  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 classifier leveraging a Multi-Head Convolutional Neural
Network-Recurrent Neural Network (MHCNN-RNN) architecture. Multi-class
classifiers are then created and compared, enabling the prediction of different
adverse events for any given flight by combining binary classifiers, and by
modifying the MHCNN-RNN to handle multiple outputs. Results obtained showed
that the multiple binary classifiers perform better and are able to accurately
forecast high speed and high path angle events during the approach phase.
Multiple binary classifiers are 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.
