基于聚类和拟合的QAR数据离群点检测算法  

QAR data outlier detection algorithm based on clustering and fitting

作　　者：杨慧[1] 王丽婧[1] 

机构地区：[1]中国民航大学计算机科学与技术学院,天津300300

出　　处：《计算机工程与设计》2015年第1期174-177,共4页Computer Engineering and Design

基　　金：国家自然科学基金与中国民航联合基金项目(61179063);国家自然科学基金项目(61301245)

摘　　要：为解决从飞机快速存取记录器(QAR)数据中发现异常数据并预测飞机潜在故障的问题,考虑QAR数据量大、飞行参数数据值相对较为稳定的特点,提出一种适用于QAR数据的离群点检测算法。第一阶段采用K均值聚类对QAR数据流分区进行聚类生成均值参考点;第二阶段采用最小二乘法对生成的均值参考点进行拟合,通过计算均值参考点到拟合飞机参数曲线的距离来判断并找出可能的离群点。实验结果表明,该算法可以准确发现飞机中的故障数据,有效解决部分飞机故障的离群点检测问题。To find the abnormal data of the aircraft quick access recorder （QAR） data and predict potential problems for planes, the characteristics of the large amount of QAR data and the relatively stable flight parameter data values were considered, and an outlier detection algorithm applied to QAR data was proposed. In the first stage of the algorithm, the K-means method was used to cluster the QAR data streams, and reference points were generated. In the second stage, the least squares method was used to fit the reference points. The distances from the reference points to the aircraft parametric curve fitted by the least squares method were computed to determine and identify the possible outliers. The results show that for some plane troubles, the algorithm can effectively solve the problem of outlier detection and find the fault data in QAR accurately.

关 键 词：飞机时序数据 K均值聚类 均值参考点 最小二乘法 离群点检测 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]