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A Fault Detection of Cyclic Signals Using Support Vector Machine-Regression  

Park, Seung-Hwan (Department of Industrial Management Engineering, Korea University)
Kim, Jun-Seok (Department of Industrial Management Engineering, Korea University)
Park, Cheong-Sool (Department of Industrial Management Engineering, Korea University)
Kim, Sung-Shick (Department of Industrial Management Engineering, Korea University)
Baek, Jun-Geol (Department of Industrial Management Engineering, Korea University)
Publication Information
Journal of Korean Society for Quality Management / v.38, no.3, 2010 , pp. 354-362 More about this Journal
Abstract
This paper presents a non-linear control chart based on support vector machine regression (SVM-R) to improve the accuracy of fault detection of cyclic signals. The proposed algorithm consists of the following two steps. First, the center line of the control chart is constructed by using SVM-R. Second, we calculate control limits by variances that are estimated by perpendicular and normal line of the center line. For performance evaluation, we apply proposed algorithm to the industrial data of the chemical vapor deposition process which is one of the semiconductor processes. The proposed method has better fault detection performance than other existing method
Keywords
Fault Detection; Cyclic Signals; Support Vector Machine-Regression;
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Times Cited By KSCI : 1  (Citation Analysis)
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1 Nello, C. and John, S.(2000), An Introduction to Support Vector Machine, Cambridge.
2 Shi, J. and Jin, J.(2000), "Diagnostic feature extraction from stamping tonnage signals based on design of experiments", Journal of Manufacturing Science and Engineering, Vol. 122, No. 22, pp. 360-369.   DOI
3 Smola, A., Scholkopf. B.(2004), "A tutorial on support vector regression", Statistics and Computing, Vol. 14, pp. 199-222.   DOI
4 Vapnik, V.(1995), The Nature of Statistical Learning Therory, Springer-Verlag.
5 Vapnik, V.(1998), The Statistical Learning Theory, John Wiley & Sons, Inc.
6 http://www.public.itrs.ne
7 박찬규(2006), "Support Vector Regression을 이용한 소프트웨어 개발비 예측", 한국경영과학회지, 23권, 2호, pp. 75-91.   과학기술학회마을
8 Burges, C. J. C.(1998), "A tutorial on support vector machines for pattern recongnition", Data Mining and Knowledge Discovery, Vol. 2, No. 2, pp. 121-167.   DOI   ScienceOn
9 Montgomery, D. C.(2001), Introduction to Statistical Quality Control, 5th Edition, Hohn Wiley & Sons, NewYork, NY.