基于灰色模糊与层次分析的多属性飞机维修决策方法
收稿日期: 2013-05-02
修回日期: 2013-07-23
网络出版日期: 2013-07-31
基金资助
航空科学基金（2010ZD54012）；国防预研项目（A0520110023）；国防基础科研项目（Z052012B002）
Multiple Attribute Maintenance Decision Making of Aircraft Based on Grey-fuzziness and Analytical Hierarchy Process
Received date: 2013-05-02
Revised date: 2013-07-23
Online published: 2013-07-31
Supported by
Aeronautical Science Foundation of China (2010ZD54012); National Defense Pre-research Foundation (A0520110023); National Defense Basic Research Program (Z052012B002)
针对目前飞机在维修保障决策过程中难以科学运用专家知识导致决策不当的问题，以飞机液压系统为具体研究对象，创建了基于灰色模糊与层次分析的多属性飞机维修保障决策模型。针对飞机液压系统健康监测时出现的不同故障模式，由专家知识确定不同故障模式对应的故障征兆是否出现以及出现的显著程度，依据历史故障统计数据、故障机理分析结果和故障征兆出现显著程度这3种不同要素对维修保障决策的影响程度不同，采用层次分析法分别确定3种不同要素的灰色模糊权重。在此基础上，由灰色模糊权重、隶属度、点灰度构建灰色模糊关系矩阵，结合不同专家知识权重，得到灰色模糊综合属性值，再由灰色模糊综合属性值确定飞机液压系统的最优维修决策。研究结果表明，采用所创建的保障决策模型对飞机液压系统进行维修保障决策，所得结果与实际状况相符，证明了所创建保障决策模型的有效性，其具有很好的应用价值与前景。
崔建国 , 傅康毅 , 陈希成 , 齐义文 , 蒋丽英 . 基于灰色模糊与层次分析的多属性飞机维修决策方法[J]. 航空学报, 2014 , 35(2) : 478 -486 . DOI: 10.7527/S1000-6893.2013.0349
In the process of aircraft maintenance support decision-making, poor decisions are often made because of the difficulty in making use of expert knowledge. This paper takes the aircraft hydraulic system as an example to establish a multiple attribute maintenance support decision model on the basis of grey-fuzzyiness and analytic hierarchy process. Different failure modes exist in aircraft hydraulic system health monitoring. The appearance of their corresponding fault symptoms and significant degrees are determined by expert knowledge. Historical fault data, fault mechanism analysis results, and the significant degrees of appearance of fault symptoms have different effects on maintenance support decision-making. This paper determines the different grey fuzzy weights for different factors by using the analytic hierarchy process. On this basis, a grey fuzzy relationship matrix is built by grey fuzzy weight, membership, and point greyness. Combined with the expert knowledge weight, the integrated grey fuzzy attribute values can be obtained and the optimal maintenance decision can be determined. This study shows that the maintenance support decision made in this paper is consistent with the actual situation. The modeling is proved valid and it has important value and bright prospects for application.
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