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Detecting abnormalities in aircraft flight data and ranking their impact on the flight

机译：检测飞机飞行数据的异常情况并确定其对飞行的影响

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摘要

To the best of the author’s knowledge, this is one of the first times that a large quantity of flight data has been studied in order to improve safety. 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 analysing snapshots at chosen heights in the descent, weight individual abnormalities and quantitatively assess the overall level of abnormality of a flight during the descent. The approach expands on a recommendation by the UK Air Accident Investigation Branch to the UK Civil Aviation Authority. The first phase identifies and 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, the Support Vector Machine (SVM), the Mixture of Gaussians and the K-means one class classifiers are compared. The method is tested using a dataset containing manually labelled 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. Furthermore, the method outperforms the event based approach currently in use. The feature selection tool F-score is used to identify differences between the abnormal and normal datasets. It identifies the heights where the discrimination between the two sets is largest and the aircraft parameters most responsible for these variations.
机译：据作者所知，这是第一次研究大量飞行数据以提高安全性。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时选定高度的快照，对单个异常进行权重并定量评估下降过程中航班异常总体水平来建模正常时间序列数据的能力。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。第一阶段识别并量化飞行中某些高度的异常。第二阶段对所有航班进行排名，以找出最不正常的航班；每个阶段都使用一个分类器。对于第一阶段和第二阶段，都比较了支持向量机（SVM），高斯混合和K-均值一类分类器。使用包含手动标记的异常航班的数据集对该方法进行了测试。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。此外，该方法优于当前使用的基于事件的方法。特征选择工具F分数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。

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    Smart Edward;

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  • 年度 2011
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