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)

Abstract:

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.

Key words: long short-term memory (LSTM), pilot, maneuvering stability, prediction model, quick access recorder (QAR), machine learning (ML)