Machine Learning기법을 이용한 Robot 이상 예지 보전

Predictive Maintenance of the Robot Trouble Using the Machine Learning Method

  • 최재성 (극동대학교 반도체장비공학과)
  • Choi, Jae Sung (Department of Semiconductor Equipment Engineering, Far East University)
  • 투고 : 2020.01.31
  • 심사 : 2020.03.18
  • 발행 : 2020.03.31

초록

In this paper, a predictive maintenance of the robot trouble using the machine learning method, so called MT(Mahalanobis Taguchi), was studied. Especially, 'MD(Mahalanobis Distance)' was used to compare the robot arm motion difference between before the maintenance(bearing change) and after the maintenance. 6-axies vibration sensor was used to detect the vibration sensing during the motion of the robot arm. The results of the comparison, MD value of the arm motions of the after the maintenance(bearing change) was much lower and stable compared to MD value of the arm motions of the before the maintenance. MD value well distinguished the fine difference of the arm vibration of the robot. The superior performance of the MT method applied to the prediction of the robot trouble was verified by this experiments.

키워드

참고문헌

  1. Zhi Peng Chang, Yan Wei Li, Nazish Fatima, "A theoretical survey on Mahalanobis-Taguchi system," Elaevier, Measurement, 136, pp. 5011-510, (2019).
  2. Boby John, R.S.Kadadevarmath., "A Methodology for quantitatively managing the bug fixing process using Mahalanobis Taguchi System", Measurement Science Letters, 5, pp. 1081-1090, (2015).
  3. Xiaohang Jin, Yu Wang, Tommy W.S. Chow, Y. Sun, "Mahalanobis Distance Based Approaches for System Health Monitoring", IET Science Measurement & Technology, 11, pp. 371-379, (2017). https://doi.org/10.1049/iet-smt.2016.0340
  4. Taguchi. G., Rajesh. J., "New Trends in Multivariate Diagnosis", Indian Journal of Statistics, Series B, 62(2), pp. 233-248, (2000).
  5. Wu. Y., "Pattern Recognition using Mahalanobis Distance", Journal of Quality Engineering Forum, 12(5), pp. 787-795, (2004).
  6. Sahoo. A.K., Rout. A.K., Das. D.K., "Response surface and artificial neural network prediction model and optimization for surface roughness in machining", International Journal of Industrial Engineering Computations, 6, pp.229-240, (2015). https://doi.org/10.5267/j.ijiec.2014.11.001
  7. T. Riho, A. Suzuki, J. Oro, et al, "The yield enhancement methodology for invisible defects using the MTS+ method", IEEE Trans. Semicond. Manuf., I8(4), pp. 561-568, (2005).
  8. Yang, T. Cheng, Y.T., "The use of Mahalanobi-Taguchi System to improve flip-chip bumping height inspection efficiency", Microelectron. Reliab., 50(3), pp. 407-414, (2010).
  9. F. Provost, T. Fawcett, "Robust classification for imprecise environments", Machine Learning, 42(3), pp.203-231, (2001). https://doi.org/10.1023/A:1007601015854
  10. Jardine, A.K.S., Lin. D., Banjevic. D., "A Review on machinery diagnostics and prognostics implementing condition-based maintenance", Mech. Syst. Signal Process., 20, pp. 1483-1510, (2006). https://doi.org/10.1016/j.ymssp.2005.09.012