考虑不完全维修的劣化系统最优视情维修策略

 针对实际装备维修工程中系统不能"修如新"以及修后工作时间逐渐缩短的情况,基于Gamma过程建立劣化系统的视情维修(CBM)模型,以定期检测的方式获知系统状态。当系统劣化状态超过预定阈值时采取预防性维修,维修后系统恢复至"如新"与"如旧"之间的某一随机状态;经过若干次预防性维修后,当劣化状态再次超过维修阈值时则进行预防性更换;在对系统进行预防性维修或更换之前若发生劣化故障,则进行故障后更换;通过对检测周期和更换策略联合优化,使得系统长期运行费用率最低。最后通过算例说明CBM模型的可行性,并利用Monte Carlo算法仿真验证模型的正确性。

Optimization of Condition-based Maintenance for Degradation Systems Under Imperfect Maintenance

Focusing on the fact that a maintained system cannot as a rule be restored to a condition as good as new after the maintenance, and that its lifetime will be decreasing due to aging, a new condition-based maintenance (CBM) model under imperfect maintenance for a degradation system is developed based on the Gamma process. The system is periodically inspected. If the observed degradation level exceeds a threshold and if no failure occurs, a preventive maintenance is performed, which can restore the system to somewhere between as good as new and as old as bad. After several preventive maintenance actions, the degradation system should be replaced when the state exceeds the threshold again and if no failure occurs. A corrective replacement is performed when failure does occur. The joint optimization of inspection period and replacement policy is investigated for the minimization of system long run cost rate. The CBM model is analyzed through a numerical example, and the validity of the model is proved using Monte Carlo simulation method.