In recent years, there has been a rapid growth in applying 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 before the adverse event itself. Therefore, precursor mining provides many benefits, including the identification of factors relevant to the occurrence of an adverse event and their 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 framework, a weakly supervised learning task, combined with a carefully designed binary classifier leveraging a Multi-Head Convolutional Neural Network–Recurrent Neural Network (MHCNN-RNN) architecture. Multiclass 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 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.

近年来，利用从商业航空公司运营中收集的航空数据来提高安全性的机器学习技术的应用迅速增长。异常检测和预测性维护一直是机器学习应用程序的主要目标。但是，本文侧重于前体的识别，这是一个相对较新的应用。前兆是与在不良事件本身之前发生的不良事件相关的事件。因此，前体挖掘提供了许多好处，包括识别与不良事件发生相关的因素及其签名，可以在整个飞行过程中对其进行跟踪，以提醒运营商未来可能发生不良事件。这项工作建议使用多实例学习框架，弱监督学习任务，结合精心设计的二元分类器，利用多头卷积神经网络 - 循环神经网络（MHCNN-RNN）架构。然后创建和比较多类分类器，通过组合二元分类器和修改 MHCNN-RNN 来处理多个输出，从而能够预测任何给定航班的不同不良事件。获得的结果表明，多个二元分类器性能更好，并且能够在接近阶段准确预测高速和大路径角事件。多个二元分类器还能够确定与这些事件相关的飞机参数。识别的参数可以被认为是事件的前兆，并且可以进一步研究/跟踪以防止将来发生这些事件。