Machine Learning Prediction Model for Depot Maintenance Policy (Maintenance Source, Type, Development Cost)

According to the depot maintenance policy, the depot maintenance item, source, type and the development cost are determined. In order to efficiently link acquisition and operation, it is necessary to review the depot maintenance policy from the planning stage. This is because, depending on how the concept of depot maintenance is set, it has a great impact on the design and future operation and maintenance. In this study, a machine learning prediction model that can be used at the beginning of the acquisition stage or during the establishment of the depot maintenance policy was presented. Whether of not depot maintenance is carried out, depot maintenance source, depot maintenance type and development cost for depot maintenance element are dependent variables. The independent variables are the price of equipment, the quantity, the number of components. The statistical model used for machine learning were Logit, Probit, LPM(Linear Probability Model), and 33 depot maintenance analysis data were trained and tested. As a result of the experiment, the accuracy of the Logit model was the highest, and the accuracy of dependent vairables was up to 100%, and the average accuracy was 65%~93%.