Abstract:

In order to reduce the operational cost and take full advantage of conditionbased maintenance (CBM), the joint optimization of spare parts inventory and CBM decision is carried out. Firstly, the degradation model of the equipment is established with the Wiener process, and the degradation parameters and residual life are estimated. Secondly, the concept of appointment of spare parts is adopted to make full use of residual life information,and inventory parameters, inspection interval and appointment threshold are used as variables, the genetic algorithm and the Monte Carlo method are combined to set up a joint optimization simulation model of maintenance and spare parts to minimize the total cost rate. Finally, an example is analyzed, and the result shows that the joint optimization method greatly reduces the total cost of maintenance and spares part, which indicates the feasibility and effectiveness of the proposed method.