Abstract：In order to solve the unreasonable problem that may exist in the establishment of support resource indicators in the process of equipment RMS indicator demonstration, and based on the common spare parts model in the process of maintenance, the utilization rate and satisfaction rate of spare parts are interpreted. Combined with the typical life distribution of spare parts prediction model and the experience of performance measurement of machine learning method, some of the comprehensive tradeoff of supportability is presented. The application analysis of the model is carried out with a practical case, and the results of parameter analysis are given. The results show that the comprehensive tradeoff method of support resource indicators based on machine learning performance measurement theory is practical in engineering and has significant theoretical advantages in comprehensive balancing, which can improve the efficiency of support resource indicator demonstration.
甘娥忠, 刘焱, 王海荣, 王承光. 基于机器学习性能度量理论的保障资源指标综合权衡研究[J]. 空天防御, 2023, 6(1): 38-44.
GAN Ezhong, LIU Yan, WANG Hairong, WANG Chengguang. Research on Comprehensive Tradeoff of Supportability Based on Machine Learning Performance Measurement Theory. Air & Space Defense, 2023, 6(1): 38-44.
