Maintenance decision-making based on condition-based maintenance for fleet

The remaining useful life prediction of aircraft fatigue structure was greatly influenced by many uncertainty factors. To overcome this weakness, a new Remaining Useful Life (RUL) prediction method based on integrating the Extended Kalman Filter(EKF)algorithm with the real-time status data was proposed to alleviate the negative influence on prediction accuracy caused by the uncertainty factors. The prediction accuracy of RUL was significantly improved through updating the uncertain parameters of fatigue crack growth model in real time. Furthermore, on the basis of the obtained RUL information of structures, a CBM-based multi-objective decision making model concentrated on both minimizing the maintenance cost and maximizing the availability of a fleet was established through taking into consideration of the maintenance resource. The numerical result demonstrated that the proposed method could estimate the RUL well and accurately identified the unknown parameters, and the established model was capable of obtaining optimization result which could simultaneously minimizing the maintenance cost and maximizing the availability on the premise of safety. © 2019, Editorial Department of CIMS. All right reserved.