Explaining Aviation Safety Incidents Using Deep Temporal Multiple Instance Learning

TL;DR

This paper introduces a deep temporal multiple-instance learning approach to analyze aviation safety incidents, identifying precursors in complex time series data to improve safety monitoring and incident explanation.

Contribution

It combines multiple-instance learning with deep recurrent neural networks to effectively analyze high-dimensional, weakly labeled time series data for aviation safety incident prediction.

Findings

The proposed method outperforms baseline models in identifying safety incident precursors.

It demonstrates scalability and effectiveness in real-world aviation data.

The approach provides interpretable insights into safety incident causes.

Abstract

Although aviation accidents are rare, safety incidents occur more frequently and require a careful analysis to detect and mitigate risks in a timely manner. Analyzing safety incidents using operational data and producing event-based explanations is invaluable to airline companies as well as to governing organizations such as the Federal Aviation Administration (FAA) in the United States. However, this task is challenging because of the complexity involved in mining multi-dimensional heterogeneous time series data, the lack of time-step-wise annotation of events in a flight, and the lack of scalable tools to perform analysis over a large number of events. In this work, we propose a precursor mining algorithm that identifies events in the multidimensional time series that are correlated with the safety incident. Precursors are valuable to systems health and safety monitoring and in explaining and forecasting safety incidents. Current methods suffer from poor scalability to high dimensional time series data and are inefficient in capturing temporal behavior. We propose an approach by combining multiple-instance learning (MIL) and deep recurrent neural networks (DRNN) to take advantage of MIL's ability to learn using weakly supervised data and DRNN's ability to model temporal behavior. We describe the algorithm, the data, the intuition behind taking a MIL approach, and a comparative analysis of the proposed algorithm with baseline models. We also discuss the application to a real-world aviation safety problem using data from a commercial airline company and discuss the model's abilities and shortcomings, with some final remarks about possible deployment directions.