Development of Automatic Hard Landing Detection Model Using Autoencoder

A hard landing is a typical accident that occurs during the landing of an aircraft. Because hard landings can cause stress buildup in the aircraft structure that can lead to fatal accidents if not properly identified, it is necessary to clearly determine whether a hard landing has occurred. However, erroneous judgments may occur because of the limitations of the existing methods of identifying hard landings. Although many studies have been conducted to reduce misjudgments, most existing approaches require the selection of proper key (flight) parameters or predefined thresholds, which require a great deal of experience and high-level professional knowledge. Therefore, in this study, a new model for identifying hard landings without explicit selection of key parameters or manual determination of thresholds is proposed by introducing an outlier detection technique. An autoencoder, an artificial neural network model, is applied to the detection of outliers from landing data obtained through high-fidelity landing simulation. The training of the autoencoder and performance analysis is conducted to demonstrate the validity of the proposed method. Normal landing data are used as the training dataset for the autoencoder, and the percentage of abnormal landing data are gradually increased to the training dataset to check the robustness of the proposed method. The performance analysis results showed that the proposed method applying the autoencoder can be successfully used to identify hard landing situations such as late flares, even if a small amount of abnormal data are included in the training dataset, as is the case for actual landing data.