고장함수를 고려한 총수명주기간 계획정비 간격 최적화에 관한 연구
Study on the Optimization of Planned Maintenance Intervals Considering the Life Cycle Failure Function
0
조회
0
다운로드
8
인용
0
참고문헌
키워드
초록
Planned maintenance reduces the occurrence of excessive lead times during production or transportation, and prevents large-scale accidents during equipment operation. The probability of failure(Failure-Function) during total life cycle should be considered to establish a maintenance schedule. In this study, we propose an Interval-Optimization- Maintenance. This divides the total life cycle according to the failure probability based on the Failure-Function, and performs an optimized Period-Based-Maintenance in the divided interval. The proposed model has both the convenience of scheduling and prevention of failure. And, the proposed model is a realistic model that can be applied to reality. Failure-Function was derived using the data of the Naval ship and the hierarchical Bayesian estimation method. The results of this study can be used in all industries operating equipment. It is possible to optimize the planned maintenance interval by estimating the Failure-Function of the industrial group and constructing a maintenance model.
Abstract
Planned maintenance reduces the occurrence of excessive lead times during production or transportation, and prevents large-scale accidents during equipment operation. The probability of failure(Failure-Function) during total life cycle should be considered to establish a maintenance schedule. In this study, we propose an Interval-Optimization- Maintenance. This divides the total life cycle according to the failure probability based on the Failure-Function, and performs an optimized Period-Based-Maintenance in the divided interval. The proposed model has both the convenience of scheduling and prevention of failure. And, the proposed model is a realistic model that can be applied to reality. Failure-Function was derived using the data of the Naval ship and the hierarchical Bayesian estimation method. The results of this study can be used in all industries operating equipment. It is possible to optimize the planned maintenance interval by estimating the Failure-Function of the industrial group and constructing a maintenance model.
참고문헌 (27)
- 남승엽, "전시 피격 및 고장을 고려한 전투기 추가 소요 예측 모델 연구", 로지스틱스연구, 24(1), pp.81~94 (2016), 한국로지스틱스학회[DOI]
- 류창남, "상호 의존적인 고장", 수리율을 갖는 정비 시스템의 서비스 수준 평가 모형, 2018, pp.26(1), 1~12[DOI]
- 유일, "단계형 고장", 수리시간과 피드백이 있는 정비 시스템에서 장비 예비품 재고수준 결정모형, 2011, pp.19(1), 5~21[DOI]
- Afenyo, M., "Arctic shipping accident scenario analysis using Bayesian Network approach", Ocean Engineering, 133, pp.224~230 (2017)
- Belotti, P., "Mixed-integer nonlinear optimization", Acta Numerica, 22, pp.1~131 (2013)
- Carpenter, B., "Stan : Aprobabilistic programming language", Journal of Statistical Software, 76(1) (2017)
- Choi, Y-C., "The study on balanced team assignment problem using the mixed integer non-linear programming", Journal of military science and technology studies, 12(2), pp.11~20 (2019)
- Cleveland, R. B., "STL : aseasonal-trend decomposition", Journal of official statistics, 6(1), pp.3~73 (1990)
- Dagum, E. B., "Seasonal adjustment methods and real time trend-cycle estimation" (2016), Springer International Publishing
- Dikis, K., "Dynamic predictive reliability assessment of ship systems", International Journal of Naval Architecture and Ocean Engineering (2019)
- Gelman, A., "Bayesian data analysis", pp.2013, Chapman and Hall
- Gelman, A., "Multilevel(hierarchical)modeling : what it can and cannot do", Technometrics, 48(3), pp.432~435 (2006)
- hierarchical models, "Data analysis using regression and multilevel", pp.2006, Gelman, A.
- Hyndman, R. J., "Optimal combination forecasts for hierarchical time series", Computational Statisticsand Data Analysis, 55(9), pp.2579~2589 (2011)
- Hyndman, R. J., "Forecasting : principles and practice" (2018), OTexts
- Hyndman, R. J., "Another look at measures of forecast accuracy", International Journal of Forecasting, 22, pp.679~688 (2006)
- Hyndman, R. J., "A state space framework for automatic forecasting using exponential smoothing methods", International Journal of forecasting, 18(3), pp.439~454 (2002)
- McElreath, R., "Statistical rethinking: A Bayesian course with examples in R and Stan" (2020), CRC press
- Moon, H-J., "Failure prediction in hierarchical equipment system: spline fitting naval ship failure", Stancon 2020 (2020)
- ROK NAVY, "Ship's Maintenance System", 9~10 (2018)
- Sherbrooke, C. C., "Optimal inventory modeling of systems: multi-echelon techniques (Vol. 72)" (2006), Springer Science & Business Media
- Tabandeh, A., "Empirical Bayes approach for developing hierarchical probabilistic predictive models and its application to the seismic reliabilityanalysis of FRP-retrofitted RC bridges", ASCE-ASME Journal of Risk andUncertainty in Engineering Systems, Part A:Civil Engineering, 1(2), pp.04015002~ (2015)
- Taieb, S. B., "Regularization in hierarchical time series forecasting with application to electricity smartmeter data", Thirty-First AAAI Conference on Artificial Intelligence (2017)
- Taylor, S. J., "Forecasting at scale", The American Statistician, 72(1), pp.37~45 (2018)
- Wang, J., "Failure Rate Prediction Model of Substation Equipment Based on Weibull Distribution and Time Series Analysis", IEEE Access, 7, pp.85298~85309 (2019)
- 유정민, "SNA 기반의 해군 함정 정비경향 분석", 한국컴퓨터정보학회논문지, 24(6), pp.165~174 (2019), 한국컴퓨터정보학회[DOI]
- Zammori, F., "A constructive algorithm to maximizethe useful life of a mechanical system subjected to ageing, withnon-resuppliable spares parts", International Journal of Industrial Engineering Computations, 11(1), pp.17~34 (2020)
