With the popularization and application of technologies such as monitoring and sensing, the application of condition-based maintenance has become increasingly widespread. However, most traditional condition-based maintenance strategies focus on a component or system, which is too large for fault diagnosis and location. Moreover, imperfect maintenance is seldom considered and most of them only optimize the single variable in condition-based maintenance decision. In this paper, the meta-action unit is taken as the research object, and the Wiener process is used to describe the performance degradation of the meta-action unit. Considering that the maintenance quality deteriorates with the increase of the number of maintenance, and has randomness, the beta distribution is used to establish an imperfect preventive maintenance quality model. A new maintenance optimization strategy is proposed, with the long-term maintenance cost rate as the goal, to obtain the best preventive maintenance threshold, inspection cycle and the optimal number of maintenance. Finally, a Monte Carlo simulation was used to solve numerically and analyze the influence of each parameter on the optimal decision. It can provide a reference for the decision-making when developing maintenance policies.

随着诸如监视和传感之类的技术的普及和应用，基于状态的维护的应用已变得越来越普遍。但是，大多数传统的基于状态的维护策略都集中在组件或系统上，对于故障诊断和定位而言，组件或系统太大。此外，很少考虑不完善的维护，并且大多数维护仅在基于状态的维护决策中优化单个变量。本文以元作用单元为研究对象，采用维纳过程描述元作用单元的性能下降。考虑到维护质量随维护次数的增加而恶化，并且具有随机性，因此使用beta分布来建立不完善的预防性维护质量模型。提出了一种以长期维修成本率为目标的维修优化策略，以期获得最佳的预防性维修门槛，检查周期和最佳的维修次数。最后，使用蒙特卡洛模拟对数值进行求解，并分析每个参数对最优决策的影响。它可以为制定维护策略时的决策提供参考。