航空发动机车间维修成本预测

Aeroengine shop visit cost prediction

为提高航空发动机车间维修成本的预测精度,对工时+材料合同模式下的车间维修成本构成进行了分析,将其重新划分为确定Ⅰ型成本、确定Ⅱ型成本、不确定Ⅰ型成本和不确定Ⅱ型成本。对不确定Ⅰ型成本和不确定Ⅱ型成本,分别建立了重要件报废概率模型和其他器材费和修理费的成本概率模型,采用粒子群优化算法对模型参数进行了求解。采用某航空公司的实际数据对所提方法进行了验证,开发了一套原型系统。通过在某大型航空公司中的试用,验证了原型系统的有效性。

To improve prediction accuracy of aeroengine shop visit cost,the shop visit cost under contract of man hour and material was analyzed and redivided into the determinacy cost of typeⅠ,the determinacy cost of typeⅡ,the indeterminacy cost of typeⅠand the indeterminacy cost of typeⅡ.Scrap probability model of important part and the cost probability model of other material and repair were established for the indeterminacy cost of typeⅠand typeⅡ,and then particle swarm optimization was adopted to obtain models＇parameters.Finally,the presented method was verified by using the actual data from some airline and a prototype system was developed.A large airline had a trial of the prototype system.The trial results showed that the prototype system could satisfy the airline＇s requirements.