Airbus provides continuous in-service support to airlines in order for them to enable the safe and efficient operation of the Airbus fleet at all times. This support, requested whenever an aircraft is operated on the limit or outside of its operation envelope, is very time and resource-consuming, causing costs for the airline and operations disruption. To achieve a Zero Aircraft on Ground (ZeroAoG) objective, among others, Airbus has created a big-data platform called Skywise that still remains to be utilized for Loads in-service support. This project demonstrates how the platform can be used to serve this objective.
The vertical tail-plane (VTP) in-service support process that is currently done using Microsoft Excel was replicated on the Skywise Slate application using SQL, HTML and JavaScript. Additional features were implemented and an end-user friendly interface was created to improve user experience, making in-service support faster, more convenient and more intuitive using Skywise.
In order to explore further capabilities of the platform and to improve this process, a \textit{RandomForestRegressor} model was implemented on Python in the Skywise Code Workbook application, predicting the loads on the VTP for conditions not available in the database of pre-computed cases. This allows the automatic processing of the aircraft's recordings for crosswind takeoff and landing events and the computation of the associated VTP loads. However, the platform proved to be not yet suited to host the process and the local implementation of the latter that was done as a comparison yielded better results on all aspects.
These results confirm the feasibility of automatic events analysis and the potential of Skywise as a spearhead in the ZeroAoG objective, but the platform is not yet suitable for such applications. Further development and the addition of new features could open the door to new in-service support operations, allowing airlines to limit maintenance costs and prevent operations disruption.