主管：中国科学院
主办：中国优选法统筹法与经济数学研究会
　　　中国科学院科技战略咨询研究院
中国管理科学

中国管理科学 ›› 2017, Vol. 25 ›› Issue (10): 109-118.doi: 10.16381/j.cnki.issn1003-207x.2017.10.012

• 论文 • 上一篇    下一篇

基于QAR数据的飞行操作模式及其风险分析

郑磊1,2, 池宏1, 邵雪焱1   

  1. 1. 中国科学院科技战略咨询研究院, 北京 100190;
    2. 中国科学院大学, 北京 100049
  • 收稿日期:2016-06-30 修回日期:2017-03-27 出版日期:2017-10-20 发布日期:2017-12-15
  • 通讯作者: 邵雪焱(1978-),女(汉族),山东巨野人,中国科学院科技战略咨询研究院助理研究员,研究方向:风险管理、数据挖掘;E-mail:xyshao@casipm.ac.cn. E-mail:xyshao@casipm.ac.cn

Pattern Recognition and Risk Analysis for Flight Operations

ZHENG Lei1,2, CHI Hong1, SHAO Xue-yan1   

  1. 1. Institutes of Science and Development, Chinese Academy of Sciences, Beijing 100190, China;
    2. University of Chinese Academy of Sciences, Beijing 100049, China
  • Received:2016-06-30 Revised:2017-03-27 Online:2017-10-20 Published:2017-12-15

PDF (PC)

717

被引次数

1

摘要： QAR海量数据分析是航空安全管理的一个重要课题，准确而高效地挖掘其中蕴含的飞行操作模式、评估其风险水平，是切实提高飞行操作品质的关键。然而，飞行过程中飞行员需要根据动态变化的环境条件、飞机状态，对油门、杆、盘、舵等设备进行操作，是一个需要不断调整、相互配合的过程，这增加了对飞行操作模式及其风险进行分析的难度。本文从飞行参数多元时间序列数据的特征提取及相似性度量入手，通过聚类挖掘蕴含的飞行操作模式，进而，通过分析飞行操作模式与QAR监控指标值之间的关联关系，量化不同飞行操作模式的风险水平。最后，本文以首都机场特定机型着陆阶段QAR实际数据为例，验证了模型的有效性。

关键词: QAR, 多元时间序列, 操作模式, 风险分析

Abstract: The analysis of QAR data is important to continuously improving the quality of flight operations. During the flight, the pilot controls the equipment, such as the rod, the plate, the rudder according to the dynamic changes of environmental conditions and the state of the aircraft. It is a process of constant adjustment and coordination, which increases the difficulty of data analysis. So whether pilots have similar operation patterns and what effects these operations hase on the QAR monitoring indexare of great interest to us.
In this paper, by studying the feature extraction method of the multivariate time series data of flight parameters, the definition of similarities of flight operations is analyzed.The piecewise linear fitting based multivariate Dynamic Time Warping distance is employed to depict the similarities of flight operations.The hierarchical clustering analysis is used to recognize the similar patterns of flight operations. And then, the descriptive statistics and the Kolmogorov-Smirnov test is adopted to quantify the relations between flight patterns and the QAR monitoring index. The judgement of risk levels is obtained. Finally, the validity of the model is verified by using the actual QAR data recorded during the landing stage of a specific aircraft.Other classifiers like BP Neural Networks and Support Vector Machine are used to compare with the proposed method.It turns out that the raised method provides an effective way to analyze flight operationsand the relationship between flight patterns and the QAR monitoring index.
In the future studies,focus will be put on the better description of multivariate time series and clustering methods for multivariate time series. The proposed approach could also be applied in the analysis of other vehicle driving, for example the monitoring of car driving. The method advocated couldhelp tofind the recurring patterns of drivers and how they affectsafety.

Key words: QAR, multivariate time series, operation pattern, risk analysis

中图分类号: