Predicting Failures in 747–8 Aircraft Hydraulic Pump Systems

Civil aviation, be it for passengers or cargo, is a highly competitive market, airlines are therefore strongly driven to increase earnings and reduce costs. The maintenance of the aircraft fleet is one pivotal aspect of this. In the industry two types of events occur, scheduled and unscheduled maintenance. While normal scheduled maintenance is already expensive, unscheduled maintenance events are even more so. The potential for savings is paramount when unscheduled events can be reduced to a minimum. Additionally, the safety of the customers is a huge concern, which is why possible failures ought to be detected as soon as possible. Over the last years, the large amounts of data that became available over the last decade open then door to a new range of applications. It got possible to learn from the past to predict future events, detect abnormal changes or behaviors, based on newly generated data. In this work we describe the application of anomaly detection on aircraft data. The goal is to predict upcoming failures of the turbine's hydraulic pumps, having severe financial implications should they be replaced in a context of unscheduled maintenance. In this context, we describe how we addressed this challenging task, and how crucial expert knowledge is when approaching such difficult undertakings. With our dataset we studied multiple outlier detection methods, ST-DBSCAN has proven to be the best suited method for this use case. We show how we identified the correct data frames to apply the methodology, and evaluate its prediction performance on a real-world dataset from several aircrafts.