基于关联规则挖掘的航空设备故障诊断研究

Study on Fault Diagnosis of Aviation Equipment Based on Association Rules Mining

为充分挖掘利用航空公司机队维修记录,进行机队设备故障诊断和定位,提出了一种基于关联规则挖掘的航空公司机队设备故障诊断方法.在综合研究Apriori算法和FP-Growth算法原理的基础上,结合收集到的航空公司波音737NG机队维修记录,采用4.0.0版本的R语言编程软件编程实现了上述两种关联规则挖掘算法.为证明Apriori和FP-Growth两种算法在挖掘机队维修记录时的性能差异,设置了三组试验,结果表明:FP-Growth算法挖掘机队设备故障关联规则的性能更高.基于FP-Growth算法挖掘出了波音737NG机队维修记录中的故障关联规则,为航空公司机务人员开展飞机检修工作提供了技术支撑.

In order to make full use of maintenance record data of airline company’s fleet,conduct fault diagnosis and location of fleet equipment,and puts forward a fault diagnosis method of airline company’s fleet based on association rule mining.On the basis of comprehensive study of principle for Apriori algorithm and FP-Growth algorithm,and the combination of maintenance records of Boeing737-NG fleet for an airline company,the classic algorithm of above two association rules are realized by uses of the R language software of version 4.0.0.In order to prove the performance difference between Apriori algorithm and FP-Growth algorithm of airline’s fleet in mining maintenance record,three groups of comparative experiments are set up,the conclusion indicates:The efficiency of FP-Growth algorithm is higher in mining fleet equipment fault association rules.The association rules mining has conduct on the maintenance record data of Boeing737-NG airlines on the basis of FP-Growth algorithm,and some common faults of Boeing737-NG aircraft system are obtained,which provides technical support for maintenance personnel of airline company to conduct fault diagnosis work.