A Fault Diagnosis Methodology for Module Process of TFT-LCD Manufacture Using Support Vector Machines

SVM을 이용한 TFT-LCD 모듈공정의 불량 진단 방안

  • Shin, Hyun-Joon (Department of Management Engineering, Sangmyung University)
  • 신현준 (상명대학교 경영공학과)
  • Received : 2010.11.30
  • Accepted : 2010.12.17
  • Published : 2010.12.31

Abstract

Fast incipient fault diagnosis is becoming one of the key requirements for economical and optimal process operation management in high-tech industries. Artificial neural networks have been used to detect faults for a number of years and shown to be highly successful in this application area. This paper presents a novel test technique for fault detection and classification for module process of TFT-LCD manufacture using support vector machines (SVMs). In order to evaluate SVMs, this paper examines the performance of the proposed method by comparing it with that of multilayer perception, one of the artificial neural network techniques, based on real benchmarking data.

Keywords

References

  1. Muller K.R., Mika S., Ratsch G., Tsuda K., & Scholkopf B.,"An introduction to kernel-based learning algorithms," IEEE Transactions on Neural Networks, Vol. 12, pp. 181-201, 2001. https://doi.org/10.1109/72.914517
  2. Dumais S., Platt J., Heckerman D., & Sahami M.,," Inductive learning algorithms and representations for text categorization," J In: Proceedings of ACM-CIKM98, pp. 148-155, 1998.
  3. Osuna E., Freund R., & Girosi F., " Training support vector machines: an application to face detection," In: 1997 Conference on Computer Vision and Pattern Recognition, pp. 130-136, 1997.
  4. Roobaert D., & Hulle V.M.," View-based 3d-object recognition with support vector machines," In: 1999 IEEE Workshop on Neural Networks for Signal Processing, pp. 77-84, 1999.
  5. Park J., & Hong T., "The Prediction of DEA based Efficiency Rating for Venture Business Using Multi-class SVM," Asia Pacific Journal of Information Systems, Vol. 19, pp.139-155, 2009.
  6. Liu S., Wang Z., Zin H., & Hu S.," Support Vector Machine based fault diagnosis for an unmanned tandem helicopter," In: 2008 IEEE World Congress on Intelligent Control and Automation, pp. 1261-1265, 2008.
  7. Shin H.J., Eom D.H., Kim S.S., "One-class support vector machines-an application in machine fault detection and classification," Computers & Industrial Engineering, Vol. 48, pp. 395-408, 2005. https://doi.org/10.1016/j.cie.2005.01.009
  8. Vapnik V., " The Nature of Statistical Learning Theory," 1995, Berlin:Springer.
  9. Scholkopf B., & Smola A., "Learning with kernels - support vector machines, regularization, optimization and beyond," 2002, Cambridge, MA:MIT Press.
  10. McCormick, A.C., & Nandi, A.K.," Real time classification of rotating shaft loading conditions using artificial neural networks," IEEE Transactions on Neural Networks, Vol. 8, pp. 748-757, 1997. https://doi.org/10.1109/72.572110
  11. Geisser, S., "The predictive sample reuse method with applications," Journal of the American Statistical Association, Vol. 70, pp. 320-328, 1975. https://doi.org/10.2307/2285815