摘要
利用设备健康状态信息预测剩余使用寿命,并进行维修和备件订购决策以达到降低设备检修成本和备件成本的目的。针对单部件系统提出基于剩余寿命预测的维修与备件订购联合策略,其中维修决策遵循控制限原则,即根据系统退化量判断是预防性更换还是故障更换,同时基于历史退化信息预测系统剩余寿命,引入订货阈值判断是否订货。通过分析更换时刻备件状态确定所有可能更新事件,推导各事件发生概率进而计算各事件更新成本和更新长度,采用更新报酬理论构建最小化单位时间内期望费用的联合策略模型,设计离散事件仿真算法求解模型。最后,通过实例验证模型和算法,得到最小的单位时间内期望费用14.6563,最优预防性更换阈值8,最优订货阈值1000。
The remaining useful life(RUL)can be predicted using the information related to the equipment health state,and the decisions of maintenance and spare ordering are made to reduce the cost associated with maintenance and spare parts.A joint policy of maintenance and spare ordering for single-component systems is proposed based on RUL,in which the control limit policy is used to maintenance decision-making,i.e.judging whether a preventive replacement or a failure replacement is performed depending on the degradation level.Meanwhile,the RUL at each monitoring time is estimated based on the historical degradation information,and the order threshold is introduced to decide whether an order is made or not.All possible renewal scenarios are obtained by analyzing the spare state when the replacement is required,and the corresponding occurrence probability is derived to calculate the expected cost and length.Then,the joint policy model is established to minimize the expected cost per unit time,and a discrete event simulation algorithm is designed to address the proposed model.Finally,a case study is given to demonstrate the proposed model and algorithm,and the minimal expected cost per unit time is 14.6563 with regard to the optimal preventive replacement threshold 8 and the optimal spare ordering threshold 1000.
作者
张新辉
王雷震
赵斐
ZHANG Xinhui;WANG Leizhen;ZHAO Fei(Northeastern University at Qinhuangdao,Qinhuangdao 066004,China;School of Information Science and Engineering,Northeastern University,Shenyang 110819,China;Equipment Command and Management Department,Army Engineering University of PLA,Shijiazhuang 050003,China)
出处
《工业工程》
北大核心
2020年第4期106-113,共8页
Industrial Engineering Journal
基金
国家自然科学基金资助项目(71701038)
教育部人文社会科学研究项目(16YJC630174)
中央高校基本科研业务专项资金资助项目(N172304017),河北省自然科学基金资助项目(G2019501074)。
