摘要
为预防民用飞机的硬着陆超限事件,首先,收集包含动力学变量、系统性能和其他工程参数的机载快速存取记录器(QAR)数据,开展机场航段聚类、样本平衡、统计特征提取等数据处理活动;然后,基于轻量级梯度提升机(LightGBM)模型预测民机硬着陆事件,并与极限梯度提升(XGBoost)、决策树(DT)、长短期记忆网络(LSTM)模型进行综合对比;最后,利用Shapley可加性解释(SHAP)算法进一步分析硬着陆事件的致因机制及各飞行参数特征对模型预测结果的影响。结果表明:所提方法不仅显示出良好的硬着陆事件预测性能,准确率、正确率和召回率分别达到99%,92%和88%,还可针对具体航段对硬着陆预测模型的决策过程提供定量的、可视化的解释信息。
In order to prevent hard landing overrun events of civil aircraft,first,data including kinematics,system performance and other engineering parameters was collected from QAR.Then QAR data processing activities such as the airport segment clustering,sample balancing and statistical feature extraction were carried out.Subsequently,LightGBM model was used to predict the hard landing events of civil aircraft,and compared with extreme gradient boosting(XGBoost),decision tree(DT)and long short-term memory(LSTM)models.Finally,the shapley additive explanation(SHAP)algorithm was employed to identify the causal mechanisms of hard landing events and to analyze the impact of various flight parameters on the model's prediction results.The result demonstrates that the proposed model not only exhibits high accuracy and precision in predicting hard landing events(accuracy,correctness and recall reaching 99%,92%and 88%,respectively),but also provides quantitative and visual explanation information for the decision-making process of hard landing prediction for specific flight segments.
作者
肖国松
刘嘉琛
张元珊
董磊
陈曦
XIAO Guosong;LIU Jiachen;ZHANG Yuanshan;DONG Lei;CHEN Xi(Key Laboratory of Civil Aircraft Airworthiness Technology,Civil Aviation University of China,Tianjin 300300,China;Science and Technology Innovation Research Institute,Civil Aviation University of China,Tianjin 300300,China;College of Safety Science and Engineering,Civil Aviation University of China,Tianjin 300300,China;COMAC Flight Test Center,Shanghai 201323,China)
出处
《中国安全科学学报》
CAS
CSCD
北大核心
2024年第10期134-142,共9页
China Safety Science Journal
基金
中央高校基本科研业务费(3122024037)
民用航空器适航审定技术重点实验室开放基金资助(SH2023101701)。
