control chart.
期刊論文
1.莊寶鵰(1996)。以模糊推論確認多變量製程管制的特殊變因。品質學報，2(2)，49-65。  延伸查詢new window
2.Apley, D. W.、Lee, H. Y.(2003)。Identifying spatial variation patterns in multivariate manufacturing processes: a blind separation approach。Technometrics，45(3)，220-234。  new window
3.陳偉星(20120200)。Variation Pattern Identification and Fault Diagnosis of Solder Paste Deposit by Using Independent Component Analysis。Journal of Quality，19(1)，21-39。new window  new window
4.鄭慧萍、鄭春生(20091000)。Control Chart Pattern Recognition Using Wavelet Analysis and Neural Networks。品質學報，16(5)，311-321。new window  new window
5.Hsu, C. C.、Chen, M. C.、Chen, L. S.(2010)。Integrating independent component analysis and support vector machine for multivariate process monitoring。Computers and Industrial Engineering，59(1)，145-156。  new window
6.Hsu, C. C.、Chen, M. C.、Chen, L. S.(2010)。Intelligent ICA-SVM fault detector for non- Gaussian multivariate process monitoring。Expert Systems with Applications，37(4)，3264-3273。  new window
7.Hsu, C. C.、Chen, M. C.、Chen, L. S.(2010)。A novel process monitoring approach with dynamic independent component analysis。Control Engineering Practice，18(3)，242-253。  new window
8.Huang, S. P.、Chiu, C. C.(2009)。Process monitoring with ICA-based signal extraction technique and CART approach。Quality and Reliability Engineering International，25(5)，631-643。  new window
9.Kano, M.、Hasebe, S.、Hashimoto, I.、Ohno, H.(2004)。Evolution of multivariate statistical process control: application of independent component analysis and external analysis。Computers and Chemical Engineering，28(6/7)，1157-1166。  new window
10.Kim, K. S.、Yum, B. J.(1999)。Control charts for random and fixed components of variation in the case of fixed wafer locations and measurement positions。IEEE Transactions on Semiconductor Manufacturing，12(2)，214-228。  new window
11.Lee, J. M.、Qin, S. J.、Lee, I. B.(2006)。Fault detection and diagnosis based on modified independent component analysis。AIChE Journal，52(10)，3501-3514。  new window
12.Lu, C.-J.、Shao, Y. E.、Li, P.-H.(2011)。Mixture control chart patterns recognition using independent component analysis and support vector machine。Neurocomputing，74(11)，1908-1914。  new window
13.Lu, C. J.、Wu, C. M.、Keng, C. J.、Chiu, C. C.(2008)。Integrated application of SPC/EPC/ICA and neural networks。International Journal of Production Research，46(4)，873-893。  new window
14.Lee, J. M.、Yoo, C. K.、Lee, I. B.(2003)。Statistical process monitoring with multivariate exponentially weighted moving average and independent component analysis。Journal of Chemical Engineering of Japan，36(5)，563-577。  new window
15.Hyvärinen, A.、Oja, E.(2000)。Independent component analysis: algorithms and applications。Neural Networks，13(4/5)，411-430。  new window
16.Kano, ‪Manabu、Tanaka, S.、Hasebe, S.、Hashimoto, I.、Ohno, H.(2003)。Monitoring Independent Components for Fault Detection。AIChE Journal，49，969-976。  new window
17.Hyvärinen, A.(1999)。Fast and robust fixed-point algorithms for independent component analysis。IEEE Transactions on Neural Networks，10(3)，626-634。  new window
18.黃馨瑩、邱志洲(20070900)。整合獨立成份分析與分類迴歸樹在製程干擾辨識上之應用。輔仁管理評論，14:(3)，121-143。new window  延伸查詢new window
圖書
1.Bowman, A. W.、Azzalini, A.(1997)。Applied smoothing techniques for data analysis。United States：New York：Oxford University Press：Oxford University Press。  new window
2.MathWorks.(2012)。MATLAB 7.14 User's Guide。Natick, MA：MathWorks Inc.。  new window
3.Montgomery, D. C.(2009)。Statistical Quality Control。New York：John Wiley & Sons。  new window
圖書論文
1.Hotelling, H.(1947)。Multivariate Quality Control Illustrated by Air Testing of Sample Bombsights。Techniques of Statistical Analysis。New York：McGraw-Hill。  new window
 
 
 
 
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