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

Research on Comprehensive Tradeoff of Supportability Based on Machine Learning Performance Measurement Theory

为了解决装备RMS指标论证过程中保障资源指标订立可能存在不合理的问题,以维修过程中常见的现场更换维修为背景,对备件利用率和备件满足率这对典型保障资源指标进行重新解读,借鉴机器学习领域中的性能度量理论,结合典型寿命分布条件下的备件需求量预测模型,提出了不同备件分类条件下的保障资源指标综合权衡方法。利用实际案例对该模型进行应用解析,并给出了参数分析结果。结果表明:基于机器学习性能度量理论的保障资源指标综合权衡方法在指标论证方面具有理论优势和工程可实践性,可提高保障资源指标论证工作的效率。

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.