Discrimination Model of QAR High-Severity Events Using Machine Learning

Abstract

The Quick Access Recorder (QAR) is an airborne equipment designed to store raw flight data, which contains a mass amount of safety related parameters such as flap angle, airspeed, altitude, etc. The assessment of QAR data is of great significance for the safety of civil aviation and the improvement of pilots skills. The existing QAR assessment approaches mainly utilizes the exceedance detection (ED) that relies on the pre-defined parameter threshold, which could miss potential flight risks. In this paper, we perform anomaly detection on the takeoff and landing phases based on an improved random forest (RF) method. The evaluation is performed on the dataset generated by a fleet of B-737NG, which shows that the method is able to discriminate the high-severity events accurately on the high dimensional multivariate time series, which also shows that the model can identify the events with potential risk pattern on the imbalanced dataset even if the event has not been pre-defined before.