Warranty Management and After-sales Operation Optimization of Equipments in the Environment of Internet-of-Things
为了解决在考虑生产任务的情况下，制造设备关键部件的维修决策问题，本文将生产任务信息、视情 维修以及机会维修结合起来，考虑设备关键部件的剩余价值以及可靠性风险，建立维修决策优化模型。在众多 学者对于视情维修和机会维修的研究基础上, 本文首先考虑了关键部件的剩余寿命与下一阶段的生产任务时长 之间的关系以进仛 步决策是否利用维修机会，其次考虑剩余价值和运行中维修的成本，在任务间隔机会权衡收 益成本差异，进而做出柺 终的维修决策，以保证在任务顺利进行且设备可靠的情况下降低成本。通过用逆高斯 过程对部件的還 化过程建模，计算各维修组合对应维修概率，推导出以成本最小化为目标的目标规划函数，通 过仿真算法得到预防性维修的构 优值。通过数值实验，验证了提出的策略及模型的有效性。
In order to solve the maintenance decision-making problem of the key components of the manufacturing equipment when the production task is considered, the production task information is combined with the condition-based maintenance(CBM) and opportunistic maintenance(OM), and the residual value and reliability risk of the key components of the equipment are considered, and the maintenance decision optimization model is established. On the basis of many scholars on condition-based maintenance and opportunistic maintenance, this article first considers the relationship between the remaining useful life of key components and the duration of the next stage of production tasks, decides whether to use maintenance opportunities, and secondly considers the remaining value and operating time. The cost of maintenance is the opportunity to weigh the difference in revenue and cost between tasks, and then make the final maintenance decision, in order to achieve the goal of reducing costs while ensuring the smooth progress of the task and the reliability of the equipment. By using the Inverse Gaussian process to simulate the degradation process of components, calculating each maintenance combination and its corresponding maintenance probability, deriving the cost function with the goal of cost minimization, and obtaining the optimal value of preventive maintenance through the simulation algorithm. Numerical experiments verify the effectiveness of the proposed strategy and model.