Support Vector Machine-Regression을 이용한 주기신호의 이상탐지
A Fault Detection of Cyclic Signals Using Support Vector Machine-Regression
0
조회
0
다운로드
5
인용
0
참고문헌
키워드
초록
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
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
참고문헌 (9)
- 박찬규, "Support Vector Regression을 이용한 소프트웨어 개발비 예측", 경영과학, 23(3), pp.75~92 (2006), 한국경영과학회
- Burges,C.J.C, "A tutorial on support vector machines for pattern recongnition", Data Mining and Knowledge Discovery, 2(2), pp.121~167 (1998)
- Montgomery,D.C, "Introduction to Statistical Quality Control" (2001), Hohn Wiley & Sons
- Nello,C, "An Introduction to Support Vector Machine", ~ (2000)
- Shi,J, "Diagnostic feature extraction from stamping tonnage signals based on design of experiments", Journal of Manufacturing Science and Engineering, 122(22), pp.360~369 (2000)
- Smola,A, "A tutorial on support vector regression", Statistics and Computing, 14, pp.199~222 (2004)
- Vapnik,V, "The Nature of Statistical Learning Therory" (1995), Springer-Verlag
- Vapnik,V, "The Statistical Learning Theory" (1998), John Wiley & Sons, Inc.
