基于多值属性Apriori算法的飞机IDG故障分析

为了更为高效地挖掘海量航空维修数据中隐含的信息,提升飞机故障分析能力及深度维修能力,通过轮廓系数K-means聚类和多值属性Apriori关联规则算法挖掘飞机IDG部件各故障因素之间的关联规则。首先,采集B737飞机故障维修数据,进行预处理并筛选出与IDG部件相关的故障数据信息;其次,利用K-means聚类和多值属性Apriori关联规则算法对IDG部件故障数据进行聚类分析和关联规则挖掘,得到主要故障现象及各因素之间的强关联规则,和传统Apriori算法对比,多值属性Apriori算法的运行时间更短、效率更高;最后,结合各组件工作机理对B737飞机IDG部件提出了相关预防性维修建议。

Fault Analysis of Aircraft IDG Based on Multi-valued Apriori Algorithm

In order to mining hidden information of massive aviation maintenance data efficiently,improve aircraft fault analysis and deep maintenance capability,Mining the association rule between fault factors of aircraft IDG components by contour coefficient K-means clustering and Apriori association rule algorithm based on multi-valued attributes.Firstly,collecting fault maintenance data of B737 aircraft,then perform pre-processing and filter out fault data information associated with IDG components;secondly,performing cluster analysis and association rule mining for failure data of IDG component using K-means Clustering and Multi-Valued attribute Apriori Algorithm,obtain major failure phenomena and strong association rule between various factors,compare with the Apriori algorithm,Multi-valued attribute Apriori algorithm has shorter runtime and higher efficiency;finally,combined with the working mechanism of components to provide suggestions for preventive maintenance of B737 aircraft IDG components.