Development of a machine learning model for predicting abnormalities of commercial airplanes

Abstract

Airplanes are a social necessity for movement of humans, goods, and other. They are generally safe modes of transportation; however, incidents and accidents occassionally occur. To prevent aviation accidents, it is necessary to develop a machine-learning model to detect and predict commercial flights using automatic dependent surveillance–broadcast data. This study combined data-quality detection, anomaly detection, and abnormality-classification-model development. The research methodology involved the following stages: problem statement, data selection and labeling, prediction-model development, deployment, and testing. The data labeling process was based on the rules framed by the international civil aviation organization for commercial, jet-engine flights and validated by expert commercial pilots. The results showed that the best prediction model, the quadratic-discriminant-analysis, was 93% accurate, indicating a “good fit.” Moreover, the model's area-under-the-curve results for abnormal and normal detection were 0.97 and 0.96, respectively, thus confirming its “good fit.”