Using ADOPT Algorithm and Operational Data to Discover Precursors to Aviation Adverse Events

In this paper, we describe a data mining method called ADOPT (Automatic Discovery of Precursors in Time series data) to identify precursors to aviation adverse events. An adverse event may refer to any unsafe event ranging from a negligible safety hazard to a catastrophic accident, depending on the scope of the analysis. A precursor is an early indicator of an increasing likelihood of the adverse event. Identifying precursors is important in the context of a proactive safety management because precursors detect the increasing severity of the underlying hazard much earlier, giving sufficient time to identify, analyze and implement corrective actions. ADOPT analyzes large volumes of historical data to find complex trends among several sensory variables simultaneously to find precursors. ADOPT’s data mining approach captures real-world effects such as human factors, weather, geographic constraints, operating procedures, airline strategies etc that are difficult to capture using first-principle models. This paper describes the algorithm using two case studies including a take-off stall hazard and RNAV adherence. While the case studies are not intended to discuss the critical safety risks in aviation, they are used to demonstrate the various steps involved in ADOPT including data preparation, variable selection, parameter tuning, experiment setup and analyzing the results. The results show that ADOPT can be a powerful tool to identify and analyze performance and safety issues in Aviation.