基于LSTM的航线飞行员操纵平稳性预测模型  

Prediction model of pilot maneuver stability based on LSTM

作　　者：王文超 何健 宋佰胜 汪磊 WANG Wenchao;HE Jian;SONG Baisheng;WANG Lei(College of Safety Science and Engineering,Civil Aviation University of China,Tianjin 300300,China;Flying Squadron Three,Shandong Airlines,Qingdao Shandong 266000,China)

机构地区：[1]中国民航大学安全科学与工程学院,天津300300 [2]山东航空股份有限公司飞行三大队,山东青岛266000

出　　处：《中国安全科学学报》2024年第12期48-55,共8页China Safety Science Journal

基　　金：中央高校基本科研业务费自然科学重点项目(3122024053)。

摘　　要：为实时预测飞行员不安全事件,使用长短期记忆神经网络(LSTM)评价飞行员操纵平稳性,并通过优化指标改进飞行员的操纵品质。首先,通过筛选飞行员在执飞中的平稳性操纵快速存取记录仪(QAR)数据,建立描述飞行员操纵行为特征的人机操纵因素集;其次,靶向分析影响飞机平稳操纵的因子,采用灰色关联度分析方法,从与飞机平稳性紧密相关的37个监测参数中定位关联风险的15个特征度量参数;然后,利用LSTM建立模型训练和测试所得数据预测飞行员的操纵平稳性,并制定指标评判标准评价安全平稳性品质;最后,通过机器学习(ML)对相关的影响因子进行重要度排序以改进模型效度。研究结果表明:时间序列模型可以有效剔除原始参数中与预测结果相关性小以及无相关的参数干扰;通过平稳性模型预测风险的精度较高,可为飞行员提供3~4 s的时间裕度采取预控措施,减少飞行过程中的不安全事件发生。To predict unsafe events for pilots in real time,a LSTM neural network was used to assess pilot maneuver stability and pilot maneuvering quality was improved by optimizing indicators.Firstly,a set of human-machine maneuvering factors presenting the pilot's maneuvering behavior characteristics was proposed by analyzing the pilot's stability maneuvering QAR data in flight.Secondly,the factors affecting the stability maneuvering of the aircraft were analyzed,and a gray correlation analysis method was used to determine the 15 characteristic parameters of associated risks from the 37 monitoring parameters closely related to the stability of the aircraft.Then,the LSTM model was used to train and test the data to predict the pilot's maneuvering stability,and indicators were proposed to evaluate safety stability quality.Finally,ML was used to rank the importance of relevant influencing factors to improve model validity.The results indicated that the time series model effectively eliminated the interference of parameters with little or no correlation with the prediction results in the original parameters.The stability model can predict risks with high accuracy and provide pilots with a 3-4 s time margin to take preventive measures and reduce unsafe incident occurrence during flight.

关 键 词：长短期记忆(LSTM) 飞行员 操纵平稳性 预测模型 快速存取记录仪(QAR) 机器学习(ML) 

分 类 号：X949[环境科学与工程—安全科学]