한국신뢰성학회지:신뢰성응용연구 (Journal of Applied Reliability)

  • 제18권2호
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  • Pages.114-121
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  • 2018
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  • 1738-9895(pISSN)
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  • 2733-8320(eISSN)
한국신뢰성학회 (The Korean Reliability Society)
권호 표지

최소수리 블록교체 모형을 활용한 상태기반 보전 정책 연구

A Study on Condition-based Maintenance Policy using Minimum-Repair Block Replacement

  • 임준형 (경기대학교 일반대학원 산업경영공학과) ;
  • 원동연 (경기대학교 일반대학원 산업경영공학과) ;
  • 심현수 (경기대학교 일반대학원 산업경영공학과) ;
  • 박철홍 (경기대학교 일반대학원 산업경영공학과) ;
  • 고관주 (경기대학교 일반대학원 산업경영공학과) ;
  • 강준규 (성결대학교 산업경영공학과) ;
  • 김용수 (경기대학교 산업경영공학과)
  • Lim, Jun Hyoung (Department of Industrial and Management Engineering, Kyonggi University Graduate School) ;
  • Won, Dong-Yeon (Department of Industrial and Management Engineering, Kyonggi University Graduate School) ;
  • Sim, Hyun Su (Department of Industrial and Management Engineering, Kyonggi University Graduate School) ;
  • Park, Cheol Hong (Department of Industrial and Management Engineering, Kyonggi University Graduate School) ;
  • Koh, Kwan-Ju (Department of Industrial and Management Engineering, Kyonggi University Graduate School) ;
  • Kang, Jun-Gyu (Department of Industrial and Management Engineering, Sungkyul University) ;
  • Kim, Yong Soo (Department of Industrial and Management Engineering, Kyonggi University)
  • 투고 : 2018.04.10
  • 심사 : 2018.05.31
  • 발행 : 2018.06.25
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초록

Purpose: This study proposes a process for evaluating the preventive maintenance policy for a system with degradation characteristics and for calculating the appropriate preventive maintenance cycle using time- and condition-based maintenance. Methods: First, the collected data is divided into the maintenance history lifetime and degradation lifetime, and analysis datasets are extracted through preprocessing. Particle filter algorithm is used to estimate the degradation lifetime from analysis datasets and prior information is obtained using LSE. The suitability and cost of the existing preventive maintenance policy are each evaluated based on the degradation lifetime and by using a minimum repair block replacement model of time-based maintenance. Results: The process is applied to the degradation of the reverse osmosis (RO) membrane in a seawater reverse osmosis (SWRO) plant to evaluate the existing preventive maintenance policy. Conclusion: This method can be used for facilities or systems that undergo degradation, which can be evaluated in terms of cost and time. The method is expected to be used in decision-making for devising the optimal preventive maintenance policy.

키워드

참고문헌

  1. Lee, Y., Lee, I., Lee, D., and Sohn, K. (2011). "Optimal Preventive Maintenance Period in Complex Systems in Considering Components Reliability Characteristic". Journal of the Korean Institute of Industrial Engineers, Vol. 37, No. 4, pp. 390-399. https://doi.org/10.7232/JKIIE.2011.37.4.390
  2. Lee, Y., Jung, K., Yoon, T., and Kwon, K. (2014). "Optimal Reliability Strategy for k-out-of-n System Considering Redundancy and Maintenance". Journal of the Korean Institute of Industrial Engineers, Vol. 40, No. 1, pp. 118-127. https://doi.org/10.7232/JKIIE.2014.40.1.118
  3. Cho, C. and Yum, B. (2011). "Development of Reliability Analysis Procedures for Repairable Systems with Interval Failure Time Data and a Related Case Study". Journal of the Korea Institute of Military Science and Technology, Vol. 14, No. 5, pp. 859-870. https://doi.org/10.9766/KIMST.2011.14.5.859
  4. Mun, B. M. and Bae, S. J. (2009). "Research for Modeling the Failure Data for a Repairable System with Non-monotonic Trend". Journal of Applied Reliability, Vol. 9, No. 2, pp. 121-130.
  5. An, D., Kim, N. H., and Choi, J. (2015). "Practical options for selecting data-driven or physics-based prognostics algorithms with reviews". Reliability Engineering and System Safety, Vol. 133, pp. 223-236. https://doi.org/10.1016/j.ress.2014.09.014
  6. Macaluso, A. (2016). "Prognostic and Health Management System for Hydraulic Servoactuators for Helicopters Main and Tail Rotor". pp. 733-736.
  7. Xu, D. and Zhao, W. (2005). "Reliability prediction using multivariate degradation data". In Reliability and Maintainability Symposium, Proceedings, Annual, IEEE, pp. 337-341.
  8. Sankavaram, C. et al. (2016). "An Inference-based Prognostic Framework for Health Management of Automotive Systems". International Journal of Prognostics and Health Management, Vol. 7, pp. 1-16.
  9. Kwon, O. and Lee, H. (2003). "A Research on the Determining Model of the Optimizing Maintenance Interval in TBM for the Preventive Maintenance of Facilities". Spring Conference of the Korean Operations and Management Science Society, pp. 106-118.
  10. George, F. (2014). "A comparison of shape and scale estimators of the two-parameter Weibull distribution". Journal of Modern Applied Statistical Methods, Vol. 13, No. 1, pp. 23-35. https://doi.org/10.22237/jmasm/1398916920
  11. Sutharssan, T., Stoyanov, S., Bailey, C., and Yin, C. (2015). "Prognostic and health management for engineering systems: a review of the data-driven approach and algorithms". The Journal of Engineering, Vol. 1, No. 1.
  12. Zio, E. and Peloni, G. (2011). "Particle filtering prognostic estimation of the remaining useful life of nonlinear components". Reliability Engineering and System Safety, Vol. 96, No. 3, pp. 403-409. https://doi.org/10.1016/j.ress.2010.08.009
  13. An, D., Choi, J., and Kim, N. H. (2013). "Prognostics 101: A tutorial for particle filter-based prognostics algorithm using Matlab". Reliability Engineering and System Safety, Vol. 115, pp. 161-169. https://doi.org/10.1016/j.ress.2013.02.019
  14. Jung, K. M. (2014). "Preventive Maintenance Policy Following the Expiration of Extended Warranty Under Replacement-Repair Warranty". Journal of Applied Reliability, Vol. 14, No. 2, pp. 122-128.