Risk assessment method for civil aircraft approach and landing at high plateau based on QAR data

Civil aircraft approach and landing at high altitude is a high-risk stage of plateau flight. In order to effectively implement the risk identification and grade judgment of approach and landing at high altitude, an LSTM-DNN deep learning risk assessment method based on variable fuzzy recognition with weights removed is proposed. This method is first based on the high-altitude flight data recorded by the quick access recorder (QAR), and draws on the civil aircraft flight quality monitoring project specification (FOQA) consultation notice and industry QAR monitoring standards. Combined with the indicator importance analysis and Delphi expert survey, five key monitoring items are extracted as the risk assessment indicators of civil aircraft approach and landing at high altitude, including large heading change during landing, low track, large descent rate during 2000-1000 ft approach, vertical acceleration at touchdown, and large descent rate during 500-50ft approach. Then, in order to overcome the subjective bias of the evaluation indicator weight, the weight of the evaluation indicator is determined by the weight removal method, and the risk grade membership function is constructed based on the variable fuzzy recognition method. Finally, a risk assessment model for civil aircraft approach and landing at high altitude based on LSTM-DNN is established. Taking the Chengdu-Lhasa approach and landing segment as an example, the QAR data was extracted to train and test the risk assessment model, and the results were compared with those of Logistic multivariate regression, support vector machine (SVM) and other evaluation methods. The results show that the average recognition rate of the LSTM-DNN deep learning model based on variable fuzzy recognition with weights removed reaches 94.18%, and the highest can reach 94.79%, which verifies the objective effectiveness of the risk assessment method.