베이지안 결합을 활용한 물류산업장비 고장함수에 관한 연구: 전동 지게차를 중심으로
A Study on the Failure Function of Logistics Industrial Equipment Using Bayesian Aggregation: An Application to Electric Forklift Failure Rate
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초록
Equipment failures create unnecessary lead time throughout the logistics supply chain. It is known that the probability of occurrence of a failure continues to change over its total life. Therefore, a failure function is required. In this study, the failure function was derived using the data of electric forklifts of domestic logistics companies. The Bayesian aggregation method, which can simultaneously reflect various characteristic information, was used to derive the failure function. In addition, a periodic preventive maintenance model was additionally established through the construction of the Mean Time Between Failure(MTBF) model. This study is meaningful in that it is designed to automatically find the optimal value in estimating the characteristic parameters of the Bayesian aggregation model. The results of this study can be used universally in all fields of equipment operation. It is expected to improve the stability of the supply chain.
Abstract
Equipment failures create unnecessary lead time throughout the logistics supply chain. It is known that the probability of occurrence of a failure continues to change over its total life. Therefore, a failure function is required. In this study, the failure function was derived using the data of electric forklifts of domestic logistics companies. The Bayesian aggregation method, which can simultaneously reflect various characteristic information, was used to derive the failure function. In addition, a periodic preventive maintenance model was additionally established through the construction of the Mean Time Between Failure(MTBF) model. This study is meaningful in that it is designed to automatically find the optimal value in estimating the characteristic parameters of the Bayesian aggregation model. The results of this study can be used universally in all fields of equipment operation. It is expected to improve the stability of the supply chain.
참고문헌 (23)
- 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)
- 최진우, "고장함수를 고려한 총수명주기간 계획정비 간격 최적화에 관한 연구", 로지스틱스연구, 28(6), pp.57~70 (2020), 한국로지스틱스학회[DOI]
- 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 Statistics and 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)
- Jagarlamudi, S., "Failure rate Studies and design alternatives for standup forklift trucks", Master, pp.2004
- Massone, J. M., "Failure of forklift forks", Engineering Failure Analysis, 17(5), pp.1062~1068 (2010)
- 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(mc-stan.org) (2020)
- Moon, H., "Hierarchical spline for time series prediction: An application to naval ship engine failure rate", Applied AI Letters, 2(1), pp.22~ (2021)
- Sherbrooke, C. C., "Optimal inventory modeling of systems: multi-echelon techniques", Springer Science & Business Media, 72 (2006)
- Tabandeh, A., "Empirical Bayes approach for developing hierarchical probabilistic predictive models and its application to the seismic reliability analysis 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)
- Zammori, F., "A constructive algorithm to maximizethe useful life of a mechanical system subjected to ageing, withnonresuppliable spares parts", International Journal of Industrial Engineering Computations, 11(1), pp.17~34 (2020)