Predicting Adverse Events and their Precursors in Aviation Using Multi-Class Multiple-Instance Learning
Marc-Henri Bleu-Laine, Tejas G. Puranik, Dimitri N. Mavris, Bryan L. Matthews · AIAA Scitech 2021 Forum · 2021
View Video Presentation: https://doi.org/10.2514/6.2021-0776.vid In recent years, there has been a rapid growth in the application of machine learning techniques that leverage aviation data collected from commercial airline operations to improve safety. Anomaly detection and predictive maintenance have been the main targets for machine learning applications. However, this paper focuses on the identification of precursors, which is a relatively newer application. Precursors are events correlated with adverse events that happen prior to the adverse event itself. Therefore, precursor mining provides many benefits including understanding the reasons behind a safety incident and the ability to identify signatures, which can be tracked throughout a flight to alert the operators of the potential for an adverse event in the future. This work proposes using the multiple-instance learning (MIL) framework, a weakly supervised learning task, combined with carefully designed binary classifiers leveraging a Multi-Head Convolutional Neural Networks-Recurrent Neural Networks (MHCNN-RNN) architecture. The classifiers are then combined to perform a multi-class task, which enables the prediction of different adverse events for any given flight and the identification of their precursors with minimum post-processing. Results obtained show that the MHCNN-RNN is able to accurately forecast high speed and high path angle events during the approach, and that it is also capable of determining the aircraft's parameters that are correlated to these events. The identified parameters can be considered precursors to the events and may be studied/tracked further to prevent these events in the future.