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高原驻训航材有寿件需求预测研究
引用本文:毕钊,侯胜利.高原驻训航材有寿件需求预测研究[J].数学的实践与认识,2021(1):178-185.
作者姓名:毕钊  侯胜利
作者单位:;1.空军勤务学院研究生大队;2.空军勤务学院航材四站系
摘    要:高原驻训航材保障中,有寿件重要度较高,部队必须携带足够的备件数量来满足任务需要.根据高原驻训条件下航材有寿件需求特点,将有寿件的需求分为到寿更换需求和随机故障消耗产生的需求两部分,分别用数学公式和仿真计算方法进行预测,再将两者结果相加为总需求量.通过实例分析验证,采用工程测算方法可以避免逐一分析器材寿命的繁杂,而单独考虑随机故障消耗可以进一步保证有寿件备件不短缺率在90%以上.该预测,该模型可以为航材有寿件到寿情况分析以及有寿件的携行方案制定提供参考.

关 键 词:高原驻训  有寿件  随机故障  需求预测

Research on Demand Forecast of Longevity Parts of Aviation Materials in Plateau Training
BI Zhao,HOU Sheng-li.Research on Demand Forecast of Longevity Parts of Aviation Materials in Plateau Training[J].Mathematics in Practice and Theory,2021(1):178-185.
Authors:BI Zhao  HOU Sheng-li
Institution:(Postgraduate Bridade,Air Force Logistics College,Xuzhou 221000,China;Department of Air Material and Four Stations,Air Force Logistics College,Xuzhou 221000,China)
Abstract:In the maintenance of high-altitude training aviation materials,life parts are more important,and troops must carry enough spare parts to meet mission needs.In this paper,according to the characteristics of the demand for longevity parts of aviation materials under the conditions of plateau training,the demand for longevity parts is divided into two parts:the demand for replacement at the end of the life and the demand generated by random failure consumption.The mathematical formula and the simulation calculation method are used to predict.The two results add up to the total demand.Finally,the historical plateau training aviation material support data is collected to verify the model.The results show that the engineering calculation method can not only avoid the complexity of analyzing the life of the equipment one by one,but also meet the life replacement needs,and considering the random failure consumption alone can further ensure longevity.No shortage of spare parts is over90%.The prediction results can provide important reference for the guarantee personnel to analyze the life-span status of the aviation materials and to formulate the carrying plan of the longevity parts.
Keywords:plateau training  longevity parts  random failure  demand forecastplateau training  demand forecast
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