综合考虑非定期检测和非完美维修，融合(s, S)库存策略，提出一种新的视情维修与备件库存联合优化策略。基于Wiener过程描述系统退化过程，引入残余退化量和退化速率，建立基于Wiener过程的非完美视情维修模型ˑ；基于首达时间的概念给出剩余寿命的概率分布，并根据剩余寿命预测结果确定非定期性能检测间隔期；建立融合(s, S)备件库存策略的联合优化模型，采用蒙特卡洛仿真与遗传算法，以部件长期平均维修费用率最小为目标对模型进行优化求解。以陀螺仪漂移退化数据为例对本文方法进行验证，并与不同假设条件下的优化模型进行对比分析。结果表明，所提方法可有效降低运维成本，在提升系统可靠性的同时显著优化备件库存水平，为工业设备维护决策提供了新的技术支撑。

Considering non-periodic inspection and imperfect maintenance comprehensively, and integrating the (s, S) inventory policy, this paper proposes a novel joint optimization strategy for condition-based maintenance and spare parts inventory. The system degradation process is described based on the Wiener process, and residual degradation and degradation rate are introduced to establish an imperfect condition-based maintenance model. Based on the concept of first-passage time, the probability distribution of remaining useful life (RUL) is derived, and the interval of non-periodic performance inspection is determined according to the predicted value of RUL. A joint optimization model integrating the (s, S) spare parts inventory policy is established, and the model is optimized and solved by combining Monte Carlo simulation with genetic algorithm, with the objective of minimizing the long-term average maintenance cost rate of components. The proposed method is validated using gyroscope drift degradation data, and compares it with optimization models based on different assumptions. The results show that the proposed method can effectively reduce operation and maintenance costs, significantly optimize the spare parts inventory level while improving system reliability, providing new technical support for maintenance decision-making of industrial equipment.