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首页> 外文期刊>Intelligent Transportation Systems, IEEE Transactions on >A Two-Phase Method of Detecting Abnormalities in Aircraft Flight Data and Ranking Their Impact on Individual Flights
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A Two-Phase Method of Detecting Abnormalities in Aircraft Flight Data and Ranking Their Impact on Individual Flights

机译：检测飞机飞行数据异常并对它们对单个飞行的影响进行排序的两阶段方法

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摘要

A two-phase novelty detection approach to locating abnormalities in the descent phase of aircraft flight data is presented. It has the ability to model normal time series data by analyzing snapshots at chosen heights in the descent, weight individual abnormalities, and quantitatively assess the overall level of abnormality of a flight during the descent to a given runway. The method models normal approaches to a given runway (as determined by the airline's standard operating procedures) and detects and ranks deviations from that model. The approach expands on a recommendation by the UK Air Accident Investigation Branch to the UK Civil Aviation Authority. The first phase quantifies abnormalities at certain heights in a flight. The second phase ranks all flights to identify the most abnormal, each phase using a one-class classifier. For both the first and second phases, i.e., the support vector machine (SVM), the mixture of Gaussians and the $K$-means one-class classifiers are compared. The method is tested using a data set containing manually labeled abnormal flights. The results show that the SVM provides the best detection rates and that the approach identifies unseen abnormalities with a high rate of accuracy. The feature selection tool $F$ -score is used to identify differences between the abnormal and normal data sets. It identifies the heights where the discrimination between the two sets is largest and the aircraft parameters most responsible for these variations. The method presented adds much value to the existing event-based approach.
机译：提出了一种两阶段新颖性检测方法来定位飞机飞行数据下降阶段的异常。它具有通过分析下降沿选定高度处的快照，对单个异常进行权重并对正常时间序列数据建模的能力，并能够定量评估下降到给定跑道的飞行异常总体水平。该方法对给定跑道的正常进场建模（由航空公司的标准操作程序确定），并检测与该模型的偏差并对其进行排名。该方法是根据英国航空事故调查处向英国民航局的建议而扩展的。第一阶段量化飞行中某些高度的异常。第二阶段使用一类分类器对所有航班进行排名，以找出最不正常的阶段。对于第一阶段和第二阶段，即支持向量机（SVM），比较了高斯和$ K $-均值一类分类器的混合。使用包含手动标记的异常航班的数据集测试该方法。结果表明，SVM提供了最佳的检测率，并且该方法能够以较高的准确率识别出看不见的异常。功能选择工具$ F $ -score用于识别异常和正常数据集之间的差异。它确定了两组之间最大区别的高度，以及造成这些变化的最主要飞机参数。提出的方法为现有的基于事件的方法增加了很多价值。

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