Analysis of Abnormal Flight and Controllers Data Based on DBSCAN Method

Abstract

In order to quickly and timely analyze the airborne and controllers data of domestic ARJ21 (Advanced Regional Jet for 21st Century) aircraft and find out the existing flight problems and potential safety hazards, this paper proposes a DBSCAN (density-based spatial clustering of applications with noise) clustering analysis method for aircraft airborne and controllers data outlier detection to evaluate and monitor the flight status. The flight QAR (quick access recorder) data stored in the aircraft rapid data recorder are input to the DBSCAN clustering analysis algorithm to detect the flight parameters that are different from the normal range. Compared with the traditional airborne data analysis method, this method can realize the real-time analysis and prediction of data, improve the efficiency of data analysis, and find the potential security risks according to the analysis results and deal with them in time. In this paper, 1,102 ARJ21 aircraft operation data are used to test. The results show that the DBSCAN clustering anomaly data detection method based on density is fast and accurate in detecting the continuous parameters recorded in the flight process, and the display results are easy to analyze, which can predict the potential safety problems in time. The outliers detected by this method can provide support for the controller to detect the outliers and related flight risks in daily flights.