A Research on Periodic Multi-Maintenance Strategy of Leased Equipment Based on Failure State Evolution

doi:10.3969/j.issn.1007-7375.2018.05.008

Abstract

Abstract: In order to ensure the availability of leased equipment, and optimize equipment maintenance costs, a periodic multi-maintenance strategy based on failure state evolution is proposed. In this strategy, the equipment selects the corresponding maintenance mode based on the periodical detection of the equipment status. Service life rollback is used to describe the recovery of equipment status after various maintenance operations. Considering the periodic maintenance cost, minor repair cost, penalty cost and lease delay cost, a multi-maintenance optimization model, which is aimed at the lowest cost of the lessor, is established. Based on mathematical modeling and statistical method, MATLAB simulation is used to analyze the example. Compared with the single preventive maintenance strategy, it is proved that the effect of the leased equipment under multi-maintenance for a fixed period has been greatly improved.

Key words: leased equipment, lreventive maintenance, failure state, multi-maintenance, delay

CLC Number:

TP16

PENG Yuanru, LIU Qinming, LYU Wenyuan, YE Chunming. A Research on Periodic Multi-Maintenance Strategy of Leased Equipment Based on Failure State Evolution[J]. Industrial Engineering Journal , 2018, 21(5): 57-63.

References

[1] PONGPECH J, MURTHY D. Optimal periodic preventive maintenance policy for leased equipment[J]. Reliability Engineering & System Safety, 2006, 91(7):772-777.
[2] SCHUTZ J, REZG N. Maintenance strategy for leased equipment[J]. Computers & Industrial Engineering, 2013, 66(3):593-600.
[3] JATURONNATEE J, MURTHY D N P, BOONDISKULCHOK R. Optimal preventive maintenance of leased equipment with corrective minimal repairs[J]. European Journal of Operational Research, 2006, 174(1):201-215.
[4] HUNG W T, TSAI T R, CHANG Y C. Periodical preventive maintenance contract for leased equipment with random failure penalties[J]. Computers & Industrial Engineering, 2017, 113(11):437-444.
[5] REZG N, HAJEJ Z, BOSCHIAN-CAMPANER V. A decision optimization model for leased manufacturing equipment with warranty for a production-maintenance forecasting problem[M]. New York:Hindawi Publishing Corporation, 2015:95-124.
[6] 金琳, 王燕, 周晓军. 基于虚拟年龄的租赁设备预防性维护策略研究[J]. 工业工程与管理, 2013, 18(06):68-72+78. JIN Lin, WANG Yan, ZHOU Xiaojun. Research on virtual age based preventive maintenance policy for leased equipment[J]. Industrial Engineering and Management, 2013, 18(06):68-72+78.
[7] ISKANDAR B P, HUSNIAH H. Optimal preventive maintenance for a two dimensional lease contract[J]. Computers & Industrial Engineering, 2017, 113(9):693-703.
[8] ZHOU X, WU C, LI Y, et al. A preventive maintenance model for leased equipment subject to internal degradation and external shock damage[J]. Reliability Engineering & System Safety, 2016, 154(10):1-7.
[9] MABROUK A B, CHELBI A, RADHOUI M. Optimal imperfect maintenance strategy for leased equipment[J]. International Journal of Production Economics, 2016, 178(8):57-64.
[10] 金琳, 黄开敏, 周晓军. 租赁设备的多阶段预防性维护策略及优化[J]. 计算机集成制造系统, 2013, 19(12):2947-2953. JIN Lin, HUANG Kaimin, ZHOU Xiaojun. Multi-period preventive maintenance policy and optimization for leased equipment[J]. Computer Integrated Manufacturing Systems, 2013, 19(12):2947-2953.
[11] 董克, 吕文元. 考虑租赁延迟的设备预防维护策略优化研究[J]. 工业工程与管理, 2016, 21(01):60-66. DONG Ke, LYU Wenyuan. Optimal preventive maintenance policy for leased equipment considering lease delay[J]. Industrial Engineering and Management, 2016, 21(01):60-66.
[12] 张耀辉, 郭金茂, 徐宗昌, 等. 基于故障风险的状态维修检测间隔期的确定[J]. 中国机械工程, 2008, 19(5):555-558. ZHANG Yaohui, GUO Jinmao, XU Zongchang, et al. Failure risk based determination of the test intervals for condition based maintenance[J]. China Mechanical Engineering, 2008, 19(5):555-558.
[13] 董克, 吕文元. 基于历史故障数据的租赁设备预防维护策略优化[J]. 运筹与管理, 2017, 26(5):119-124. DONG Ke, LYU Wenyuan. Optimal periodic preventive maintenance policy for leased equipment based on history failure data[J]. Operations Research and Management Science, 2017, 26(5):119-124.