基于Energy-Metrics的训练飞行数据异常识别方法  

Training Flight Data Abnormal Recognition Method Based on Energy-Metrics

作　　者：曾琛[1] ZENG Chen(Xinjin College,Civil Aviation Flight College of China,Xinjin 611431,China)

机构地区：[1]中国民航飞行学院新津分院,新津611431

出　　处：《科学技术与工程》2024年第25期10993-11001,共9页Science Technology and Engineering

基　　金：国家重点研发计划(2021YFB2601704-02,2021YFF0603904);2023年度中央高校基本科研业务费资助项目青年基金(QJ2023-054)。

摘　　要：训练飞行的首要前提就是保证人机安全,通过分析飞行数据来监控和评估飞行质量成为提高安全性和训练质量的手段之一。基于Energy-Metrics(能量度量)的异常飞行数据分析方法可为分析训练飞行安全分析提供帮助。提出了一种在训练飞行进近着陆阶段基于能量度量的异常飞行数据识别方法,首先,通过基于能量度量指标方法为飞行数据生成特征向量,然后,借助具有噪声的基于密度的聚类方法(density-based spatial clustering of applications with noise,DBSCAN)聚类和单类支持向量机(support vector machine,SVM)方法,对能量度量的指标数据特征向量进行飞行异常检测和识别。通过将模拟异常飞行数据隐藏在实际飞行数据中进行飞行数据的检测和识别,证明该方法的异常数据检测成功率为95%以上,且使用不同能量度量指标识别出的异常飞行数据具有高达98%的一致性,证明了该方法在识别异常飞行数据上具有较强的有效性和鲁棒性,为训练飞行回顾性安全分析提供有力的帮助。In order to ensure human-machine safety during training flight,analyzing flight data to monitor and evaluate flight quality has become one of the means to improve safety and training quality.The flight anomaly data analysis method based on Energy-Metrics was used to investigate the safety of training flights.A general method for identifying abnormal flight data during the approach and landing phase of training flights based on energy metrics was proposed.Firstly,the energy metric was used to generate feature vectors for flight data records.Then,with the help of density-based spatial clustering of applications with noise(DBSCAN)clustering and single class support vector machine(SVM)methods,the energy metric metric data was used for flight anomaly detection and recognition.By hiding simulated abnormal flight data in actual flight data,flight data was detected and identified.The results show that the detection success rate of this method is over 95%,and the consistency of abnormal flight data identified using different energy measurement indicators is as high as 98%.It is concluded that this method has strong effectiveness and robustness in identifying abnormal flight data.

关 键 词：能量指标 训练飞行 安全分析 数据挖掘 

分 类 号：V211.7[航空宇航科学与技术—航空宇航推进理论与工程]