Journal of Korean Society for Quality Management (품질경영학회지)

Korean Society for Quality Management (한국품질경영학회)
권호 표지
DOI QR코드

DOI QR Code

A Study on the Prediction Diagnosis System Improvement by Error Terms and Learning Methodologies Application

오차항과 러닝 기법을 활용한 예측진단 시스템 개선 방안 연구

  • Kim, Myung Joon (Department of Business Statistics, Hannam University) ;
  • Park, Youngho (Department of Business Statistics, Hannam University) ;
  • Kim, Tai Kyoo (Department of Business Statistics, Hannam University) ;
  • Jung, Jae-Seok (Technology and Research Center, Korea Midland Power Co.)
  • 김명준 (한남대학교 비즈니스통계학과) ;
  • 박영호 (한남대학교 비즈니스통계학과) ;
  • 김태규 (한남대학교 비즈니스통계학과) ;
  • 정재석 (한국중부발전(주) 기술연구센터)
  • Received : 2019.11.03
  • Accepted : 2019.11.26
  • Published : 2019.12.31
Download PDF
⟨ Previous Next ⟩

Abstract

Purpose: The purpose of this study is to apply the machine and deep learning methodology on error terms which are continuously auto-generated on the sensors with specific time period and prove the improvement effects of power generator prediction diagnosis system by comparing detection ability. Methods: The SVM(Support Vector Machine) and MLP(Multi Layer Perception) learning procedures were applied for predicting the target values and sequentially producing the error terms for confirming the detection improvement effects of suggested application. For checking the effectiveness of suggested procedures, several detection methodologies such as Cusum and EWMA were used for the comparison. Results: The statistical analysis result shows that without noticing the sequential trivial changes on current diagnosis system, suggested approach based on the error term diagnosis is sensing the changes in the very early stages. Conclusion: Using pattern of error terms as a diagnosis tool for the safety control process with SVM and MLP learning procedure, unusual symptoms could be detected earlier than current prediction system. By combining the suggested error term management methodology with current process seems to be meaningful for sustainable safety condition by early detecting the symptoms.

Keywords

References

  1. Basheer, I. A., and Hajmeer, M. 2000. "Artificial Neural Networks: Fundamentals, Computing, Design, and Apllication." Jounal of Microbiological Methods 43(3):3-31. https://doi.org/10.1016/S0167-7012(00)00201-3
  2. Ghosh, M. 1992. "Constrained Bayes Estimation with Applications." Journal of the American Statistical Association 82:1153-1162. https://doi.org/10.1080/01621459.1987.10478553
  3. Ghosh, M., Kim, Myung Joon, and Kim, Dal-Ho. 2007. "Constrained Bayes and Empirical Bayes Estimation with Balanced Loss Functions." Communication in Statistics-Theory and Method 36(8):1527-1542. https://doi.org/10.1080/03610920601125938
  4. Gunn, S. R. 1998. "Support Vector Machines for Classification and Regression." ISIS Technical Report, 14(1):5-16.
  5. Hagan, M. T., Demuth, H. B., Beale, M. H., and Jesus, O. D. 2002, "An Introduction to the Use of Neural Networks in Control Systems." International Journal of Robust and Nonlinear Control, 12(11):959-985. https://doi.org/10.1002/rnc.727
  6. Hinton, G. E. 1986, "Learning Distributed Representations of Concepts." Proceedings of the Eighth Annual Conference of the Cognitive Science Society 1:12.
  7. Kim, Myung Joon, and Kim, Yeong-Hwa. 2014. "Application of Constrained Bayes Estimation under Balanced Loss Function in Insurance Pricing." Communication for Statistical Applications and Methods 21(3):235-243. https://doi.org/10.5351/CSAM.2014.21.3.235
  8. Kim, Tai Kyoo, and Kim, Myung Joon. 2015. "A Study on the Application of Constrained Bayes Estimation for Product Quality Control." Journal of the Korean Society for Quality Management 43(1):57-66. https://doi.org/10.7469/JKSQM.2015.43.1.057
  9. Kim, Tai Kyoo, and Kim, Myung Joon. 2014. "A Study on the Bayes Estimation Application for Korean Standard-Quality Excellence Index(KS-QEI)." Journal of the Korean Society for Quality Management 42(4):747-756. https://doi.org/10.7469/JKSQM.2014.42.4.747
  10. Kourou, K., Exarchosab, T., Exarchosab, K., Karamouzisc, M. amd Fortiadisab, D. 2015. "Machine Learning Applications in Cancer Prognosis and Prediction." Computational and Structural Biotechnology Journal 13:8-17. https://doi.org/10.1016/j.csbj.2014.11.005
  11. Oliver, J. 1996. "A Machine Learning Approach to Automated Negotiation and Prospects for Electric Commerce." Journal of Management Information Systems 13:83-112. https://doi.org/10.1080/07421222.1996.11518135
  12. Picton, P. 1994. Introduction to Neural Networks. Macmillan International Higher Education.
  13. Smola, A. J., and Scholkopf B. 1998. "A Tutorial on Support Vector Regression." NeuroCOLT, Technical Report NC-TR-98-030. Royal Holloway College, University of London, UK.
  14. Vapnik, V. 2013. "The Nature of Statistical Learning Theory." Springer Science & Business Media.
  15. Vapnik, S. Golowich, and A. Smola. 1997. "Support Vector Method for Function Approximation, Regression Estimation, and Signal Processing." Neural Information Processing Systems 9. MIT Press, Cambridge, MA.