基于损伤标尺的电子设备预测维修决策优化

 基于故障预测信息进行维修决策是预测性维修等新型维修模式的主要特征之一,可以有效提高装备使用可用度、降低寿命周期费用。面向单部件电子系统,针对故障预测中的损伤标尺方法,在完美换件维修的假设下,以单位时间成本、平均使用可用度与平均效费比为指标,提出了一种预测维修决策优化模型。对于与外场可更换单元(LRU)寿命独立的损伤标尺,选择预测距离与Weibull分布的形状参数为决策变量,对于与LRU寿命相关的损伤标尺,选择累积损伤因子与随机标准差为决策变量,应用Monte Carlo方法仿真研究了各个决策变量对维修效能指标的影响。结果表明:应用与LRU寿命独立损伤标尺的预测维修策略的效果优于事后维修策略,但劣于年龄换件策略,揭示了该方法的本质特性;应用与LRU寿命相关损伤标尺的预测维修策略的效果在一定条件下优于年龄换件策略。最后分析了维修决策的优选方法。

Optimal Predictive Maintenance Decision of Electronics Based on Canaries

Predictive maintenance based on prognostic information is an emerging maintenance mode which can decrease life cycle cost and increase operational availability effectively. This paper focuses on the prognostic approach based on canaries which can be divided into two categories: line replaceable unit(LRU)-independent canaries and LRU-dependent canaries. Under the perfect replacement assumption, a predictive maintenance decision model is proposed based on the renewal reward theorem, which can evaluate the benefit of the use of canary devices from the long-run average cost rate, average operational availability and average effectiveness-cost ratio. For LRU-independent canaries, the prognostic distance and shape parameter of Weibull distribution are chosen to optimize the maintenance decision, while for LRU-dependent canaries, the accumulated damage factor and stochastic standard deviation are chosen as decision variables. Finally, this model is demonstrated with a numerical implementation example using Monte Carlo simulation. The results show that the predictive maintenance policy with LRU-independent canaries is better than the corrective maintenance policy but worse than the age replacement policy, which exhibits an essential characteristic of the method. Furthermore, the predictive maintenance policy with LRU-dependent canaries is better than the age replacement policy in some conditions and the optimal parameters of canaries for maintenance decisions are chosen.