Flight quality assessment in full flight phase based on KOA-CNN-GRU-self-attention - Wu, Tianyi; Lin, Zichun; Huang, Jianan; Ding, Yuxi; Maihemuti, Ansaierding; Xu, Xiaowei (Bulletin of the Polish Academy of Sciences Technical Sciences, 2024)
The main causes of aviation accidents in recent years are mostly related to pilot operational errors and pilot operational characteristics directly reflect flight quality. Hence, flight quality and flight safety are inseparable. Improving the assessment method of flight quality is of great significance for building a competency-based and evidence-based flight training system as well as enhancing flight safety. However, the problem is that some of the existing research is one-sided, and the assessment accuracy is not high. We propose a flight quality assessment method based on KOA-CNN-GRU-self-attention for the whole flight phase to accurately assess the flight quality and to improve and supplement the existing system. Firstly, the QAR data of the whole flight phase is selected and divided into three data sets according to the three indexes of operational smoothness, accuracy, and promptness, which are respectively substituted into the PCA comprehensive evaluation model to assess flight quality. Then, the evaluation results are labelled with the rating as the input of CNN-GRU-self-attention, and the parameters are optimized using KOA. Finally, the evaluation of flight quality for the three indexes was achieved by training the KOA-CNN-GRU-self-attention model. Thet est results show that the accuracy of operational smoothness, accuracy, and promptness reaches 98.73%, 95.07%, and 97.18%, respectively, and the assessment outcome is better and higher than the existing model. The model is also compared and analyzed with three base models CNN, QDA, XGBoost, and three fusion models CNN-self-attention, GRU-self-attention, CNN-GRU-self-attention, which show overall better results in accuracy, recall, precision, and F1-Score.