Abstract: The accurate prediction of the Remaining Useful Life (RUL) is the key for the equipment's preventive maintanance decision.Based on present moment and history monitoring information,a real-time remaining useful life prediction model was established by using stochastic filtering theory under the imperfect preventive maintenance strategies.A maintenance strategy of imperfect preventive maintenance and replacement strategy by taking average remaining useful life as threshold was proposed,and an optimization model was established to choose the threshold of preventive maintenance as optimal variable and the minimum average maintenance cost as object function.Particle Swarm Optimization (PSO) algorithm was used to solve the model,which could obtain the best preventive maintenance and preventive replacement threshold,and make the long-run average cost rate lowest.The fatigue cracking which accorded with Weibull distribution was taken as the example to verify the feasibility of proposed methods.

Key words: real-time, remaining useful life, prediction, long-run average cost rate, imperfect, preventive maintenance

摘要： 准确的剩余寿命预测是对设备进行预防性维护维修决策的关键。利用随机滤波理论,基于当前时刻及之前的历史状态监测信息,由已知的设备初始寿命分布函数建立非完美维修策略下的实时剩余寿命分布函数预测模型。同时考虑非完美维修效果与时间相关性,提出一种以平均剩余寿命为阈值的非完美预防性维护及更换的维修策略,建立了以系统的预防性维护及预防性更换阈值为优化变量和最小化平均维护维修费用为目标函数的优化模型。采用微粒群算法进行优化求解,得到系统最佳的预防性维护及预防性更换阈值,并使系统长期运行平均费用率最低。以初始寿命符合威布尔分布的疲劳裂纹为例,验证了该实时剩余寿命分布预测方法及非完美维护维修策略的可行性。

关键词: 实时, 剩余寿命, 预测, 长期平均费用率, 非完美, 预防性维护维修